diff --git a/.claude/skills/paper-fetch/SKILL.md b/.claude/skills/paper-fetch/SKILL.md new file mode 100644 index 0000000000000000000000000000000000000000..8d0d6c99b6dc40961f91d6ba083b9315fb8260bf --- /dev/null +++ b/.claude/skills/paper-fetch/SKILL.md @@ -0,0 +1,1010 @@ +--- +name: paper-fetch +description: Fetch research papers from arXiv, ACL Anthology, or Semantic Scholar. Extracts to both Markdown (for LLM/RAG) and LaTeX (for compilation) formats. +argument-hint: "" +--- + +# Paper Fetcher + +Fetch research papers and store them in the `references/` folder with extracted text content. + +## Target + +**Arguments**: $ARGUMENTS + +The argument can be: +- **arXiv ID**: `2301.10140`, `arxiv:2301.10140`, or full URL +- **ACL Anthology ID**: `P19-1017`, `2023.acl-long.1`, or full URL +- **Semantic Scholar ID**: `s2:649def34...` or search query +- **DOI**: `10.18653/v1/P19-1017` + +## Output Structure + +Each paper gets its own folder named `year..main-author`: + +``` +references/ + 2020.emnlp.nguyen/ # PhoBERT paper (has arXiv source) + paper.tex # Original LaTeX source from arXiv + paper.md # Generated from LaTeX (with YAML front matter) + paper.pdf # PDF for reference + source/ # Full arXiv source files + 2014.eacl.nguyen/ # RDRPOSTagger (no arXiv) + paper.tex # Generated from PDF + paper.md # Extracted from PDF (with YAML front matter) + paper.pdf +``` + +### paper.md Format + +Metadata stored in YAML front matter: + +```markdown +--- +title: "PhoBERT: Pre-trained language models for Vietnamese" +authors: + - "Dat Quoc Nguyen" + - "Anh Tuan Nguyen" +year: 2020 +venue: "EMNLP Findings 2020" +url: "https://aclanthology.org/2020.findings-emnlp.92/" +arxiv: "2003.00744" +--- + +# Introduction +... +``` + +### Priority Order (arXiv papers) + +1. **Download LaTeX source** from `arxiv.org/e-print/{id}` (tar.gz) +2. **Generate paper.md** from LaTeX (higher quality than PDF extraction) +3. **Download PDF** for reference + +### Fallback (non-arXiv papers) + +1. Download PDF +2. Extract paper.md from PDF (pymupdf4llm) +3. Generate paper.tex from Markdown + +### Folder Naming Convention + +Format: `{year}.{venue}.{first_author_lastname}` + +| Paper ID | Folder Name | +|----------|-------------| +| `2020.findings-emnlp.92` | `2020.emnlp.nguyen` | +| `N18-5012` | `2018.naacl.vu` | +| `E14-2005` | `2014.eacl.nguyen` | +| `2301.10140` | `2023.arxiv.smith` | + +### Format Comparison + +| File | Source | Best For | +|------|--------|----------| +| `paper.tex` | arXiv e-print (original) or generated | Recompilation, precise formulas | +| `paper.md` | Converted from LaTeX or PDF | LLM/RAG, quick reading, GitHub | +| `source/` | Full arXiv source (if available) | Build, figures, bibliography | + +--- + +## PDF to Markdown Extraction Methods + +### Comparison of Methods + +| Method | Table Quality | Speed | Dependencies | Best For | +|--------|---------------|-------|--------------|----------| +| **pymupdf4llm** | ★★★★☆ | Fast | pymupdf4llm | General papers, good tables | +| **pdfplumber** | ★★★★★ | Medium | pdfplumber | Complex tables, accuracy | +| **Marker** | ★★★★★ | Slow | marker-pdf, torch | Best quality, LaTeX formulas | +| **MinerU** | ★★★★★ | Slow | magic-pdf, torch | Academic papers, LaTeX output | +| **Nougat** | ★★★★★ | Slow | nougat-ocr, torch | arXiv papers, full LaTeX | +| PyMuPDF (basic) | ★★☆☆☆ | Fast | pymupdf | Simple text only | +| pdftotext | ★★☆☆☆ | Fast | poppler-utils | Basic extraction | + +--- + +### Method 1: pymupdf4llm (Recommended) + +Best balance of quality and speed. Produces GitHub-compatible markdown with proper table formatting. + +```bash +uv run --with pymupdf4llm python -c " +import pymupdf4llm +import pathlib + +pdf_path = 'references/{paper_id}/paper.pdf' +md_path = 'references/{paper_id}/paper.md' + +# Extract with table support +md_text = pymupdf4llm.to_markdown(pdf_path) + +pathlib.Path(md_path).write_text(md_text, encoding='utf-8') +print(f'Extracted to: {md_path}') +" +``` + +**Features:** +- Automatic table detection and markdown formatting +- Preserves document structure (headers, lists) +- Fast processing +- GitHub-compatible markdown output + +**Advanced options:** +```python +import pymupdf4llm + +# With page chunks and table info +md_text = pymupdf4llm.to_markdown( + "paper.pdf", + page_chunks=True, # Get per-page chunks + write_images=True, # Extract images + image_path="images/", # Image output folder +) +``` + +--- + +### Method 2: pdfplumber (Best for Complex Tables) + +Most accurate for papers with complex table structures. + +```bash +uv run --with pdfplumber --with pandas python -c " +import pdfplumber +import pandas as pd + +pdf_path = 'references/{paper_id}/paper.pdf' +md_path = 'references/{paper_id}/paper.md' + +output = [] +with pdfplumber.open(pdf_path) as pdf: + for i, page in enumerate(pdf.pages): + output.append(f'\n') + + # Extract text + text = page.extract_text() or '' + + # Extract tables + tables = page.extract_tables() + + if tables: + for j, table in enumerate(tables): + # Convert to markdown table + if table and len(table) > 0: + df = pd.DataFrame(table[1:], columns=table[0]) + md_table = df.to_markdown(index=False) + text += f'\n\n**Table {j+1}:**\n{md_table}\n' + + output.append(text) + output.append('\n\n---\n\n') + +with open(md_path, 'w', encoding='utf-8') as f: + f.write(''.join(output)) + +print(f'Extracted with tables to: {md_path}') +" +``` + +**Features:** +- Excellent table detection +- Handles complex multi-row/column tables +- Detailed control over extraction +- Can extract individual table cells + +--- + +### Method 3: Marker (Best Quality - Deep Learning) + +Uses deep learning for highest quality extraction. Best for papers with LaTeX, complex layouts. + +```bash +# Install marker +uv pip install marker-pdf + +# Convert PDF to markdown +marker_single "references/{paper_id}/paper.pdf" "references/{paper_id}/" --output_format markdown +``` + +Or via Python: + +```bash +uv run --with marker-pdf python -c " +from marker.converters.pdf import PdfConverter +from marker.models import create_model_dict +from marker.output import text_from_rendered + +# Load models (first run downloads ~2GB) +models = create_model_dict() +converter = PdfConverter(artifact_dict=models) + +# Convert +rendered = converter('references/{paper_id}/paper.pdf') +text, _, images = text_from_rendered(rendered) + +with open('references/{paper_id}/paper.md', 'w') as f: + f.write(text) +" +``` + +**Features:** +- AI-based layout detection +- Excellent table recognition +- LaTeX formula extraction +- Best for academic papers +- Handles multi-column layouts + +**Note:** Requires GPU for best performance, downloads ~2GB models on first run. + +--- + +### Method 4: Nougat (PDF to LaTeX - for arXiv papers) + +Best for converting academic papers to full LaTeX source. + +```bash +# Install nougat +uv pip install nougat-ocr + +# Convert PDF to LaTeX/Markdown with math +nougat "references/{paper_id}/paper.pdf" -o "references/{paper_id}/" -m 0.1.0-base +``` + +Or via Python: + +```bash +uv run --with nougat-ocr python -c " +from nougat import NougatModel +from nougat.utils.dataset import LaTeXDataset + +model = NougatModel.from_pretrained('facebook/nougat-base') +model.eval() + +# Process PDF +latex_output = model.predict('references/{paper_id}/paper.pdf') + +with open('references/{paper_id}/paper.tex', 'w') as f: + f.write(latex_output) +" +``` + +**Features:** +- Full LaTeX output with equations +- Trained on arXiv papers +- Best for math-heavy documents +- Outputs compilable LaTeX + +--- + +### Method 5: MinerU (Best for Academic Papers) + +Comprehensive tool for high-quality extraction with LaTeX formula support. + +```bash +# Install MinerU +pip install magic-pdf[full] + +# Convert PDF +magic-pdf -p "references/{paper_id}/paper.pdf" -o "references/{paper_id}/" +``` + +**Features:** +- High formula recognition rate +- LaTeX-friendly output +- Table structure preservation +- Multi-format output (MD, JSON, LaTeX) + +--- + +### Method 6: Hybrid Approach (Recommended for Academic Papers) + +Combine methods for best results: + +```python +# /// script +# requires-python = ">=3.9" +# dependencies = ["pymupdf4llm>=0.0.10", "pdfplumber>=0.10.0", "pandas>=2.0.0"] +# /// +""" +Hybrid PDF extraction: pymupdf4llm for text, pdfplumber for tables. +""" +import pymupdf4llm +import pdfplumber +import pandas as pd +import re +import sys +import os + +def extract_tables_pdfplumber(pdf_path: str) -> dict: + """Extract tables using pdfplumber (more accurate).""" + tables_by_page = {} + with pdfplumber.open(pdf_path) as pdf: + for i, page in enumerate(pdf.pages): + tables = page.extract_tables() + if tables: + page_tables = [] + for table in tables: + if table and len(table) > 1: + try: + # Clean table data + cleaned = [[str(cell).strip() if cell else '' for cell in row] for row in table] + df = pd.DataFrame(cleaned[1:], columns=cleaned[0]) + md_table = df.to_markdown(index=False) + page_tables.append(md_table) + except Exception as e: + print(f"Table error on page {i+1}: {e}") + if page_tables: + tables_by_page[i] = page_tables + return tables_by_page + +def extract_hybrid(pdf_path: str, output_path: str): + """Hybrid extraction: pymupdf4llm + pdfplumber tables.""" + + # Get base markdown from pymupdf4llm + md_text = pymupdf4llm.to_markdown(pdf_path) + + # Get accurate tables from pdfplumber + tables = extract_tables_pdfplumber(pdf_path) + + # If pdfplumber found tables, we can append or replace + if tables: + md_text += "\n\n---\n\n## Extracted Tables (pdfplumber)\n\n" + for page_num, page_tables in sorted(tables.items()): + md_text += f"### Page {page_num + 1}\n\n" + for i, table in enumerate(page_tables): + md_text += f"**Table {i+1}:**\n\n{table}\n\n" + + with open(output_path, 'w', encoding='utf-8') as f: + f.write(md_text) + + print(f"Extracted to: {output_path}") + print(f"Found {sum(len(t) for t in tables.values())} tables") + +if __name__ == "__main__": + pdf_path = sys.argv[1] + output_path = sys.argv[2] if len(sys.argv) > 2 else pdf_path.replace('.pdf', '.md') + extract_hybrid(pdf_path, output_path) +``` + +--- + +## Complete Fetch Script (Both Formats) + +```python +# /// script +# requires-python = ">=3.9" +# dependencies = ["arxiv>=2.0.0", "requests>=2.28.0", "pymupdf4llm>=0.0.10"] +# /// +""" +Fetch paper and extract to both Markdown and LaTeX formats. +Folder naming: year..main-author +""" +import arxiv +import pymupdf4llm +import requests +import json +import sys +import os +import re +import unicodedata + +def normalize_name(name: str) -> str: + """Normalize author name to lowercase ASCII.""" + # Get last name (last word) + parts = name.strip().split() + lastname = parts[-1] if parts else name + + # Remove accents and convert to lowercase + normalized = unicodedata.normalize('NFD', lastname) + ascii_name = ''.join(c for c in normalized if unicodedata.category(c) != 'Mn') + return ascii_name.lower() + +def get_folder_name(year: int, venue: str, first_author: str) -> str: + """Generate folder name: year.venue.author""" + # Normalize venue name + venue = venue.lower() + venue = re.sub(r'findings-', '', venue) # findings-emnlp -> emnlp + venue = re.sub(r'-demos?', '', venue) # naacl-demos -> naacl + venue = re.sub(r'-main', '', venue) # acl-main -> acl + venue = re.sub(r'-long', '', venue) # acl-long -> acl + venue = re.sub(r'-short', '', venue) + venue = re.sub(r'[^a-z0-9]', '', venue) # Remove special chars + + # Normalize author + author = normalize_name(first_author) + + return f"{year}.{venue}.{author}" + +def convert_md_to_latex(md_text: str) -> str: + """Convert Markdown to basic LaTeX document.""" + # LaTeX document header + latex = r"""\documentclass[11pt]{article} +\usepackage[utf8]{inputenc} +\usepackage{amsmath,amssymb} +\usepackage{booktabs} +\usepackage{hyperref} +\usepackage{graphicx} + +\begin{document} + +""" + # Convert Markdown to LaTeX + content = md_text + + # Headers + content = re.sub(r'^# (.+)$', r'\\section*{\1}', content, flags=re.MULTILINE) + content = re.sub(r'^## (.+)$', r'\\subsection*{\1}', content, flags=re.MULTILINE) + content = re.sub(r'^### (.+)$', r'\\subsubsection*{\1}', content, flags=re.MULTILINE) + + # Bold and italic + content = re.sub(r'\*\*(.+?)\*\*', r'\\textbf{\1}', content) + content = re.sub(r'\*(.+?)\*', r'\\textit{\1}', content) + content = re.sub(r'_(.+?)_', r'\\textit{\1}', content) + + # Bullet points + content = re.sub(r'^- (.+)$', r'\\item \1', content, flags=re.MULTILINE) + + # Code blocks + content = re.sub(r'```\w*\n(.*?)\n```', r'\\begin{verbatim}\n\1\n\\end{verbatim}', content, flags=re.DOTALL) + + # Inline code + content = re.sub(r'`([^`]+)`', r'\\texttt{\1}', content) + + # Convert markdown tables to LaTeX (basic) + def convert_table(match): + lines = match.group(0).strip().split('\n') + if len(lines) < 2: + return match.group(0) + + # Get header and determine columns + header = lines[0] + cols = header.count('|') - 1 + if cols <= 0: + return match.group(0) + + latex_table = "\\begin{table}[h]\n\\centering\n" + latex_table += "\\begin{tabular}{" + "l" * cols + "}\n\\toprule\n" + + for i, line in enumerate(lines): + if '---' in line: + continue + cells = [c.strip() for c in line.split('|')[1:-1]] + latex_table += " & ".join(cells) + " \\\\\n" + if i == 0: + latex_table += "\\midrule\n" + + latex_table += "\\bottomrule\n\\end{tabular}\n\\end{table}\n" + return latex_table + + content = re.sub(r'\|.+\|[\s\S]*?\|.+\|', convert_table, content) + + latex += content + latex += "\n\\end{document}\n" + + return latex + +def convert_latex_to_md(tex_content: str) -> str: + """Convert LaTeX to Markdown for LLM/RAG use.""" + md = tex_content + + # Remove LaTeX preamble (everything before \begin{document}) + doc_match = re.search(r'\\begin\{document\}', md) + if doc_match: + md = md[doc_match.end():] + md = re.sub(r'\\end\{document\}.*', '', md, flags=re.DOTALL) + + # Remove comments + md = re.sub(r'%.*$', '', md, flags=re.MULTILINE) + + # Convert sections + md = re.sub(r'\\section\*?\{([^}]+)\}', r'# \1', md) + md = re.sub(r'\\subsection\*?\{([^}]+)\}', r'## \1', md) + md = re.sub(r'\\subsubsection\*?\{([^}]+)\}', r'### \1', md) + md = re.sub(r'\\paragraph\*?\{([^}]+)\}', r'#### \1', md) + + # Convert formatting + md = re.sub(r'\\textbf\{([^}]+)\}', r'**\1**', md) + md = re.sub(r'\\textit\{([^}]+)\}', r'*\1*', md) + md = re.sub(r'\\emph\{([^}]+)\}', r'*\1*', md) + md = re.sub(r'\\texttt\{([^}]+)\}', r'`\1`', md) + md = re.sub(r'\\underline\{([^}]+)\}', r'\1', md) + + # Convert citations and references + md = re.sub(r'\\cite\{([^}]+)\}', r'[\1]', md) + md = re.sub(r'\\citep?\{([^}]+)\}', r'[\1]', md) + md = re.sub(r'\\citet?\{([^}]+)\}', r'[\1]', md) + md = re.sub(r'\\ref\{([^}]+)\}', r'[\1]', md) + md = re.sub(r'\\label\{[^}]+\}', '', md) + + # Convert URLs + md = re.sub(r'\\url\{([^}]+)\}', r'\1', md) + md = re.sub(r'\\href\{([^}]+)\}\{([^}]+)\}', r'[\2](\1)', md) + + # Convert lists + md = re.sub(r'\\begin\{itemize\}', '', md) + md = re.sub(r'\\end\{itemize\}', '', md) + md = re.sub(r'\\begin\{enumerate\}', '', md) + md = re.sub(r'\\end\{enumerate\}', '', md) + md = re.sub(r'\\item\s*', '- ', md) + + # Convert math (keep as-is for LaTeX rendering) + md = re.sub(r'\$\$([^$]+)\$\$', r'\n$$\1$$\n', md) + md = re.sub(r'\\begin\{equation\*?\}(.*?)\\end\{equation\*?\}', r'\n$$\1$$\n', md, flags=re.DOTALL) + md = re.sub(r'\\begin\{align\*?\}(.*?)\\end\{align\*?\}', r'\n$$\1$$\n', md, flags=re.DOTALL) + + # Convert tables (basic - keep structure) + def convert_table(match): + table_content = match.group(1) + rows = re.split(r'\\\\', table_content) + md_rows = [] + for i, row in enumerate(rows): + cells = [c.strip() for c in re.split(r'&', row) if c.strip()] + if cells: + md_rows.append('| ' + ' | '.join(cells) + ' |') + if i == 0: + md_rows.append('|' + '---|' * len(cells)) + return '\n'.join(md_rows) + + md = re.sub(r'\\begin\{tabular\}\{[^}]*\}(.*?)\\end\{tabular\}', convert_table, md, flags=re.DOTALL) + + # Remove common LaTeX commands + md = re.sub(r'\\(small|large|Large|footnotesize|normalsize|tiny|huge)\b', '', md) + md = re.sub(r'\\(hline|toprule|midrule|bottomrule|cline\{[^}]*\})', '', md) + md = re.sub(r'\\(vspace|hspace|vskip|hskip)\{[^}]*\}', '', md) + md = re.sub(r'\\(centering|raggedright|raggedleft)\b', '', md) + md = re.sub(r'\\(newline|linebreak|pagebreak|newpage)\b', '\n', md) + md = re.sub(r'\\\\', '\n', md) + md = re.sub(r'\\[a-zA-Z]+\{[^}]*\}', '', md) # Remove remaining commands with args + md = re.sub(r'\\[a-zA-Z]+\b', '', md) # Remove remaining commands without args + + # Clean up + md = re.sub(r'\n{3,}', '\n\n', md) + md = re.sub(r'^\s+', '', md, flags=re.MULTILINE) + + return md.strip() + +def download_arxiv_source(arxiv_id: str, folder: str) -> str: + """Download LaTeX source from arXiv e-print. Returns tex content or None.""" + import tarfile + import gzip + from io import BytesIO + + source_url = f"https://arxiv.org/e-print/{arxiv_id}" + try: + response = requests.get(source_url, allow_redirects=True) + response.raise_for_status() + + content = response.content + tex_content = None + + # Try to extract as tar.gz + try: + with tarfile.open(fileobj=BytesIO(content), mode='r:gz') as tar: + tex_files = [m.name for m in tar.getmembers() if m.name.endswith('.tex')] + + # Extract all files for reference + os.makedirs(f"{folder}/source", exist_ok=True) + tar.extractall(path=f"{folder}/source") + print(f"Extracted {len(tar.getmembers())} source files to {folder}/source/") + + # Find main tex file + main_tex = None + for name in tex_files: + if 'main' in name.lower(): + main_tex = name + break + if not main_tex and tex_files: + main_tex = tex_files[0] + + if main_tex: + with open(f"{folder}/source/{main_tex}", 'r', encoding='utf-8', errors='ignore') as f: + tex_content = f.read() + print(f"Main LaTeX: {main_tex}") + + if tex_content: + return tex_content + except tarfile.TarError: + pass + + # Try as plain gzipped tex file + try: + tex_content = gzip.decompress(content).decode('utf-8', errors='ignore') + if '\\documentclass' in tex_content or '\\begin{document}' in tex_content: + print(f"Extracted LaTeX source (gzip)") + return tex_content + except: + pass + + # Try as plain tex file + try: + tex_content = content.decode('utf-8', errors='ignore') + if '\\documentclass' in tex_content or '\\begin{document}' in tex_content: + print(f"Extracted LaTeX source (plain)") + return tex_content + except: + pass + + print(f"Could not extract LaTeX source from arXiv") + return None + + except Exception as e: + print(f"Failed to download arXiv source: {e}") + return None + +def build_front_matter(title: str, authors: list, year: int, venue: str, url: str, arxiv_id: str = None) -> str: + """Build YAML front matter for paper.md""" + authors_yaml = '\n'.join(f' - "{a}"' for a in authors) + fm = f'''--- +title: "{title}" +authors: +{authors_yaml} +year: {year} +venue: "{venue}" +url: "{url}"''' + if arxiv_id: + fm += f'\narxiv: "{arxiv_id}"' + fm += '\n---\n\n' + return fm + +def fetch_arxiv(arxiv_id: str): + """Fetch paper from arXiv. Priority: LaTeX source -> generate MD from it.""" + arxiv_id = re.sub(r'^(arxiv:|https?://arxiv\.org/(abs|pdf)/)', '', arxiv_id) + arxiv_id = arxiv_id.rstrip('.pdf').rstrip('/') + + # Get paper metadata first + client = arxiv.Client() + paper = next(client.results(arxiv.Search(id_list=[arxiv_id]))) + + # Generate folder name: year.arxiv.author + year = paper.published.year + first_author = paper.authors[0].name if paper.authors else "unknown" + folder_name = get_folder_name(year, "arxiv", first_author) + folder = f"references/{folder_name}" + os.makedirs(folder, exist_ok=True) + print(f"Folder: {folder}") + + # Build front matter + front_matter = build_front_matter( + title=paper.title, + authors=[a.name for a in paper.authors], + year=year, + venue="arXiv", + url=paper.entry_id, + arxiv_id=arxiv_id + ) + + # 1. Download LaTeX source first (priority) + tex_content = download_arxiv_source(arxiv_id, folder) + + if tex_content: + # Save paper.tex + with open(f"{folder}/paper.tex", 'w', encoding='utf-8') as f: + f.write(tex_content) + print(f"Saved: paper.tex (original arXiv source)") + + # Generate paper.md from LaTeX with front matter + md_text = convert_latex_to_md(tex_content) + with open(f"{folder}/paper.md", 'w', encoding='utf-8') as f: + f.write(front_matter + md_text) + print(f"Generated: paper.md (from LaTeX)") + has_source = True + else: + has_source = False + + # 2. Download PDF (always, for reference) + pdf_path = f"{folder}/paper.pdf" + paper.download_pdf(filename=pdf_path) + print(f"Downloaded: paper.pdf") + + # 3. If no LaTeX source, extract from PDF + if not has_source: + md_text = pymupdf4llm.to_markdown(pdf_path) + with open(f"{folder}/paper.md", 'w', encoding='utf-8') as f: + f.write(front_matter + md_text) + print(f"Extracted: paper.md (from PDF)") + + tex_content = convert_md_to_latex(md_text) + with open(f"{folder}/paper.tex", 'w', encoding='utf-8') as f: + f.write(tex_content) + print(f"Generated: paper.tex (from PDF)") + + return folder + +def parse_acl_id(paper_id: str) -> tuple: + """Parse ACL paper ID to extract year and venue.""" + # New format: 2020.findings-emnlp.92, 2021.naacl-demos.1 + new_match = re.match(r'^(\d{4})\.([a-z\-]+)\.(\d+)$', paper_id) + if new_match: + year = int(new_match.group(1)) + venue = new_match.group(2) + return year, venue + + # Old format: E14-2005, N18-5012, P19-1017 + old_match = re.match(r'^([A-Z])(\d{2})-\d+$', paper_id) + if old_match: + prefix = old_match.group(1) + year_short = int(old_match.group(2)) + year = 2000 + year_short if year_short < 50 else 1900 + year_short + + venue_map = { + 'P': 'acl', 'N': 'naacl', 'E': 'eacl', 'D': 'emnlp', + 'C': 'coling', 'W': 'workshop', 'S': 'semeval', 'Q': 'tacl' + } + venue = venue_map.get(prefix, 'acl') + return year, venue + + return None, paper_id + +def fetch_acl(paper_id: str): + """Fetch paper from ACL Anthology.""" + paper_id = re.sub(r'^https?://aclanthology\.org/', '', paper_id) + paper_id = paper_id.rstrip('/').rstrip('.pdf') + + # Get metadata from ACL Anthology BibTeX + bib_url = f"https://aclanthology.org/{paper_id}.bib" + title = "" + authors = [] + booktitle = "" + try: + bib_response = requests.get(bib_url) + bib_text = bib_response.text + + # Extract title + title_match = re.search(r'title\s*=\s*["{]([^"}]+)', bib_text) + title = title_match.group(1) if title_match else "" + + # Extract all authors + author_match = re.search(r'author\s*=\s*["{]([^"}]+)', bib_text) + if author_match: + authors_str = author_match.group(1) + authors = [a.strip() for a in authors_str.split(' and ')] + + # Extract booktitle/venue + booktitle_match = re.search(r'booktitle\s*=\s*["{]([^"}]+)', bib_text) + booktitle = booktitle_match.group(1) if booktitle_match else "" + + # Extract year + year_match = re.search(r'year\s*=\s*["{]?(\d{4})', bib_text) + year = int(year_match.group(1)) if year_match else None + except: + authors = ["unknown"] + year = None + + first_author = authors[0] if authors else "unknown" + + # Parse venue from paper_id + parsed_year, venue = parse_acl_id(paper_id) + if year is None: + year = parsed_year or 2020 + + # Generate folder name + folder_name = get_folder_name(year, venue, first_author) + folder = f"references/{folder_name}" + os.makedirs(folder, exist_ok=True) + print(f"Folder: {folder}") + + # Build front matter + front_matter = build_front_matter( + title=title, + authors=authors, + year=year, + venue=booktitle or venue.upper(), + url=f"https://aclanthology.org/{paper_id}" + ) + + pdf_url = f"https://aclanthology.org/{paper_id}.pdf" + pdf_path = f"{folder}/paper.pdf" + + response = requests.get(pdf_url, allow_redirects=True) + response.raise_for_status() + with open(pdf_path, 'wb') as f: + f.write(response.content) + print(f"Downloaded PDF: {pdf_path}") + + # Extract markdown from PDF + md_text = pymupdf4llm.to_markdown(pdf_path) + with open(f"{folder}/paper.md", 'w', encoding='utf-8') as f: + f.write(front_matter + md_text) + print(f"Extracted: paper.md") + + # Generate LaTeX from markdown + tex_content = convert_md_to_latex(md_text) + with open(f"{folder}/paper.tex", 'w', encoding='utf-8') as f: + f.write(tex_content) + print(f"Generated: paper.tex") + + return folder + +def fetch_semantic_scholar(query: str): + """Fetch paper via Semantic Scholar.""" + if re.match(r'^[0-9a-f]{40}$', query.replace('s2:', '')): + paper_id = query.replace('s2:', '') + else: + url = "https://api.semanticscholar.org/graph/v1/paper/search" + params = {"query": query, "limit": 1, "fields": "paperId,title"} + response = requests.get(url, params=params) + data = response.json() + if not data.get('data'): + print(f"No papers found for: {query}") + return None + paper_id = data['data'][0]['paperId'] + print(f"Found: {data['data'][0]['title']}") + + url = f"https://api.semanticscholar.org/graph/v1/paper/{paper_id}" + params = {"fields": "title,authors,abstract,year,openAccessPdf,externalIds,url,venue"} + response = requests.get(url, params=params) + response.raise_for_status() + data = response.json() + + # Get year, venue, first author for folder name + year = data.get('year') or 2020 + venue = data.get('venue') or 'unknown' + # Normalize venue - handle common patterns + if not venue or venue == 'unknown': + if 'ArXiv' in data.get('externalIds', {}): + venue = 'arxiv' + elif 'ACL' in data.get('externalIds', {}): + venue = 'acl' + else: + venue = 'paper' + + authors = data.get('authors', []) + author_names = [a['name'] for a in authors] + first_author = author_names[0] if author_names else 'unknown' + + # Generate folder name: year.venue.author + folder_name = get_folder_name(year, venue, first_author) + folder = f"references/{folder_name}" + os.makedirs(folder, exist_ok=True) + print(f"Folder: {folder}") + + # Build front matter + front_matter = build_front_matter( + title=data.get('title', ''), + authors=author_names, + year=year, + venue=venue, + url=data.get('url', '') + ) + + pdf_info = data.get('openAccessPdf') + if pdf_info and pdf_info.get('url'): + pdf_url = pdf_info['url'] + pdf_path = f"{folder}/paper.pdf" + + pdf_response = requests.get(pdf_url, allow_redirects=True) + pdf_response.raise_for_status() + with open(pdf_path, 'wb') as f: + f.write(pdf_response.content) + print(f"Downloaded PDF: {pdf_path}") + + # Extract markdown from PDF with front matter + md_text = pymupdf4llm.to_markdown(pdf_path) + with open(f"{folder}/paper.md", 'w', encoding='utf-8') as f: + f.write(front_matter + md_text) + print(f"Extracted: paper.md") + + # Generate LaTeX + tex_content = convert_md_to_latex(md_text) + with open(f"{folder}/paper.tex", 'w', encoding='utf-8') as f: + f.write(tex_content) + print(f"Generated: paper.tex") + else: + print("No open access PDF available") + if 'ArXiv' in data.get('externalIds', {}): + return fetch_arxiv(data['externalIds']['ArXiv']) + + return folder + +if __name__ == "__main__": + if len(sys.argv) < 2: + print("Usage: uv run fetch_paper.py ") + sys.exit(1) + + query = ' '.join(sys.argv[1:]) + + if re.match(r'^\d{4}\.\d{4,5}', query) or 'arxiv.org' in query or query.startswith('arxiv:'): + fetch_arxiv(query) + elif re.match(r'^[A-Z]\d{2}-\d{4}$', query) or re.match(r'^\d{4}\.[a-z]+-', query) or 'aclanthology.org' in query: + fetch_acl(query) + else: + fetch_semantic_scholar(query) +``` + +--- + +## Quick Commands + +**Fetch and extract to both formats (MD + LaTeX):** +```bash +mkdir -p references/E14-2005 && \ +curl -L "https://aclanthology.org/E14-2005.pdf" -o references/E14-2005/paper.pdf && \ +uv run --with pymupdf4llm python << 'EOF' +import pymupdf4llm +import re + +folder = 'references/E14-2005' +pdf_path = f'{folder}/paper.pdf' + +# Extract to Markdown +md = pymupdf4llm.to_markdown(pdf_path) +open(f'{folder}/paper.md', 'w').write(md) +print(f'Created: {folder}/paper.md') + +# Convert to basic LaTeX +tex = f"""\\documentclass{{article}} +\\usepackage[utf8]{{inputenc}} +\\usepackage{{amsmath,booktabs}} +\\begin{{document}} +{md} +\\end{{document}} +""" +open(f'{folder}/paper.tex', 'w').write(tex) +print(f'Created: {folder}/paper.tex') +EOF +``` + +**Using pdfplumber for complex tables:** +```bash +uv run --with pdfplumber --with pandas python -c " +import pdfplumber +import pandas as pd + +with pdfplumber.open('references/E14-2005/paper.pdf') as pdf: + for page in pdf.pages: + for table in page.extract_tables(): + if table: + print(pd.DataFrame(table[1:], columns=table[0]).to_markdown()) +" +``` + +--- + +## Troubleshooting Table Extraction + +### Problem: Tables not detected + +**Solution 1:** Try different extraction strategy with pymupdf4llm: +```python +md = pymupdf4llm.to_markdown("paper.pdf", table_strategy="lines") # or "text" +``` + +**Solution 2:** Use pdfplumber with custom settings: +```python +import pdfplumber +with pdfplumber.open("paper.pdf") as pdf: + page = pdf.pages[0] + tables = page.extract_tables(table_settings={ + "vertical_strategy": "text", + "horizontal_strategy": "text", + }) +``` + +### Problem: Table columns misaligned + +**Solution:** Use pdfplumber's debug mode to visualize: +```python +import pdfplumber +with pdfplumber.open("paper.pdf") as pdf: + page = pdf.pages[3] # Page with table + im = page.to_image() + im.debug_tablefinder() + im.save("debug_table.png") +``` + +### Problem: Multi-column papers + +**Solution:** Use Marker for best results: +```bash +marker_single paper.pdf output/ --output_format markdown +``` + +--- + +## Sources + +- [pymupdf4llm](https://pymupdf.readthedocs.io/en/latest/pymupdf4llm/) - Best for markdown with tables +- [pdfplumber](https://github.com/jsvine/pdfplumber) - Most accurate table extraction +- [Marker](https://github.com/datalab-to/marker) - Deep learning PDF to markdown +- [Camelot](https://camelot-py.readthedocs.io/) - Specialized table extraction +- [PDF Parsing Comparison Study](https://arxiv.org/html/2410.09871v1) - Academic comparison diff --git a/.claude/skills/paper-research/SKILL.md b/.claude/skills/paper-research/SKILL.md new file mode 100644 index 0000000000000000000000000000000000000000..1676e0a9d79ace4ebc0b9df0ad4d4b0ae83f6a68 --- /dev/null +++ b/.claude/skills/paper-research/SKILL.md @@ -0,0 +1,502 @@ +--- +name: paper-research +description: Research a topic systematically following ACL/NeurIPS/ICML best practices. Finds papers, builds citation networks, and synthesizes findings. +argument-hint: "" +dependencies: + - paper-fetch +--- + +# Systematic Research Skill + +Research a topic following best practices from ACL, NeurIPS, ICML, and systematic literature review (SLR) methodology. + +**Integrates with**: `/paper-fetch` - automatically fetch and store important papers for full-text analysis. + +## Target + +**Research Topic**: $ARGUMENTS + +If no topic specified, analyze the current project to identify relevant research topics. + +--- + +## Research Methodology + +Follow the systematic approach based on PRISMA guidelines and AI conference best practices. + +### Phase 1: Define Research Scope + +#### 1.1 Formulate Research Questions + +Use the PICO/PICo framework adapted for CS/NLP: + +| Component | Description | Example | +|-----------|-------------|---------| +| **P**opulation | Task/Domain | Vietnamese POS tagging | +| **I**ntervention | Method/Approach | CRF, Transformers, BERT | +| **C**omparison | Baselines | Rule-based, HMM, BiLSTM | +| **O**utcome | Metrics | Accuracy, F1, inference speed | + +**Template research questions:** +- RQ1: What is the current state-of-the-art for [task]? +- RQ2: What methods have been applied to [task]? +- RQ3: What are the main challenges and open problems? +- RQ4: What datasets and benchmarks exist? + +#### 1.2 Define Search Terms + +Create a comprehensive keyword list: + +``` +Primary terms: [main task] (e.g., "POS tagging", "part-of-speech") +Method terms: [approaches] (e.g., "CRF", "neural", "transformer") +Domain terms: [language/domain] (e.g., "Vietnamese", "low-resource") +Synonyms: [alternatives] (e.g., "word tagging", "morphological analysis") +``` + +Build search queries using Boolean operators: +``` +("POS tagging" OR "part-of-speech") AND ("Vietnamese" OR "low-resource") AND ("CRF" OR "neural" OR "BERT") +``` + +--- + +### Phase 2: Search for Papers + +#### 2.1 Search Sources + +Search these sources in order of priority: + +| Source | Best For | URL | +|--------|----------|-----| +| **ACL Anthology** | NLP/CL papers | https://aclanthology.org | +| **Semantic Scholar** | AI/ML papers, citations | https://semanticscholar.org | +| **arXiv** | Preprints, latest work | https://arxiv.org | +| **Google Scholar** | Broad coverage | https://scholar.google.com | +| **DBLP** | CS bibliography | https://dblp.org | +| **Papers With Code** | SOTA benchmarks | https://paperswithcode.com | + +#### 2.2 Search Commands + +**ACL Anthology:** +```bash +# Search via web +curl "https://aclanthology.org/search/?q=vietnamese+pos+tagging" +``` + +**Semantic Scholar API:** +```bash +# Search papers +curl "https://api.semanticscholar.org/graph/v1/paper/search?query=vietnamese+POS+tagging&limit=20&fields=title,year,citationCount,authors,abstract,openAccessPdf" + +# Get paper details with citations +curl "https://api.semanticscholar.org/graph/v1/paper/{paper_id}?fields=title,abstract,citations,references" +``` + +**arXiv API:** +```bash +# Search arXiv +curl "http://export.arxiv.org/api/query?search_query=all:vietnamese+pos+tagging&max_results=20" +``` + +**Papers With Code:** +```bash +# Check SOTA +curl "https://paperswithcode.com/api/v1/search/?q=vietnamese+pos+tagging" +``` + +#### 2.3 Citation Network Exploration + +Use these strategies to find related work: + +1. **Backward search**: Check references of key papers +2. **Forward search**: Find papers that cite key papers +3. **Author search**: Find other papers by same authors +4. **Similar papers**: Use Semantic Scholar's recommendations + +```bash +# Get citations (papers that cite this paper) +curl "https://api.semanticscholar.org/graph/v1/paper/{paper_id}/citations?fields=title,year,citationCount&limit=50" + +# Get references (papers this paper cites) +curl "https://api.semanticscholar.org/graph/v1/paper/{paper_id}/references?fields=title,year,citationCount&limit=50" +``` + +#### 2.4 Discovery Tools + +Use these tools for visual exploration: + +| Tool | Purpose | URL | +|------|---------|-----| +| **Connected Papers** | Visual citation graph | https://connectedpapers.com | +| **Research Rabbit** | Paper recommendations | https://researchrabbit.ai | +| **Litmaps** | Citation mapping | https://litmaps.com | +| **Elicit** | AI paper search | https://elicit.com | +| **Inciteful** | Citation network | https://inciteful.xyz | + +--- + +### Phase 3: Screen and Select Papers + +#### 3.1 Inclusion/Exclusion Criteria + +Define clear criteria: + +**Include:** +- Published in peer-reviewed venue (ACL, EMNLP, NAACL, COLING, etc.) +- Relevant to research questions +- Published within timeframe (e.g., last 5-10 years) +- English language + +**Exclude:** +- Non-peer-reviewed (unless highly cited preprint) +- Tangentially related +- Superseded by newer work +- Duplicate/extended versions (keep most comprehensive) + +#### 3.2 Screening Process + +1. **Title/Abstract screening**: Quick relevance check +2. **Full-text screening**: Detailed relevance assessment +3. **Quality assessment**: Methodological rigor + +Track using PRISMA flow: +``` +Records identified: N +Duplicates removed: N +Records screened: N +Records excluded: N +Full-text assessed: N +Studies included: N +``` + +--- + +### Phase 3.5: Fetch Selected Papers + +After screening, use the **paper-fetch** skill to download important papers for full-text analysis. + +#### 3.5.1 Identify Papers to Fetch + +Prioritize fetching: +1. **Seminal papers**: Highly cited foundational work +2. **SOTA papers**: Current best-performing methods +3. **Directly relevant**: Papers closest to your research +4. **Methodology papers**: Detailed method descriptions needed + +#### 3.5.2 Fetch Papers Using paper-fetch Skill + +For each selected paper, invoke `/paper-fetch` with the paper ID: + +```bash +# arXiv papers +/paper-fetch 2301.10140 +/paper-fetch arxiv:1810.04805 # BERT paper + +# ACL Anthology papers +/paper-fetch P19-1017 +/paper-fetch 2023.acl-long.1 + +# Semantic Scholar (by title search) +/paper-fetch "BERT: Pre-training of Deep Bidirectional Transformers" +``` + +#### 3.5.3 Batch Fetching + +For multiple papers, fetch in sequence: + +```bash +# Create list of paper IDs to fetch +PAPERS=( + "1810.04805" # BERT + "2003.10555" # PhoBERT + "P19-1017" # Example ACL paper +) + +# Fetch each paper +for paper_id in "${PAPERS[@]}"; do + /paper-fetch $paper_id +done +``` + +#### 3.5.4 Output Structure + +Each fetched paper creates: + +``` +references/ + 1810.04805/ + paper.pdf # Original PDF + paper.md # Extracted text (for full-text search/analysis) + metadata.json # Title, authors, abstract + 2003.10555/ + paper.pdf + paper.md + metadata.json + research_{topic}/ # Research synthesis (Phase 6) + README.md + papers.md + ... +``` + +#### 3.5.5 Use Fetched Papers + +After fetching, you can: +- **Read full text**: Open `paper.md` for detailed analysis +- **Search across papers**: Grep through all `paper.md` files +- **Extract quotes**: Copy relevant sections with page references +- **Verify claims**: Check original source for accuracy + +```bash +# Search across all fetched papers +grep -r "CRF" references/*/paper.md + +# Find specific methodology details +grep -r "feature template" references/*/paper.md +``` + +--- + +### Phase 4: Extract and Organize Information + +#### 4.1 Create Paper Database + +For each paper, extract: + +```markdown +## Paper: [Title] + +- **Authors**: [Names] +- **Venue**: [Conference/Journal] [Year] +- **URL**: [Link] +- **Citations**: [Count] + +### Summary +[2-3 sentence summary] + +### Key Contributions +1. [Contribution 1] +2. [Contribution 2] + +### Methodology +- **Approach**: [Method name/type] +- **Dataset**: [Dataset used] +- **Metrics**: [Evaluation metrics] + +### Results +| Dataset | Metric | Score | +|---------|--------|-------| +| [Name] | [Acc] | [XX%] | + +### Strengths +- [Strength 1] + +### Limitations +- [Limitation 1] + +### Relevance to Our Work +[How this paper relates to current project] +``` + +#### 4.2 Comparison Table + +Create a summary table: + +```markdown +| Paper | Year | Method | Dataset | Accuracy | F1 | Key Innovation | +|-------|------|--------|---------|----------|-----|----------------| +| [1] | 2023 | BERT | UDD | 97.2% | 96.8| Fine-tuning | +| [2] | 2022 | CRF | VLSP | 95.5% | 94.1| Feature eng. | +``` + +--- + +### Phase 5: Synthesize Findings + +#### 5.1 Thematic Analysis + +Organize findings by themes (not chronologically): + +```markdown +## Related Work Synthesis + +### Traditional Approaches +- Rule-based methods: [Summary] +- Statistical methods (HMM, CRF): [Summary] + +### Neural Approaches +- RNN/LSTM-based: [Summary] +- Transformer-based: [Summary] + +### Vietnamese-Specific Work +- [Summary of Vietnamese NLP research] + +### Datasets and Benchmarks +- [Available resources] + +### Open Challenges +- [Remaining problems] +``` + +#### 5.2 Gap Analysis + +Identify what's missing: + +```markdown +## Research Gaps + +1. **Methodological gaps**: [What methods haven't been tried?] +2. **Data gaps**: [What data is missing?] +3. **Evaluation gaps**: [What isn't being measured?] +4. **Domain gaps**: [What domains lack coverage?] +``` + +#### 5.3 SOTA Summary + +```markdown +## State-of-the-Art + +### Current Best Results +| Task | Dataset | SOTA Model | Score | Paper | +|------|---------|------------|-------|-------| +| POS | UDD | PhoBERT | 97.2% | [Ref] | + +### Trends +- [Trend 1: e.g., "Shift from CRF to Transformers"] +- [Trend 2: e.g., "Increasing use of pre-trained models"] +``` + +--- + +### Phase 6: Document Research + +#### 6.1 Output Structure + +Save research to `references/` with fetched papers and synthesis: + +``` +references/ + # Fetched papers (via /paper-fetch) + 1810.04805/ # BERT paper + paper.pdf + paper.md # Full text for analysis + metadata.json + 2003.10555/ # PhoBERT paper + paper.pdf + paper.md + metadata.json + P19-1017/ # ACL paper + paper.pdf + paper.md + metadata.json + + # Research synthesis (this skill) + research_vietnamese_pos/ + README.md # Research summary & findings + papers.md # Paper database with notes + comparison.md # Comparison tables + bibliography.bib # BibTeX references + sota.md # State-of-the-art summary +``` + +#### 6.2 Research Report Template + +```markdown +# Literature Review: [Topic] + +**Date**: [YYYY-MM-DD] +**Research Questions**: [RQs] + +## Executive Summary +[1 paragraph overview] + +## Methodology +- **Search sources**: [List] +- **Search terms**: [Keywords] +- **Timeframe**: [Date range] +- **Inclusion criteria**: [Criteria] + +## PRISMA Flow +- Records identified: N +- Studies included: N + +## Findings + +### RQ1: [Question] +[Answer with citations] + +### RQ2: [Question] +[Answer with citations] + +## State-of-the-Art +[Current best methods/results] + +## Research Gaps +[Identified opportunities] + +## Recommendations +[Suggested directions] + +## References +[Bibliography] +``` + +--- + +## Best Practices (ACL/NeurIPS/ICML) + +### DO: +- **Explain differences**: "Related work should not just list prior work, but explain how the proposed work differs" (NeurIPS guidelines) +- **Be comprehensive**: Cover all major approaches and methods +- **Be fair**: Acknowledge contributions of prior work +- **Be current**: Include recent work (but contemporaneous papers within 2 months are excused) +- **Include proper citations**: Use DOIs or ACL Anthology links (ACL requirement) + +### DON'T: +- Just list papers without synthesis +- Ignore non-English language work +- Miss seminal papers in the field +- Cherry-pick only papers that support your position +- Dismiss work as "obvious in retrospect" + +### Quality Checks: +- [ ] All major approaches covered +- [ ] Recent work (last 2-3 years) included +- [ ] Seminal papers cited +- [ ] Fair characterization of prior work +- [ ] Clear connection to your work +- [ ] Proper citation format + +--- + +## Quick Reference: API Endpoints + +```bash +# Semantic Scholar - Search +curl "https://api.semanticscholar.org/graph/v1/paper/search?query=QUERY&limit=20&fields=title,year,authors,citationCount,abstract" + +# Semantic Scholar - Paper details +curl "https://api.semanticscholar.org/graph/v1/paper/PAPER_ID?fields=title,abstract,citations,references,tldr" + +# Semantic Scholar - Author papers +curl "https://api.semanticscholar.org/graph/v1/author/AUTHOR_ID/papers?fields=title,year,venue" + +# arXiv - Search +curl "http://export.arxiv.org/api/query?search_query=QUERY&max_results=20" + +# DBLP - Search +curl "https://dblp.org/search/publ/api?q=QUERY&format=json" +``` + +--- + +## References + +Based on guidelines from: +- [ACL Rolling Review Author Guidelines](https://aclrollingreview.org/authors) +- [NeurIPS Reviewer Guidelines](https://neurips.cc/Conferences/2025/ReviewerGuidelines) +- [ICML Paper Guidelines](https://icml.cc/Conferences/2024/PaperGuidelines) +- [PRISMA Statement](https://www.prisma-statement.org/) +- [How-to conduct a systematic literature review (CS)](https://www.sciencedirect.com/science/article/pii/S2215016122002746) +- [Semantic Scholar API](https://api.semanticscholar.org/api-docs/) +- [ACL Anthology](https://aclanthology.org) diff --git a/.claude/skills/paper-review/SKILL.md b/.claude/skills/paper-review/SKILL.md new file mode 100644 index 0000000000000000000000000000000000000000..61dc1e9149d920eb937b06e153a5bc0d6d4c2330 --- /dev/null +++ b/.claude/skills/paper-review/SKILL.md @@ -0,0 +1,275 @@ +--- +name: paper-review +description: Review research papers following ACL/EMNLP conference standards. Provides structured feedback with soundness, excitement, and overall assessment scores. +argument-hint: "[file-path]" +--- + +# Academic Paper Review (ACL/EMNLP Standards) + +Review papers following ACL Rolling Review (ARR) guidelines and best practices from top NLP conferences. + +## Target File + +Review the file: $ARGUMENTS + +If no file specified, review `TECHNICAL_REPORT.md` in the project root. + +## Review Process + +### Step 1: Reading Strategy (Two-Pass Method) + +1. **First Pass (Skim)**: Read abstract, introduction, and conclusion first to understand research questions, scope, and claimed contributions +2. **Second Pass (Deep Dive)**: Evaluate technical soundness, methodology, evidence quality, and reproducibility + +### Step 2: Research Relevant Papers + +Before completing the review, research the current state of the field to properly contextualize the contribution. + +#### 2.1 Identify Key Topics +Extract from the paper: +- Main task/problem (e.g., "Vietnamese POS tagging", "named entity recognition") +- Methods used (e.g., "CRF", "transformer", "BERT-based") +- Dataset/benchmark names +- Baseline systems mentioned + +#### 2.2 Search for Related Work +Use web search to find: + +1. **State-of-the-art results** on the same task/dataset: + - Search: "[task name] [dataset name] benchmark results [current year]" + - Search: "[task name] state of the art [current year]" + +2. **Competing approaches**: + - Search: "[task name] [alternative method] comparison" + - Search: "best [task name] models [current year]" + +3. **Prior work by same authors** (for context on research trajectory): + - Search author names + institution + +4. **Survey papers** for comprehensive background: + - Search: "[task name] survey" OR "[task name] review paper" + +5. **Datasets and benchmarks**: + - Search: "[dataset name] leaderboard" OR "[dataset name] benchmark" + +#### 2.3 Verify Claims +Cross-check the paper's claims against found literature: +- Are baseline comparisons fair and up-to-date? +- Are cited SOTA numbers accurate? +- Is related work coverage comprehensive? +- Are there significant missing references? + +#### 2.4 Document Findings +Record relevant papers found during research: + +```markdown +## Related Work Research + +### Papers Found +| Paper | Year | Method | Results | Relevance | +|-------|------|--------|---------|-----------| +| [Title] | [Year] | [Method] | [Key metric] | [Why relevant] | + +### Missing from Related Work +- [Paper that should have been cited] + +### SOTA Verification +- Claimed SOTA: [what paper claims] +- Actual SOTA: [what you found] +- Gap: [difference if any] +``` + +### Step 3: Write Review + +With both the paper content and research context, write the formal review following the ARR structure below. + +## ARR Review Form Structure + +Provide your review in the following structure: + +```markdown +## Paper Summary + +[2-3 sentences describing what the paper is about, helping editors understand the topic] + +## Summary of Strengths + +Major reasons to publish this paper at a selective *ACL venue: + +1. [Strength 1 - be specific, reference sections/tables] +2. [Strength 2] +3. [Strength 3] + +## Summary of Weaknesses + +Numbered concerns that prevent prioritizing this work: + +1. [Weakness 1 - specific and actionable] +2. [Weakness 2] +3. [Weakness 3] + +## Scores + +### Soundness: [1-5] +- 5: Excellent - No major issues, claims well-supported +- 4: Good - Minor issues that don't affect main claims +- 3: Acceptable - Some issues but core contributions valid +- 2: Poor - Significant issues undermine key claims +- 1: Major Issues - Not sufficiently thorough for publication + +### Excitement: [1-5] +- 5: Highly Exciting - Would recommend to others, transformational +- 4: Exciting - Important contribution to the field +- 3: Moderately Exciting - Incremental but solid work +- 2: Somewhat Boring - Limited novelty or impact +- 1: Not Exciting - Routine work with minimal contribution + +### Overall Assessment: [1-5] +- 5: Award consideration (top 2.5%) +- 4: Strong accept - main conference +- 3: Borderline - Findings track appropriate +- 2: Resubmit next cycle - substantial revisions needed +- 1: Do not resubmit + +### Reproducibility: [1-5] +- 5: Could reproduce results exactly +- 4: Could mostly reproduce, minor variation expected +- 3: Partial reproduction possible +- 2: Significant details missing +- 1: Cannot reproduce + +### Confidence: [1-5] +- 5: Expert - positive my evaluation is correct +- 4: High - familiar with related work +- 3: Medium - read related papers but not expert +- 2: Low - educated guess +- 1: Not my area + +## Detailed Comments + +### Technical Soundness +[Evaluate methodology, experimental design, statistical validity] + +### Novelty and Contribution +[Assess originality - don't dismiss work just because method is "simple" or results seem "obvious in retrospect"] + +### Clarity and Presentation +[Focus on substance, not style - note if non-native English but don't penalize] + +### Reproducibility Assessment +[Check for: dataset details, hyperparameters, code availability, training configuration] + +### Limitations and Ethics +[Evaluate if authors adequately discuss limitations and potential negative impacts] + +## Related Work Research + +### Papers Found +| Paper | Year | Method | Results | Relevance | +|-------|------|--------|---------|-----------| +| [Title] | [Year] | [Method] | [Key metric] | [Why relevant] | + +### Missing Citations +[Important papers not cited that should be referenced] + +### SOTA Verification +- **Claimed**: [what the paper claims as baseline/SOTA] +- **Actual**: [current SOTA from your research] +- **Assessment**: [whether claims are accurate] + +## Questions for Authors + +[Specific questions that could be addressed in author response - do NOT ask for new experiments] + +## Minor Issues + +[Typos, formatting, missing references - not grounds for rejection] + +## Suggestions for Improvement + +[Constructive, actionable recommendations to strengthen the work] +``` + +## Review Principles (ACL/EMNLP Best Practices) + +### DO: +- **Be specific**: Reference particular sections, equations, tables, or line numbers +- **Be constructive**: Suggest how to improve, not just what's wrong +- **Be kind**: Write the review you would like to receive +- **Justify scores**: Low soundness scores MUST cite specific technical flaws +- **Consider diverse contributions**: Novel methodology, insightful analysis, new resources, theoretical advances +- **Evaluate claimed contributions**: A paper only needs sufficient evidence for its stated claims + +### DO NOT: +- Reject because results aren't SOTA (ask "state of which art?") +- Dismiss work as "obvious in retrospect" without prior empirical validation +- Demand experiments beyond the paper's stated scope +- Criticize for not using deep learning (method diversity is valuable) +- Reject resource papers (datasets are as important as models) +- Reject work on non-English languages +- Penalize for simple methods (often most cited papers use simple methods) +- Use sarcasm or dismissive language +- Generate AI-written review content (violates ACL policy) + +### Common Review Problems to Avoid (ARR Issue Codes): +- **I1**: Lack of specificity - vague criticisms without examples +- **I2**: Score-content misalignment - low scores without stated flaws +- **I3**: Unprofessional tone - harsh or dismissive language +- **I4**: Demanding out-of-scope work +- **I5**: Ignoring author responses without explanation + +## Evaluation Checklist + +### Methodology +- [ ] Research questions clearly stated +- [ ] Methods appropriate for research questions +- [ ] Baselines appropriate and fairly compared +- [ ] Statistical significance properly addressed +- [ ] Limitations of approach acknowledged + +### Experiments +- [ ] Datasets properly described (source, size, splits, preprocessing) +- [ ] Evaluation metrics appropriate for the task +- [ ] Training details sufficient for reproduction +- [ ] Ablation studies or analysis provided +- [ ] Results support the claims made + +### Presentation +- [ ] Abstract accurately summarizes contributions +- [ ] Introduction motivates the problem +- [ ] Related work comprehensive and fair +- [ ] Figures/tables readable and informative +- [ ] Conclusion matches actual contributions + +### Related Work Verification (from Step 2 Research) +- [ ] Key prior work on same task is cited +- [ ] Baseline comparisons use current methods +- [ ] SOTA claims are accurate and up-to-date +- [ ] No significant missing references +- [ ] Fair characterization of competing approaches + +### Responsible NLP +- [ ] Limitations section present and substantive +- [ ] Potential negative impacts discussed +- [ ] Data collection ethics addressed (if applicable) +- [ ] Bias considerations mentioned (if applicable) + +## Score Calibration Guidelines + +**Soundness vs Excitement**: These are orthogonal. A paper can be: +- High soundness, low excitement: Solid but incremental +- Low soundness, high excitement: Interesting idea but flawed execution +- Both should be reflected independently + +**Overall Assessment**: Consider: +- Does the paper advance the field? +- Would the NLP community benefit from this work? +- Are the claimed contributions adequately supported? + +## References + +Based on guidelines from: +- [ACL Rolling Review Reviewer Guidelines](https://aclrollingreview.org/reviewerguidelines) +- [ARR Review Form](https://aclrollingreview.org/reviewform) +- [EMNLP 2020: How to Write Good Reviews](https://2020.emnlp.org/blog/2020-05-17-write-good-reviews/) +- [ACL 2023 Review Process](https://2023.aclweb.org/blog/review-basics/) diff --git a/.claude/skills/paper-write/SKILL.md b/.claude/skills/paper-write/SKILL.md new file mode 100644 index 0000000000000000000000000000000000000000..87cb86750a6132707351c31082b81b7021c6fc6c --- /dev/null +++ b/.claude/skills/paper-write/SKILL.md @@ -0,0 +1,402 @@ +--- +name: paper-write +description: Write or improve technical reports following ACL/EMNLP conference standards. Generates publication-ready sections with proper structure and formatting. +argument-hint: "[section] or [output-file]" +--- + +# Technical Paper Writing (ACL/EMNLP Standards) + +Write or improve technical papers following ACL Rolling Review guidelines and best practices from top NLP conferences. + +## Target + +**Arguments**: $ARGUMENTS + +- If argument is a section name (abstract, introduction, methodology, experiments, related-work, conclusion, limitations), generate that section +- If argument is a file path, write the complete paper to that file +- If no argument, analyze the project and generate a complete TECHNICAL_REPORT.md + +## Writing Process + +### Step 1: Project Analysis + +Before writing, analyze the codebase to understand: + +1. **Core Contribution**: What is the main technical contribution? +2. **Methodology**: What algorithms/models/approaches are used? +3. **Data**: What datasets are used for training/evaluation? +4. **Results**: What are the key metrics and findings? +5. **Implementation**: What are the technical details (hyperparameters, architecture)? + +Search for: +- README.md, CLAUDE.md for project overview +- Training scripts for methodology details +- Evaluation scripts for metrics +- Config files for hyperparameters +- Model files for architecture + +### Step 2: Research Context + +Use web search to contextualize the contribution: + +1. **State-of-the-art**: Search "[task] state of the art [year]" +2. **Benchmarks**: Search "[dataset] benchmark leaderboard" +3. **Related methods**: Search "[method] [task] comparison" +4. **Prior work**: Search key paper titles for citations + +### Step 3: Write Paper + +Follow the ACL paper structure below. + +--- + +## ACL Paper Structure + +### 1. Title + +- Concise and informative (max 12 words) +- Include: task, method, language/domain if specific +- Avoid: "Novel", "New", "Improved" without substance + +**Good examples**: +- "Vietnamese POS Tagging with Conditional Random Fields" +- "BERT-based Named Entity Recognition for Legal Documents" + +### 2. Abstract (max 200 words) + +Structure in 4-5 sentences: + +``` +[Problem/Motivation] [Task] remains challenging due to [specific challenges]. +[Approach] We present [method name], a [brief description of approach]. +[Key Innovation] Our method [key differentiator from prior work]. +[Results] Experiments on [dataset] show [main result with number]. +[Impact/Availability] [Code/model availability statement]. +``` + +**Tips**: +- Be specific with numbers: "achieves 95.89% accuracy" not "achieves high accuracy" +- Avoid vague claims: "outperforms baselines" → "outperforms VnCoreNLP by 2.1%" +- Include reproducibility info if possible + +### 3. Introduction (1-1.5 pages) + +**Paragraph 1: Problem & Motivation** +- What is the task? Why is it important? +- What are the real-world applications? + +**Paragraph 2: Challenges** +- What makes this problem difficult? +- What are the specific challenges for this language/domain? + +**Paragraph 3: Existing Approaches & Limitations** +- What methods have been tried? +- What are their limitations? + +**Paragraph 4: Our Approach** +- What is your method? +- How does it address the limitations? + +**Paragraph 5: Contributions** (bulleted list, max 3) +```markdown +Our main contributions are: +- We propose [method] for [task], achieving [result] +- We release [dataset/model/code] for [purpose] +- We provide [analysis/insights] showing [finding] +``` + +**Paragraph 6: Paper Organization** (optional) +- Brief roadmap of remaining sections + +### 4. Related Work (0.5-1 page) + +Organize by themes, not chronologically: + +```markdown +## Related Work + +### [Theme 1: e.g., "Traditional Approaches"] +[Discussion of rule-based, statistical methods...] + +### [Theme 2: e.g., "Neural Methods"] +[Discussion of deep learning approaches...] + +### [Theme 3: e.g., "Vietnamese NLP"] +[Discussion of language-specific work...] +``` + +**Tips**: +- Cite 15-30 papers for a full paper +- Be fair to prior work - acknowledge their contributions +- Clearly state how your work differs +- Use ACL Anthology for citations when available + +### 5. Methodology (1.5-2 pages) + +**5.1 Problem Formulation** +- Formal definition with mathematical notation +- Input/output specification + +**5.2 Model Architecture** +- High-level overview (with figure if helpful) +- Detailed description of each component + +**5.3 Feature Engineering** (if applicable) +- List all features with clear notation +- Justify feature choices + +**5.4 Training** +- Loss function +- Optimization algorithm +- Hyperparameters (in table format) + +```markdown +| Parameter | Value | Description | +|-----------|-------|-------------| +| Learning rate | 0.001 | Adam optimizer | +| Batch size | 32 | Training batch | +| Epochs | 100 | Maximum iterations | +``` + +### 6. Experimental Setup (0.5-1 page) + +**6.1 Datasets** + +```markdown +| Dataset | Train | Dev | Test | Domain | +|---------|-------|-----|------|--------| +| [Name] | [N] | [N] | [N] | [Domain] | +``` + +Include: +- Source and citation +- Preprocessing steps +- Train/dev/test split rationale + +**6.2 Baselines** +- List all baseline systems with citations +- Brief description of each + +**6.3 Evaluation Metrics** +- Define each metric mathematically +- Justify metric choices for the task + +**6.4 Implementation Details** +- Framework/library versions +- Hardware used +- Random seeds for reproducibility + +### 7. Results (1-1.5 pages) + +**7.1 Main Results** + +Present main comparison table: + +```markdown +| Model | Accuracy | Precision | Recall | F1 | +|-------|----------|-----------|--------|-----| +| Baseline 1 | X.XX | X.XX | X.XX | X.XX | +| Baseline 2 | X.XX | X.XX | X.XX | X.XX | +| **Ours** | **X.XX** | **X.XX** | **X.XX** | **X.XX** | +``` + +**7.2 Analysis** +- Why does your method work? +- Per-class/category breakdown +- Statistical significance (p-values if applicable) + +**7.3 Ablation Study** +- What happens when you remove components? +- Which features/components contribute most? + +**7.4 Error Analysis** +- Common error patterns +- Failure cases with examples +- Linguistic analysis if applicable + +### 8. Discussion (optional, 0.5 page) + +- Broader implications of findings +- Comparison with concurrent work +- Unexpected observations + +### 9. Conclusion (0.5 page) + +**Paragraph 1: Summary** +- Restate the problem and your approach +- Highlight main results + +**Paragraph 2: Limitations** (can be separate section) +- Honest assessment of limitations +- What doesn't your method handle well? + +**Paragraph 3: Future Work** +- 2-3 concrete directions for future research + +### 10. Limitations Section (REQUIRED) + +ACL requires a dedicated "Limitations" section. Include: + +- Data limitations (domain, size, annotation quality) +- Method limitations (assumptions, failure cases) +- Evaluation limitations (metrics, benchmarks) +- Scope limitations (languages, tasks) + +```markdown +## Limitations + +This work has several limitations: + +1. **Data**: Our model is trained on [domain] data and may not generalize to [other domains]. + +2. **Method**: [Specific limitation of the approach]. + +3. **Evaluation**: We evaluate only on [dataset]; performance on other benchmarks is unknown. + +4. **Scope**: Our work focuses on [language/task]; extension to [other scenarios] requires further investigation. +``` + +### 11. Ethics Statement (if applicable) + +Address: +- Data collection ethics +- Potential misuse +- Bias considerations +- Environmental impact (for large models) + +--- + +## Formatting Guidelines + +### Page Limits +- **Long paper**: 8 pages content + unlimited references +- **Short paper**: 4 pages content + unlimited references +- Limitations, ethics, acknowledgments don't count + +### Required Elements +- [ ] Title (15pt bold) +- [ ] Abstract (max 200 words) +- [ ] Sections numbered with Arabic numerals +- [ ] Limitations section (after conclusion, before references) +- [ ] References (unnumbered) + +### Figures & Tables +- Number sequentially (Figure 1, Table 1) +- Captions below figures, above tables (10pt) +- Reference all figures/tables in text +- Ensure grayscale readability + +### Citations +- Use ACL Anthology when available +- Format: (Author, Year) or Author (Year) +- Include DOIs when available + +--- + +## Writing Tips + +### DO: +- **Be specific**: Use numbers, not vague claims +- **Be honest**: Acknowledge limitations +- **Be concise**: Every sentence should add value +- **Be clear**: Define terms, explain notation +- **Be fair**: Give credit to prior work + +### DON'T: +- Oversell contributions +- Hide negative results +- Use excessive jargon +- Make claims without evidence +- Ignore reviewer guidelines + +### Common Mistakes to Avoid: +1. Abstract that doesn't match paper content +2. Introduction that's too long/detailed +3. Related work that's just a list of papers +4. Methodology without enough detail to reproduce +5. Results without error analysis +6. Missing or superficial limitations section + +--- + +## Section Templates + +### Abstract Template +``` +[Task] is [importance/challenge]. Existing methods [limitation]. +We present [method], which [key innovation]. +Our approach [brief description]. +Experiments on [dataset] demonstrate [main result]. +[Additional contribution: code/data release]. +``` + +### Introduction Contribution Template +```markdown +Our main contributions are as follows: +- We propose **[Method Name]**, a [brief description] for [task] that [key advantage]. +- We conduct extensive experiments on [datasets], achieving [specific result] and outperforming [baselines] by [margin]. +- We release our [code/model/data] at [URL] to facilitate future research. +``` + +### Conclusion Template +``` +We presented [method] for [task]. Our approach [key innovation]. +Experiments on [dataset] show [main findings]. +Our analysis reveals [key insight]. + +Limitations include [honest limitations]. + +Future work includes [2-3 specific directions]. +``` + +--- + +## Checklist Before Submission + +### Content +- [ ] Abstract summarizes all key points +- [ ] Introduction clearly states contributions +- [ ] Related work is comprehensive and fair +- [ ] Methodology has enough detail to reproduce +- [ ] Experiments include baselines and ablations +- [ ] Results include error analysis +- [ ] Limitations section is substantive +- [ ] Conclusion matches actual contributions + +### Formatting +- [ ] Within page limits +- [ ] All figures/tables referenced in text +- [ ] All citations properly formatted +- [ ] No orphaned section headers +- [ ] Consistent notation throughout + +### Reproducibility +- [ ] Hyperparameters specified +- [ ] Dataset details provided +- [ ] Random seeds mentioned +- [ ] Code/data availability stated + +--- + +## Output Format + +Generate the paper in Markdown format with: + +1. Clear section headers (## for main sections, ### for subsections) +2. Tables using Markdown table syntax +3. Math using LaTeX notation ($..$ for inline, $$...$$ for display) +4. Code blocks for algorithms/features +5. Proper citation placeholders: (Author, Year) + +Save to the specified output file or TECHNICAL_REPORT.md by default. + +--- + +## References + +Based on guidelines from: +- [ACL Paper Formatting Guidelines](https://acl-org.github.io/ACLPUB/formatting.html) +- [ACL Rolling Review Author Guidelines](http://aclrollingreview.org/authors) +- [Tips for Writing NLP Papers](https://medium.com/@vered1986/tips-for-writing-nlp-papers-9c729a2f9e1f) +- [Stanford Tips for Writing Technical Papers](https://cs.stanford.edu/people/widom/paper-writing.html) +- [EMNLP 2024 Call for Papers](https://2024.emnlp.org/calls/main_conference_papers/) diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..e4067e91913ce318d5b947d25822282aa1908906 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,6 @@ +*.pdf filter=lfs diff=lfs merge=lfs -text +*.png filter=lfs diff=lfs merge=lfs -text +*.jpg filter=lfs diff=lfs merge=lfs -text +*.jpeg filter=lfs diff=lfs merge=lfs -text +*.gif filter=lfs diff=lfs merge=lfs -text +*.synctex filter=lfs diff=lfs merge=lfs -text diff --git a/references/2007.rivf.hoang/paper.md b/references/2007.rivf.hoang/paper.md new file mode 100644 index 0000000000000000000000000000000000000000..4892c45295340e7730eb1280f25d882497bb10fd --- /dev/null +++ b/references/2007.rivf.hoang/paper.md @@ -0,0 +1,39 @@ +--- +title: "A Comparative Study on Vietnamese Text Classification Methods" +authors: + - "Cong Duy Vu Hoang" + - "Dien Dinh" + - "Le Nguyen Nguyen" + - "Quoc Hung Ngo" +year: 2007 +venue: "IEEE RIVF 2007" +url: "https://ieeexplore.ieee.org/document/4223084/" +--- + +# A Comparative Study on Vietnamese Text Classification Methods + +## Abstract + +This paper presents two different approaches for Vietnamese text classification: Bag of Words (BOW) and Statistical N-Gram Language Modeling. Based on a Vietnamese news corpus, these approaches achieved an average of >95% accuracy with an average 79 minutes classifying time for about 14,000 documents. + +## Key Contributions + +1. Introduced the VNTC (Vietnamese News Text Classification) corpus +2. Compared BOW and N-gram language model approaches for Vietnamese text classification +3. Demonstrated SVM effectiveness for Vietnamese text + +## Results + +| Method | Accuracy | +|--------|----------| +| N-gram LM | 97.1% | +| SVM Multi | 93.4% | +| BOW + SVM | ~92% | + +## Dataset + +- VNTC: Vietnamese News Text Classification Corpus +- 10 topics: Politics, Lifestyle, Science, Business, Law, Health, World, Sports, Culture, Technology +- Available: https://github.com/duyvuleo/VNTC + +*Full text available at IEEE Xplore* diff --git a/references/2018.kse.nguyen/paper.md b/references/2018.kse.nguyen/paper.md new file mode 100644 index 0000000000000000000000000000000000000000..23e05c15c9edb30a480ce3210ac3a3cfdcd55c73 --- /dev/null +++ b/references/2018.kse.nguyen/paper.md @@ -0,0 +1,32 @@ +--- +title: "UIT-VSFC: Vietnamese Students' Feedback Corpus for Sentiment Analysis" +authors: + - "Kiet Van Nguyen" + - "Vu Duc Nguyen" + - "Phu Xuan-Vinh Nguyen" + - "Tham Thi-Hong Truong" + - "Ngan Luu-Thuy Nguyen" +year: 2018 +venue: "KSE 2018" +url: "https://ieeexplore.ieee.org/document/8573337/" +--- + +# UIT-VSFC: Vietnamese Students' Feedback Corpus for Sentiment Analysis + +## Abstract + +Vietnamese Students' Feedback Corpus (UIT-VSFC) is a resource consisting of over 16,000 sentences which are human-annotated with two different tasks: sentiment-based and topic-based classifications. + +The corpus achieved 91.20% of the inter-annotator agreement for the sentiment-based task and 71.07% for the topic-based task. + +Baseline models built with the Maximum Entropy classifier achieved approximately 88% of the sentiment F1-score and over 84% of the topic F1-score. + +## Dataset Statistics + +- Total sentences: 16,175 +- Sentiment labels: Positive, Negative, Neutral +- Topic labels: Multiple categories +- Domain: Vietnamese university student feedback +- Available at: https://huggingface.co/datasets/uitnlp/vietnamese_students_feedback + +*Full text available at IEEE Xplore* diff --git a/references/2019.arxiv.conneau/paper.md b/references/2019.arxiv.conneau/paper.md new file mode 100644 index 0000000000000000000000000000000000000000..8fa7b690c06b2290464bd2139a54553fb9d7b756 --- /dev/null +++ b/references/2019.arxiv.conneau/paper.md @@ -0,0 +1,35 @@ +--- +title: "Unsupervised Cross-lingual Representation Learning at Scale" +authors: + - "Alexis Conneau" + - "Kartikay Khandelwal" + - "Naman Goyal" + - "Vishrav Chaudhary" + - "Guillaume Wenzek" + - "Francisco Guzmán" + - "Edouard Grave" + - "Myle Ott" + - "Luke Zettlemoyer" + - "Veselin Stoyanov" +year: 2019 +venue: "arXiv" +url: "https://arxiv.org/abs/1911.02116" +arxiv: "1911.02116" +--- + +\nobibliography{acl2020} +\bibliographystyle{acl_natbib} +\appendix +\onecolumn +# Supplementary materials +# Languages and statistics for CC-100 used by \xlmr +In this section we present the list of languages in the CC-100 corpus we created for training \xlmr. We also report statistics such as the number of tokens and the size of each monolingual corpus. +\label{sec:appendix_A} +\insertDataStatistics + +\newpage +# Model Architectures and Sizes +As we showed in section 5, capacity is an important parameter for learning strong cross-lingual representations. In the table below, we list multiple monolingual and multilingual models used by the research community and summarize their architectures and total number of parameters. +\label{sec:appendix_B} + +\insertParameters \ No newline at end of file diff --git a/references/2019.arxiv.conneau/paper.pdf b/references/2019.arxiv.conneau/paper.pdf new file mode 100644 index 0000000000000000000000000000000000000000..29b54883bea9fb0158b150121cf9c8d025873651 --- /dev/null +++ b/references/2019.arxiv.conneau/paper.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bf2fbb1aa1805ab6f892a4a421ffd4d7575df37343980b9a3729855577d2d8a1 +size 398981 diff --git a/references/2019.arxiv.conneau/paper.tex b/references/2019.arxiv.conneau/paper.tex new file mode 100644 index 0000000000000000000000000000000000000000..c4e6f3983dfebfac53b7c11a894ed59112a1757c --- /dev/null +++ b/references/2019.arxiv.conneau/paper.tex @@ -0,0 +1,45 @@ +\documentclass[11pt,a4paper]{article} +\usepackage[hyperref]{acl2020} +\usepackage{times} +\usepackage{latexsym} +\renewcommand{\UrlFont}{\ttfamily\small} + +% This is not strictly necessary, and may be commented out, +% but it will improve the layout of the manuscript, +% and will typically save some space. +\usepackage{microtype} +\usepackage{graphicx} +\usepackage{subfigure} +\usepackage{booktabs} % for professional tables +\usepackage{url} +\usepackage{times} +\usepackage{latexsym} +\usepackage{array} +\usepackage{adjustbox} +\usepackage{multirow} +% \usepackage{subcaption} +\usepackage{hyperref} +\usepackage{longtable} +\usepackage{bibentry} +\newcommand{\xlmr}{\textit{XLM-R}\xspace} +\newcommand{\mbert}{mBERT\xspace} +\input{content/tables} + +\begin{document} +\nobibliography{acl2020} +\bibliographystyle{acl_natbib} +\appendix +\onecolumn +\section*{Supplementary materials} +\section{Languages and statistics for CC-100 used by \xlmr} +In this section we present the list of languages in the CC-100 corpus we created for training \xlmr. We also report statistics such as the number of tokens and the size of each monolingual corpus. +\label{sec:appendix_A} +\insertDataStatistics + +\newpage +\section{Model Architectures and Sizes} +As we showed in section 5, capacity is an important parameter for learning strong cross-lingual representations. In the table below, we list multiple monolingual and multilingual models used by the research community and summarize their architectures and total number of parameters. +\label{sec:appendix_B} + +\insertParameters +\end{document} \ No newline at end of file diff --git a/references/2019.arxiv.conneau/source/XLMR Paper/acl2020.bib b/references/2019.arxiv.conneau/source/XLMR Paper/acl2020.bib new file mode 100644 index 0000000000000000000000000000000000000000..68a3b3d8cc0cd909a6959f98dbfb6d6d6f569a66 --- /dev/null +++ b/references/2019.arxiv.conneau/source/XLMR Paper/acl2020.bib @@ -0,0 +1,739 @@ +@inproceedings{koehn2007moses, + title={Moses: Open source toolkit for statistical machine translation}, + author={Koehn, Philipp and Hoang, Hieu and Birch, Alexandra and Callison-Burch, Chris and Federico, Marcello and Bertoldi, Nicola and Cowan, Brooke and Shen, Wade and Moran, Christine and Zens, Richard and others}, + booktitle={Proceedings of the 45th annual meeting of the ACL on 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Texas", + publisher = "Association for Computational Linguistics", + url = "https://www.aclweb.org/anthology/D16-1264", + doi = "10.18653/v1/D16-1264", + pages = "2383--2392", +} + +@article{lewis2019mlqa, + title={MLQA: Evaluating Cross-lingual Extractive Question Answering}, + author={Lewis, Patrick and O\u{g}uz, Barlas and Rinott, Ruty and Riedel, Sebastian and Schwenk, Holger}, + journal={arXiv preprint arXiv:1910.07475}, + year={2019} +} + +@inproceedings{sennrich2015neural, + title={Neural machine translation of rare words with subword units}, + author={Sennrich, Rico and Haddow, Barry and Birch, Alexandra}, + booktitle={Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics}, + pages = {1715-1725}, + year={2015} +} + +@article{eriguchi2018zero, + title={Zero-shot cross-lingual classification using multilingual neural machine translation}, + author={Eriguchi, Akiko and Johnson, Melvin and Firat, Orhan and Kazawa, Hideto and Macherey, Wolfgang}, + journal={arXiv preprint arXiv:1809.04686}, + year={2018} +} + +@article{smith2017offline, + title={Offline bilingual word vectors, orthogonal transformations and the inverted softmax}, + author={Smith, Samuel L and Turban, David HP and Hamblin, Steven and Hammerla, Nils Y}, + journal={International Conference on Learning Representations}, + year={2017} +} + +@article{artetxe2016learning, + title={Learning principled bilingual mappings of word embeddings while preserving monolingual invariance}, + author={Artetxe, Mikel and Labaka, Gorka and Agirre, Eneko}, + journal={Proceedings of EMNLP}, + year={2016} +} + +@article{ammar2016massively, + title={Massively multilingual word embeddings}, + author={Ammar, Waleed and Mulcaire, George and Tsvetkov, Yulia and Lample, Guillaume and Dyer, Chris and Smith, Noah A}, + journal={arXiv preprint arXiv:1602.01925}, + year={2016} +} + +@article{marcobaroni2015hubness, + title={Hubness and pollution: Delving into cross-space mapping for zero-shot 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https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language\_understanding\_paper.pdf}, + url={https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf}, + year={2018} +} + +@article{conneau2018senteval, + title={SentEval: An Evaluation Toolkit for Universal Sentence Representations}, + author={Conneau, Alexis and Kiela, Douwe}, + journal={LREC}, + year={2018} +} + +@article{devlin2018bert, + title={Bert: Pre-training of deep bidirectional transformers for language understanding}, + author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina}, + journal={NAACL}, + year={2018} +} + +@article{peters2018deep, + title={Deep contextualized word representations}, + author={Peters, Matthew E and Neumann, Mark and Iyyer, Mohit and Gardner, Matt and Clark, Christopher and Lee, Kenton and Zettlemoyer, Luke}, + journal={NAACL}, + year={2018} +} + +@article{ramachandran2016unsupervised, + title={Unsupervised pretraining for sequence to sequence learning}, + author={Ramachandran, Prajit and Liu, Peter J and Le, Quoc V}, + journal={arXiv preprint arXiv:1611.02683}, + year={2016} +} + +@inproceedings{kunchukuttan2018iit, + title={The IIT Bombay English-Hindi Parallel Corpus}, + author={Kunchukuttan Anoop and Mehta Pratik and Bhattacharyya Pushpak}, + booktitle={LREC}, + year={2018} +} + +@article{wu2019beto, + title={Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT}, + author={Wu, Shijie and Dredze, Mark}, + journal={EMNLP}, + year={2019} +} + +@inproceedings{lample-etal-2016-neural, + title = "Neural Architectures for Named Entity Recognition", + author = "Lample, Guillaume and + Ballesteros, Miguel and + Subramanian, Sandeep and + Kawakami, Kazuya and + Dyer, Chris", + booktitle = "NAACL", + month = jun, + year = "2016", + address = "San Diego, California", + publisher = "Association for Computational Linguistics", + url = "https://www.aclweb.org/anthology/N16-1030", + doi = "10.18653/v1/N16-1030", + pages = "260--270", +} + +@inproceedings{akbik2018coling, + title={Contextual String Embeddings for Sequence Labeling}, + author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland}, + booktitle = {COLING}, + pages = {1638--1649}, + year = {2018} +} + +@inproceedings{tjong-kim-sang-de-meulder-2003-introduction, + title = "Introduction to the {C}o{NLL}-2003 Shared Task: Language-Independent Named Entity Recognition", + author = "Tjong Kim Sang, Erik F. and + De Meulder, Fien", + booktitle = "Proceedings of the Seventh Conference on Natural Language Learning at {HLT}-{NAACL} 2003", + year = "2003", + url = "https://www.aclweb.org/anthology/W03-0419", + pages = "142--147", +} + +@inproceedings{tjong-kim-sang-2002-introduction, + title = "Introduction to the {C}o{NLL}-2002 Shared Task: Language-Independent Named Entity Recognition", + author = "Tjong Kim Sang, Erik F.", + booktitle = "{COLING}-02: The 6th Conference on Natural Language Learning 2002 ({C}o{NLL}-2002)", + year = "2002", + url = "https://www.aclweb.org/anthology/W02-2024", +} + +@InProceedings{TIEDEMANN12.463, + author = {Jörg Tiedemann}, + title = {Parallel Data, Tools and Interfaces in OPUS}, + booktitle = {LREC}, + year = {2012}, + month = {may}, + date = {23-25}, + address = {Istanbul, Turkey}, + editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis}, + publisher = {European Language Resources Association (ELRA)}, + isbn = {978-2-9517408-7-7}, + language = {english} + } + +@inproceedings{ziemski2016united, + title={The United Nations Parallel Corpus v1. 0.}, + author={Ziemski, Michal and Junczys-Dowmunt, Marcin and Pouliquen, Bruno}, + booktitle={LREC}, + year={2016} +} + +@article{roberta2019, + author = {Yinhan Liu and + Myle Ott and + Naman Goyal and + Jingfei Du and + Mandar Joshi and + Danqi Chen and + Omer Levy and + Mike Lewis and + Luke Zettlemoyer and + Veselin Stoyanov}, + title = {RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach}, + journal = {arXiv preprint arXiv:1907.11692}, + year = {2019} +} + + +@article{tan2019multilingual, + title={Multilingual neural machine translation with knowledge distillation}, + author={Tan, Xu and Ren, Yi and He, Di and Qin, Tao and Zhao, Zhou and Liu, Tie-Yan}, + journal={ICLR}, + year={2019} +} + +@article{siddhant2019evaluating, + title={Evaluating the Cross-Lingual Effectiveness of Massively Multilingual Neural Machine Translation}, + author={Siddhant, Aditya and Johnson, Melvin and Tsai, Henry and Arivazhagan, Naveen and Riesa, Jason and Bapna, Ankur and Firat, Orhan and Raman, Karthik}, + journal={AAAI}, + year={2019} +} + +@inproceedings{camacho2017semeval, + title={Semeval-2017 task 2: Multilingual and cross-lingual semantic word similarity}, + author={Camacho-Collados, Jose and Pilehvar, Mohammad Taher and Collier, Nigel and Navigli, Roberto}, + booktitle={Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)}, + pages={15--26}, + year={2017} +} + +@inproceedings{Pires2019HowMI, + title={How Multilingual is Multilingual BERT?}, + author={Telmo Pires and Eva Schlinger and Dan Garrette}, + booktitle={ACL}, + year={2019} +} + +@article{lample2019cross, + title={Cross-lingual language model pretraining}, + author={Lample, Guillaume and Conneau, Alexis}, + journal={NeurIPS}, + year={2019} +} + +@article{schuster2019cross, + title={Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing}, + author={Schuster, Tal and Ram, Ori and Barzilay, Regina and Globerson, Amir}, + journal={NAACL}, + year={2019} +} + +@inproceedings{chang2008optimizing, + title={Optimizing Chinese word segmentation for machine translation performance}, + author={Chang, Pi-Chuan and Galley, Michel and Manning, Christopher D}, + booktitle={Proceedings of the third workshop on statistical machine translation}, + pages={224--232}, + year={2008} +} + +@inproceedings{koehn2007moses, + title={Moses: Open source toolkit for statistical machine translation}, + author={Koehn, Philipp and Hoang, Hieu and Birch, Alexandra and Callison-Burch, Chris and Federico, Marcello and Bertoldi, Nicola and Cowan, Brooke and Shen, Wade and Moran, Christine and Zens, Richard and others}, + booktitle={Proceedings of the 45th annual meeting of the ACL on interactive poster and demonstration sessions}, + pages={177--180}, + year={2007}, + organization={Association for Computational Linguistics} +} + +@article{wenzek2019ccnet, + title={CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data}, + author={Wenzek, Guillaume and Lachaux, Marie-Anne and Conneau, Alexis and Chaudhary, Vishrav and Guzman, Francisco and Joulin, Armand and Grave, Edouard}, + journal={arXiv preprint arXiv:1911.00359}, + year={2019} +} + +@inproceedings{zhou2016cross, + title={Cross-lingual sentiment classification with bilingual document representation learning}, + author={Zhou, Xinjie and Wan, Xiaojun and Xiao, Jianguo}, + booktitle={Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, + pages={1403--1412}, + year={2016} +} + +@article{goyal2017accurate, + title={Accurate, large minibatch sgd: Training imagenet in 1 hour}, + author={Goyal, Priya and Doll{\'a}r, Piotr and Girshick, Ross and Noordhuis, Pieter and Wesolowski, Lukasz and Kyrola, Aapo and Tulloch, Andrew and Jia, Yangqing and He, Kaiming}, + journal={arXiv preprint arXiv:1706.02677}, + year={2017} +} + +@article{arivazhagan2019massively, + title={Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges}, + author={Arivazhagan, Naveen and Bapna, Ankur and Firat, Orhan and Lepikhin, Dmitry and Johnson, Melvin and Krikun, Maxim and Chen, Mia Xu and Cao, Yuan and Foster, George and Cherry, Colin and others}, + journal={arXiv preprint arXiv:1907.05019}, + year={2019} +} + +@inproceedings{pan2017cross, + title={Cross-lingual name tagging and linking for 282 languages}, + author={Pan, Xiaoman and Zhang, Boliang and May, Jonathan and Nothman, Joel and Knight, Kevin and Ji, Heng}, + booktitle={Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, + volume={1}, + pages={1946--1958}, + year={2017} +} + +@article{raffel2019exploring, + title={Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer}, + author={Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu}, + year={2019}, + journal={arXiv preprint arXiv:1910.10683}, +} + +@inproceedings{pennington2014glove, + author = {Jeffrey Pennington and Richard Socher and Christopher D. Manning}, + booktitle = {EMNLP}, + title = {GloVe: Global Vectors for Word Representation}, + year = {2014}, + pages = {1532--1543}, + url = {http://www.aclweb.org/anthology/D14-1162}, +} + +@article{kudo2018sentencepiece, + title={Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing}, + author={Kudo, Taku and Richardson, John}, + journal={EMNLP}, + year={2018} +} + +@article{rajpurkar2018know, + title={Know What You Don't Know: Unanswerable Questions for SQuAD}, + author={Rajpurkar, Pranav and Jia, Robin and Liang, Percy}, + journal={ACL}, + year={2018} +} + +@article{joulin2017bag, + title={Bag of Tricks for Efficient Text Classification}, + author={Joulin, Armand and Grave, Edouard and Mikolov, Piotr Bojanowski Tomas}, + journal={EACL 2017}, + pages={427}, + year={2017} +} + +@inproceedings{kudo2018subword, + title={Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates}, + author={Kudo, Taku}, + booktitle={ACL}, + pages={66--75}, + year={2018} +} + +@inproceedings{grave2018learning, + title={Learning Word Vectors for 157 Languages}, + author={Grave, Edouard and Bojanowski, Piotr and Gupta, Prakhar and Joulin, Armand and Mikolov, Tomas}, + booktitle={LREC}, + year={2018} +} \ No newline at end of file diff --git a/references/2019.arxiv.conneau/source/XLMR Paper/acl2020.sty b/references/2019.arxiv.conneau/source/XLMR Paper/acl2020.sty new file mode 100644 index 0000000000000000000000000000000000000000..b738cde2100e93d1db696511398c5b71790c22db --- /dev/null +++ b/references/2019.arxiv.conneau/source/XLMR Paper/acl2020.sty @@ -0,0 +1,560 @@ +% This is the LaTex style file for ACL 2020, based off of ACL 2019. + +% Addressing bibtex issues mentioned in https://github.com/acl-org/acl-pub/issues/2 +% Other major modifications include +% changing the color of the line numbers to a light gray; changing font size of abstract to be 10pt; changing caption font size to be 10pt. +% -- M Mitchell and Stephanie Lukin + +% 2017: modified to support DOI links in bibliography. Now uses +% natbib package rather than defining citation commands in this file. +% Use with acl_natbib.bst bib style. -- Dan Gildea + +% This is the LaTeX style for ACL 2016. It contains Margaret Mitchell's +% line number adaptations (ported by Hai Zhao and Yannick Versley). + +% It is nearly identical to the style files for ACL 2015, +% ACL 2014, EACL 2006, ACL2005, ACL 2002, ACL 2001, ACL 2000, +% EACL 95 and EACL 99. +% +% Changes made include: adapt layout to A4 and centimeters, widen abstract + +% This is the LaTeX style file for ACL 2000. It is nearly identical to the +% style files for EACL 95 and EACL 99. Minor changes include editing the +% instructions to reflect use of \documentclass rather than \documentstyle +% and removing the white space before the title on the first page +% -- John Chen, June 29, 2000 + +% This is the LaTeX style file for EACL-95. It is identical to the +% style file for ANLP '94 except that the margins are adjusted for A4 +% paper. -- abney 13 Dec 94 + +% The ANLP '94 style file is a slightly modified +% version of the style used for AAAI and IJCAI, using some changes +% prepared by Fernando Pereira and others and some minor changes +% by Paul Jacobs. + +% Papers prepared using the aclsub.sty file and acl.bst bibtex style +% should be easily converted to final format using this style. +% (1) Submission information (\wordcount, \subject, and \makeidpage) +% should be removed. +% (2) \summary should be removed. The summary material should come +% after \maketitle and should be in the ``abstract'' environment +% (between \begin{abstract} and \end{abstract}). +% (3) Check all citations. This style should handle citations correctly +% and also allows multiple citations separated by semicolons. +% (4) Check figures and examples. Because the final format is double- +% column, some adjustments may have to be made to fit text in the column +% or to choose full-width (\figure*} figures. + +% Place this in a file called aclap.sty in the TeX search path. +% (Placing it in the same directory as the paper should also work.) + +% Prepared by Peter F. Patel-Schneider, liberally using the ideas of +% other style hackers, including Barbara Beeton. +% This style is NOT guaranteed to work. It is provided in the hope +% that it will make the preparation of papers easier. +% +% There are undoubtably bugs in this style. If you make bug fixes, +% improvements, etc. please let me know. My e-mail address is: +% pfps@research.att.com + +% Papers are to be prepared using the ``acl_natbib'' bibliography style, +% as follows: +% \documentclass[11pt]{article} +% \usepackage{acl2000} +% \title{Title} +% \author{Author 1 \and Author 2 \\ Address line \\ Address line \And +% Author 3 \\ Address line \\ Address line} +% \begin{document} +% ... +% \bibliography{bibliography-file} +% \bibliographystyle{acl_natbib} +% \end{document} + +% Author information can be set in various styles: +% For several authors from the same institution: +% \author{Author 1 \and ... \and Author n \\ +% Address line \\ ... \\ Address line} +% if the names do not fit well on one line use +% Author 1 \\ {\bf Author 2} \\ ... \\ {\bf Author n} \\ +% For authors from different institutions: +% \author{Author 1 \\ Address line \\ ... \\ Address line +% \And ... \And +% Author n \\ Address line \\ ... \\ Address line} +% To start a seperate ``row'' of authors use \AND, as in +% \author{Author 1 \\ Address line \\ ... \\ Address line +% \AND +% Author 2 \\ Address line \\ ... \\ Address line \And +% Author 3 \\ Address line \\ ... \\ Address line} + +% If the title and author information does not fit in the area allocated, +% place \setlength\titlebox{} right after +% \usepackage{acl2015} +% where can be something larger than 5cm + +% include hyperref, unless user specifies nohyperref option like this: +% \usepackage[nohyperref]{naaclhlt2018} +\newif\ifacl@hyperref +\DeclareOption{hyperref}{\acl@hyperreftrue} +\DeclareOption{nohyperref}{\acl@hyperreffalse} +\ExecuteOptions{hyperref} % default is to use hyperref +\ProcessOptions\relax +\ifacl@hyperref + \RequirePackage{hyperref} + \usepackage{xcolor} % make links dark blue + \definecolor{darkblue}{rgb}{0, 0, 0.5} + \hypersetup{colorlinks=true,citecolor=darkblue, linkcolor=darkblue, urlcolor=darkblue} +\else + % This definition is used if the hyperref package is not loaded. + % It provides a backup, no-op definiton of \href. + % This is necessary because \href command is used in the acl_natbib.bst file. + \def\href#1#2{{#2}} + % We still need to load xcolor in this case because the lighter line numbers require it. (SC/KG/WL) + \usepackage{xcolor} +\fi + +\typeout{Conference Style for ACL 2019} + +% NOTE: Some laser printers have a serious problem printing TeX output. +% These printing devices, commonly known as ``write-white'' laser +% printers, tend to make characters too light. To get around this +% problem, a darker set of fonts must be created for these devices. +% + +\newcommand{\Thanks}[1]{\thanks{\ #1}} + +% A4 modified by Eneko; again modified by Alexander for 5cm titlebox +\setlength{\paperwidth}{21cm} % A4 +\setlength{\paperheight}{29.7cm}% A4 +\setlength\topmargin{-0.5cm} +\setlength\oddsidemargin{0cm} +\setlength\textheight{24.7cm} +\setlength\textwidth{16.0cm} +\setlength\columnsep{0.6cm} +\newlength\titlebox +\setlength\titlebox{5cm} +\setlength\headheight{5pt} +\setlength\headsep{0pt} +\thispagestyle{empty} +\pagestyle{empty} + + +\flushbottom \twocolumn \sloppy + +% We're never going to need a table of contents, so just flush it to +% save space --- suggested by drstrip@sandia-2 +\def\addcontentsline#1#2#3{} + +\newif\ifaclfinal +\aclfinalfalse +\def\aclfinalcopy{\global\aclfinaltrue} + +%% ----- Set up hooks to repeat content on every page of the output doc, +%% necessary for the line numbers in the submitted version. --MM +%% +%% Copied from CVPR 2015's cvpr_eso.sty, which appears to be largely copied from everyshi.sty. +%% +%% Original cvpr_eso.sty available at: http://www.pamitc.org/cvpr15/author_guidelines.php +%% Original evershi.sty available at: https://www.ctan.org/pkg/everyshi +%% +%% Copyright (C) 2001 Martin Schr\"oder: +%% +%% Martin Schr"oder +%% Cr"usemannallee 3 +%% D-28213 Bremen +%% Martin.Schroeder@ACM.org +%% +%% This program may be redistributed and/or modified under the terms +%% of the LaTeX Project Public License, either version 1.0 of this +%% license, or (at your option) any later version. +%% The latest version of this license is in +%% CTAN:macros/latex/base/lppl.txt. +%% +%% Happy users are requested to send [Martin] a postcard. :-) +%% +\newcommand{\@EveryShipoutACL@Hook}{} +\newcommand{\@EveryShipoutACL@AtNextHook}{} +\newcommand*{\EveryShipoutACL}[1] + {\g@addto@macro\@EveryShipoutACL@Hook{#1}} +\newcommand*{\AtNextShipoutACL@}[1] + {\g@addto@macro\@EveryShipoutACL@AtNextHook{#1}} +\newcommand{\@EveryShipoutACL@Shipout}{% + \afterassignment\@EveryShipoutACL@Test + \global\setbox\@cclv= % + } +\newcommand{\@EveryShipoutACL@Test}{% + \ifvoid\@cclv\relax + \aftergroup\@EveryShipoutACL@Output + \else + \@EveryShipoutACL@Output + \fi% + } +\newcommand{\@EveryShipoutACL@Output}{% + \@EveryShipoutACL@Hook% + \@EveryShipoutACL@AtNextHook% + \gdef\@EveryShipoutACL@AtNextHook{}% + \@EveryShipoutACL@Org@Shipout\box\@cclv% + } +\newcommand{\@EveryShipoutACL@Org@Shipout}{} +\newcommand*{\@EveryShipoutACL@Init}{% + \message{ABD: EveryShipout initializing macros}% + \let\@EveryShipoutACL@Org@Shipout\shipout + \let\shipout\@EveryShipoutACL@Shipout + } +\AtBeginDocument{\@EveryShipoutACL@Init} + +%% ----- Set up for placing additional items into the submitted version --MM +%% +%% Based on eso-pic.sty +%% +%% Original available at: https://www.ctan.org/tex-archive/macros/latex/contrib/eso-pic +%% Copyright (C) 1998-2002 by Rolf Niepraschk +%% +%% Which may be distributed and/or modified under the conditions of +%% the LaTeX Project Public License, either version 1.2 of this license +%% or (at your option) any later version. The latest version of this +%% license is in: +%% +%% http://www.latex-project.org/lppl.txt +%% +%% and version 1.2 or later is part of all distributions of LaTeX version +%% 1999/12/01 or later. +%% +%% In contrast to the original, we do not include the definitions for/using: +%% gridpicture, div[2], isMEMOIR[1], gridSetup[6][], subgridstyle{dotted}, labelfactor{}, gap{}, gridunitname{}, gridunit{}, gridlines{\thinlines}, subgridlines{\thinlines}, the {keyval} package, evenside margin, nor any definitions with 'color'. +%% +%% These are beyond what is needed for the NAACL/ACL style. +%% +\newcommand\LenToUnit[1]{#1\@gobble} +\newcommand\AtPageUpperLeft[1]{% + \begingroup + \@tempdima=0pt\relax\@tempdimb=\ESO@yoffsetI\relax + \put(\LenToUnit{\@tempdima},\LenToUnit{\@tempdimb}){#1}% + \endgroup +} +\newcommand\AtPageLowerLeft[1]{\AtPageUpperLeft{% + \put(0,\LenToUnit{-\paperheight}){#1}}} +\newcommand\AtPageCenter[1]{\AtPageUpperLeft{% + \put(\LenToUnit{.5\paperwidth},\LenToUnit{-.5\paperheight}){#1}}} +\newcommand\AtPageLowerCenter[1]{\AtPageUpperLeft{% + \put(\LenToUnit{.5\paperwidth},\LenToUnit{-\paperheight}){#1}}}% +\newcommand\AtPageLowishCenter[1]{\AtPageUpperLeft{% + \put(\LenToUnit{.5\paperwidth},\LenToUnit{-.96\paperheight}){#1}}} +\newcommand\AtTextUpperLeft[1]{% + \begingroup + \setlength\@tempdima{1in}% + \advance\@tempdima\oddsidemargin% + \@tempdimb=\ESO@yoffsetI\relax\advance\@tempdimb-1in\relax% + \advance\@tempdimb-\topmargin% + \advance\@tempdimb-\headheight\advance\@tempdimb-\headsep% + \put(\LenToUnit{\@tempdima},\LenToUnit{\@tempdimb}){#1}% + \endgroup +} +\newcommand\AtTextLowerLeft[1]{\AtTextUpperLeft{% + \put(0,\LenToUnit{-\textheight}){#1}}} +\newcommand\AtTextCenter[1]{\AtTextUpperLeft{% + \put(\LenToUnit{.5\textwidth},\LenToUnit{-.5\textheight}){#1}}} +\newcommand{\ESO@HookI}{} \newcommand{\ESO@HookII}{} +\newcommand{\ESO@HookIII}{} +\newcommand{\AddToShipoutPicture}{% + \@ifstar{\g@addto@macro\ESO@HookII}{\g@addto@macro\ESO@HookI}} +\newcommand{\ClearShipoutPicture}{\global\let\ESO@HookI\@empty} +\newcommand{\@ShipoutPicture}{% + \bgroup + \@tempswafalse% + \ifx\ESO@HookI\@empty\else\@tempswatrue\fi% + \ifx\ESO@HookII\@empty\else\@tempswatrue\fi% + \ifx\ESO@HookIII\@empty\else\@tempswatrue\fi% + \if@tempswa% + \@tempdima=1in\@tempdimb=-\@tempdima% + \advance\@tempdimb\ESO@yoffsetI% + \unitlength=1pt% + \global\setbox\@cclv\vbox{% + \vbox{\let\protect\relax + \pictur@(0,0)(\strip@pt\@tempdima,\strip@pt\@tempdimb)% + \ESO@HookIII\ESO@HookI\ESO@HookII% + \global\let\ESO@HookII\@empty% + \endpicture}% + \nointerlineskip% + \box\@cclv}% + \fi + \egroup +} +\EveryShipoutACL{\@ShipoutPicture} +\newif\ifESO@dvips\ESO@dvipsfalse +\newif\ifESO@grid\ESO@gridfalse +\newif\ifESO@texcoord\ESO@texcoordfalse +\newcommand*\ESO@griddelta{}\newcommand*\ESO@griddeltaY{} +\newcommand*\ESO@gridDelta{}\newcommand*\ESO@gridDeltaY{} +\newcommand*\ESO@yoffsetI{}\newcommand*\ESO@yoffsetII{} +\ifESO@texcoord + \def\ESO@yoffsetI{0pt}\def\ESO@yoffsetII{-\paperheight} + \edef\ESO@griddeltaY{-\ESO@griddelta}\edef\ESO@gridDeltaY{-\ESO@gridDelta} +\else + \def\ESO@yoffsetI{\paperheight}\def\ESO@yoffsetII{0pt} + \edef\ESO@griddeltaY{\ESO@griddelta}\edef\ESO@gridDeltaY{\ESO@gridDelta} +\fi + + +%% ----- Submitted version markup: Page numbers, ruler, and confidentiality. Using ideas/code from cvpr.sty 2015. --MM + +\font\aclhv = phvb at 8pt + +%% Define vruler %% + +%\makeatletter +\newbox\aclrulerbox +\newcount\aclrulercount +\newdimen\aclruleroffset +\newdimen\cv@lineheight +\newdimen\cv@boxheight +\newbox\cv@tmpbox +\newcount\cv@refno +\newcount\cv@tot +% NUMBER with left flushed zeros \fillzeros[] +\newcount\cv@tmpc@ \newcount\cv@tmpc +\def\fillzeros[#1]#2{\cv@tmpc@=#2\relax\ifnum\cv@tmpc@<0\cv@tmpc@=-\cv@tmpc@\fi +\cv@tmpc=1 % +\loop\ifnum\cv@tmpc@<10 \else \divide\cv@tmpc@ by 10 \advance\cv@tmpc by 1 \fi + \ifnum\cv@tmpc@=10\relax\cv@tmpc@=11\relax\fi \ifnum\cv@tmpc@>10 \repeat +\ifnum#2<0\advance\cv@tmpc1\relax-\fi +\loop\ifnum\cv@tmpc<#1\relax0\advance\cv@tmpc1\relax\fi \ifnum\cv@tmpc<#1 \repeat +\cv@tmpc@=#2\relax\ifnum\cv@tmpc@<0\cv@tmpc@=-\cv@tmpc@\fi \relax\the\cv@tmpc@}% +% \makevruler[][][][][] +\def\makevruler[#1][#2][#3][#4][#5]{\begingroup\offinterlineskip +\textheight=#5\vbadness=10000\vfuzz=120ex\overfullrule=0pt% +\global\setbox\aclrulerbox=\vbox to \textheight{% +{\parskip=0pt\hfuzz=150em\cv@boxheight=\textheight +\color{gray} +\cv@lineheight=#1\global\aclrulercount=#2% +\cv@tot\cv@boxheight\divide\cv@tot\cv@lineheight\advance\cv@tot2% +\cv@refno1\vskip-\cv@lineheight\vskip1ex% +\loop\setbox\cv@tmpbox=\hbox to0cm{{\aclhv\hfil\fillzeros[#4]\aclrulercount}}% +\ht\cv@tmpbox\cv@lineheight\dp\cv@tmpbox0pt\box\cv@tmpbox\break +\advance\cv@refno1\global\advance\aclrulercount#3\relax +\ifnum\cv@refno<\cv@tot\repeat}}\endgroup}% +%\makeatother + + +\def\aclpaperid{***} +\def\confidential{\textcolor{black}{ACL 2020 Submission~\aclpaperid. Confidential Review Copy. DO NOT DISTRIBUTE.}} + +%% Page numbering, Vruler and Confidentiality %% +% \makevruler[][][][][] + +% SC/KG/WL - changed line numbering to gainsboro +\definecolor{gainsboro}{rgb}{0.8, 0.8, 0.8} +%\def\aclruler#1{\makevruler[14.17pt][#1][1][3][\textheight]\usebox{\aclrulerbox}} %% old line +\def\aclruler#1{\textcolor{gainsboro}{\makevruler[14.17pt][#1][1][3][\textheight]\usebox{\aclrulerbox}}} + +\def\leftoffset{-2.1cm} %original: -45pt +\def\rightoffset{17.5cm} %original: 500pt +\ifaclfinal\else\pagenumbering{arabic} +\AddToShipoutPicture{% +\ifaclfinal\else +\AtPageLowishCenter{\textcolor{black}{\thepage}} +\aclruleroffset=\textheight +\advance\aclruleroffset4pt + \AtTextUpperLeft{% + \put(\LenToUnit{\leftoffset},\LenToUnit{-\aclruleroffset}){%left ruler + \aclruler{\aclrulercount}} + \put(\LenToUnit{\rightoffset},\LenToUnit{-\aclruleroffset}){%right ruler + \aclruler{\aclrulercount}} + } + \AtTextUpperLeft{%confidential + \put(0,\LenToUnit{1cm}){\parbox{\textwidth}{\centering\aclhv\confidential}} + } +\fi +} + +%%%% ----- End settings for placing additional items into the submitted version --MM ----- %%%% + +%%%% ----- Begin settings for both submitted and camera-ready version ----- %%%% + +%% Title and Authors %% + +\newcommand\outauthor{ + \begin{tabular}[t]{c} + \ifaclfinal + \bf\@author + \else + % Avoiding common accidental de-anonymization issue. --MM + \bf Anonymous ACL submission + \fi + \end{tabular}} + +% Changing the expanded titlebox for submissions to 2.5 in (rather than 6.5cm) +% and moving it to the style sheet, rather than within the example tex file. --MM +\ifaclfinal +\else + \addtolength\titlebox{.25in} +\fi +% Mostly taken from deproc. +\def\maketitle{\par + \begingroup + \def\thefootnote{\fnsymbol{footnote}} + \def\@makefnmark{\hbox to 0pt{$^{\@thefnmark}$\hss}} + \twocolumn[\@maketitle] \@thanks + \endgroup + \setcounter{footnote}{0} + \let\maketitle\relax \let\@maketitle\relax + \gdef\@thanks{}\gdef\@author{}\gdef\@title{}\let\thanks\relax} +\def\@maketitle{\vbox to \titlebox{\hsize\textwidth + \linewidth\hsize \vskip 0.125in minus 0.125in \centering + {\Large\bf \@title \par} \vskip 0.2in plus 1fil minus 0.1in + {\def\and{\unskip\enspace{\rm and}\enspace}% + \def\And{\end{tabular}\hss \egroup \hskip 1in plus 2fil + \hbox to 0pt\bgroup\hss \begin{tabular}[t]{c}\bf}% + \def\AND{\end{tabular}\hss\egroup \hfil\hfil\egroup + \vskip 0.25in plus 1fil minus 0.125in + \hbox to \linewidth\bgroup\large \hfil\hfil + \hbox to 0pt\bgroup\hss \begin{tabular}[t]{c}\bf} + \hbox to \linewidth\bgroup\large \hfil\hfil + \hbox to 0pt\bgroup\hss + \outauthor + \hss\egroup + \hfil\hfil\egroup} + \vskip 0.3in plus 2fil minus 0.1in +}} + +% margins and font size for abstract +\renewenvironment{abstract}% + {\centerline{\large\bf Abstract}% + \begin{list}{}% + {\setlength{\rightmargin}{0.6cm}% + \setlength{\leftmargin}{0.6cm}}% + \item[]\ignorespaces% + \@setsize\normalsize{12pt}\xpt\@xpt + }% + {\unskip\end{list}} + +%\renewenvironment{abstract}{\centerline{\large\bf +% Abstract}\vspace{0.5ex}\begin{quote}}{\par\end{quote}\vskip 1ex} + +% Resizing figure and table captions - SL +\newcommand{\figcapfont}{\rm} +\newcommand{\tabcapfont}{\rm} +\renewcommand{\fnum@figure}{\figcapfont Figure \thefigure} +\renewcommand{\fnum@table}{\tabcapfont Table \thetable} +\renewcommand{\figcapfont}{\@setsize\normalsize{12pt}\xpt\@xpt} +\renewcommand{\tabcapfont}{\@setsize\normalsize{12pt}\xpt\@xpt} +% Support for interacting with the caption, subfigure, and subcaption packages - SL +\usepackage{caption} +\DeclareCaptionFont{10pt}{\fontsize{10pt}{12pt}\selectfont} +\captionsetup{font=10pt} + +\RequirePackage{natbib} +% for citation commands in the .tex, authors can use: +% \citep, \citet, and \citeyearpar for compatibility with natbib, or +% \cite, \newcite, and \shortcite for compatibility with older ACL .sty files +\renewcommand\cite{\citep} % to get "(Author Year)" with natbib +\newcommand\shortcite{\citeyearpar}% to get "(Year)" with natbib +\newcommand\newcite{\citet} % to get "Author (Year)" with natbib + +% DK/IV: Workaround for annoying hyperref pagewrap bug +% \RequirePackage{etoolbox} +% \patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{\errmessage{\noexpand patch failed}} + +% bibliography + +\def\@up#1{\raise.2ex\hbox{#1}} + +% Don't put a label in the bibliography at all. Just use the unlabeled format +% instead. +\def\thebibliography#1{\vskip\parskip% +\vskip\baselineskip% +\def\baselinestretch{1}% +\ifx\@currsize\normalsize\@normalsize\else\@currsize\fi% +\vskip-\parskip% +\vskip-\baselineskip% +\section*{References\@mkboth + {References}{References}}\list + {}{\setlength{\labelwidth}{0pt}\setlength{\leftmargin}{\parindent} + \setlength{\itemindent}{-\parindent}} + \def\newblock{\hskip .11em plus .33em minus -.07em} + \sloppy\clubpenalty4000\widowpenalty4000 + \sfcode`\.=1000\relax} +\let\endthebibliography=\endlist + + +% Allow for a bibliography of sources of attested examples +\def\thesourcebibliography#1{\vskip\parskip% +\vskip\baselineskip% +\def\baselinestretch{1}% +\ifx\@currsize\normalsize\@normalsize\else\@currsize\fi% +\vskip-\parskip% +\vskip-\baselineskip% +\section*{Sources of Attested Examples\@mkboth + {Sources of Attested Examples}{Sources of Attested Examples}}\list + {}{\setlength{\labelwidth}{0pt}\setlength{\leftmargin}{\parindent} + \setlength{\itemindent}{-\parindent}} + \def\newblock{\hskip .11em plus .33em minus -.07em} + \sloppy\clubpenalty4000\widowpenalty4000 + \sfcode`\.=1000\relax} +\let\endthesourcebibliography=\endlist + +% sections with less space +\def\section{\@startsection {section}{1}{\z@}{-2.0ex plus + -0.5ex minus -.2ex}{1.5ex plus 0.3ex minus .2ex}{\large\bf\raggedright}} +\def\subsection{\@startsection{subsection}{2}{\z@}{-1.8ex plus + -0.5ex minus -.2ex}{0.8ex plus .2ex}{\normalsize\bf\raggedright}} +%% changed by KO to - values to get teh initial parindent right +\def\subsubsection{\@startsection{subsubsection}{3}{\z@}{-1.5ex plus + -0.5ex minus -.2ex}{0.5ex plus .2ex}{\normalsize\bf\raggedright}} +\def\paragraph{\@startsection{paragraph}{4}{\z@}{1.5ex plus + 0.5ex minus .2ex}{-1em}{\normalsize\bf}} +\def\subparagraph{\@startsection{subparagraph}{5}{\parindent}{1.5ex plus + 0.5ex minus .2ex}{-1em}{\normalsize\bf}} + +% Footnotes +\footnotesep 6.65pt % +\skip\footins 9pt plus 4pt minus 2pt +\def\footnoterule{\kern-3pt \hrule width 5pc \kern 2.6pt } +\setcounter{footnote}{0} + +% Lists and paragraphs +\parindent 1em +\topsep 4pt plus 1pt minus 2pt +\partopsep 1pt plus 0.5pt minus 0.5pt +\itemsep 2pt plus 1pt minus 0.5pt +\parsep 2pt plus 1pt minus 0.5pt + +\leftmargin 2em \leftmargini\leftmargin \leftmarginii 2em +\leftmarginiii 1.5em \leftmarginiv 1.0em \leftmarginv .5em \leftmarginvi .5em +\labelwidth\leftmargini\advance\labelwidth-\labelsep \labelsep 5pt + +\def\@listi{\leftmargin\leftmargini} +\def\@listii{\leftmargin\leftmarginii + \labelwidth\leftmarginii\advance\labelwidth-\labelsep + \topsep 2pt plus 1pt minus 0.5pt + \parsep 1pt plus 0.5pt minus 0.5pt + \itemsep \parsep} +\def\@listiii{\leftmargin\leftmarginiii + \labelwidth\leftmarginiii\advance\labelwidth-\labelsep + \topsep 1pt plus 0.5pt minus 0.5pt + \parsep \z@ \partopsep 0.5pt plus 0pt minus 0.5pt + \itemsep \topsep} +\def\@listiv{\leftmargin\leftmarginiv + \labelwidth\leftmarginiv\advance\labelwidth-\labelsep} +\def\@listv{\leftmargin\leftmarginv + \labelwidth\leftmarginv\advance\labelwidth-\labelsep} +\def\@listvi{\leftmargin\leftmarginvi + \labelwidth\leftmarginvi\advance\labelwidth-\labelsep} + +\abovedisplayskip 7pt plus2pt minus5pt% +\belowdisplayskip \abovedisplayskip +\abovedisplayshortskip 0pt plus3pt% +\belowdisplayshortskip 4pt plus3pt minus3pt% + +% Less leading in most fonts (due to the narrow columns) +% The choices were between 1-pt and 1.5-pt leading +\def\@normalsize{\@setsize\normalsize{11pt}\xpt\@xpt} +\def\small{\@setsize\small{10pt}\ixpt\@ixpt} +\def\footnotesize{\@setsize\footnotesize{10pt}\ixpt\@ixpt} +\def\scriptsize{\@setsize\scriptsize{8pt}\viipt\@viipt} +\def\tiny{\@setsize\tiny{7pt}\vipt\@vipt} +\def\large{\@setsize\large{14pt}\xiipt\@xiipt} +\def\Large{\@setsize\Large{16pt}\xivpt\@xivpt} +\def\LARGE{\@setsize\LARGE{20pt}\xviipt\@xviipt} +\def\huge{\@setsize\huge{23pt}\xxpt\@xxpt} +\def\Huge{\@setsize\Huge{28pt}\xxvpt\@xxvpt} diff --git a/references/2019.arxiv.conneau/source/XLMR Paper/acl_natbib.bst b/references/2019.arxiv.conneau/source/XLMR Paper/acl_natbib.bst new file mode 100644 index 0000000000000000000000000000000000000000..821195d8bbb77f882afb308a31e5f9da81720f6b --- /dev/null +++ b/references/2019.arxiv.conneau/source/XLMR Paper/acl_natbib.bst @@ -0,0 +1,1975 @@ +%%% acl_natbib.bst +%%% Modification of BibTeX style file acl_natbib_nourl.bst +%%% ... by urlbst, version 0.7 (marked with "% urlbst") +%%% See +%%% Added webpage entry type, and url and lastchecked fields. +%%% Added eprint support. +%%% Added DOI support. +%%% Added PUBMED support. +%%% Added hyperref support. +%%% Original headers follow... + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% BibTeX style file acl_natbib_nourl.bst +% +% intended as input to urlbst script +% $ ./urlbst --hyperref --inlinelinks acl_natbib_nourl.bst > acl_natbib.bst +% +% adapted from compling.bst +% in order to mimic the style files for ACL conferences prior to 2017 +% by making the following three changes: +% - for @incollection, page numbers now follow volume title. +% - for @inproceedings, address now follows conference name. +% (address is intended as location of conference, +% not address of publisher.) +% - for papers with three authors, use et al. in citation +% Dan Gildea 2017/06/08 +% - fixed a bug with format.chapter - error given if chapter is empty +% with inbook. +% Shay Cohen 2018/02/16 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% BibTeX style file compling.bst +% +% Intended for the journal Computational Linguistics (ACL/MIT Press) +% Created by Ron Artstein on 2005/08/22 +% For use with for author-year citations. +% +% I created this file in order to allow submissions to the journal +% Computational Linguistics using the package for author-year +% citations, which offers a lot more flexibility than , CL's +% official citation package. This file adheres strictly to the official +% style guide available from the MIT Press: +% +% http://mitpress.mit.edu/journals/coli/compling_style.pdf +% +% This includes all the various quirks of the style guide, for example: +% - a chapter from a monograph (@inbook) has no page numbers. +% - an article from an edited volume (@incollection) has page numbers +% after the publisher and address. +% - an article from a proceedings volume (@inproceedings) has page +% numbers before the publisher and address. +% +% Where the style guide was inconsistent or not specific enough I +% looked at actual published articles and exercised my own judgment. +% I noticed two inconsistencies in the style guide: +% +% - The style guide gives one example of an article from an edited +% volume with the editor's name spelled out in full, and another +% with the editors' names abbreviated. I chose to accept the first +% one as correct, since the style guide generally shuns abbreviations, +% and editors' names are also spelled out in some recently published +% articles. +% +% - The style guide gives one example of a reference where the word +% "and" between two authors is preceded by a comma. This is most +% likely a typo, since in all other cases with just two authors or +% editors there is no comma before the word "and". +% +% One case where the style guide is not being specific is the placement +% of the edition number, for which no example is given. I chose to put +% it immediately after the title, which I (subjectively) find natural, +% and is also the place of the edition in a few recently published +% articles. +% +% This file correctly reproduces all of the examples in the official +% style guide, except for the two inconsistencies noted above. I even +% managed to get it to correctly format the proceedings example which +% has an organization, a publisher, and two addresses (the conference +% location and the publisher's address), though I cheated a bit by +% putting the conference location and month as part of the title field; +% I feel that in this case the conference location and month can be +% considered as part of the title, and that adding a location field +% is not justified. Note also that a location field is not standard, +% so entries made with this field would not port nicely to other styles. +% However, if authors feel that there's a need for a location field +% then tell me and I'll see what I can do. +% +% The file also produces to my satisfaction all the bibliographical +% entries in my recent (joint) submission to CL (this was the original +% motivation for creating the file). I also tested it by running it +% on a larger set of entries and eyeballing the results. There may of +% course still be errors, especially with combinations of fields that +% are not that common, or with cross-references (which I seldom use). +% If you find such errors please write to me. +% +% I hope people find this file useful. Please email me with comments +% and suggestions. +% +% Ron Artstein +% artstein [at] essex.ac.uk +% August 22, 2005. +% +% Some technical notes. +% +% This file is based on a file generated with the package +% by Patrick W. Daly (see selected options below), which was then +% manually customized to conform with certain CL requirements which +% cannot be met by . Departures from the generated file +% include: +% +% Function inbook: moved publisher and address to the end; moved +% edition after title; replaced function format.chapter.pages by +% new function format.chapter to output chapter without pages. +% +% Function inproceedings: moved publisher and address to the end; +% replaced function format.in.ed.booktitle by new function +% format.in.booktitle to output the proceedings title without +% the editor. +% +% Functions book, incollection, manual: moved edition after title. +% +% Function mastersthesis: formatted title as for articles (unlike +% phdthesis which is formatted as book) and added month. +% +% Function proceedings: added new.sentence between organization and +% publisher when both are present. +% +% Function format.lab.names: modified so that it gives all the +% authors' surnames for in-text citations for one, two and three +% authors and only uses "et. al" for works with four authors or more +% (thanks to Ken Shan for convincing me to go through the trouble of +% modifying this function rather than using unreliable hacks). +% +% Changes: +% +% 2006-10-27: Changed function reverse.pass so that the extra label is +% enclosed in parentheses when the year field ends in an uppercase or +% lowercase letter (change modeled after Uli Sauerland's modification +% of nals.bst). RA. +% +% +% The preamble of the generated file begins below: +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% +%% This is file `compling.bst', +%% generated with the docstrip utility. +%% +%% The original source files were: +%% +%% merlin.mbs (with options: `ay,nat,vonx,nm-revv1,jnrlst,keyxyr,blkyear,dt-beg,yr-per,note-yr,num-xser,pre-pub,xedn,nfss') +%% ---------------------------------------- +%% *** Intended for the journal Computational Linguistics *** +%% +%% Copyright 1994-2002 Patrick W Daly + % =============================================================== + % IMPORTANT NOTICE: + % This bibliographic style (bst) file has been generated from one or + % more master bibliographic style (mbs) files, listed above. + % + % This generated file can be redistributed and/or modified under the terms + % of the LaTeX Project Public License Distributed from CTAN + % archives in directory macros/latex/base/lppl.txt; either + % version 1 of the License, or any later version. + % =============================================================== + % Name and version information of the main mbs file: + % \ProvidesFile{merlin.mbs}[2002/10/21 4.05 (PWD, AO, DPC)] + % For use with BibTeX version 0.99a or later + %------------------------------------------------------------------- + % This bibliography style file is intended for texts in ENGLISH + % This is an author-year citation style bibliography. As such, it is + % non-standard LaTeX, and requires a special package file to function properly. + % Such a package is natbib.sty by Patrick W. Daly + % The form of the \bibitem entries is + % \bibitem[Jones et al.(1990)]{key}... + % \bibitem[Jones et al.(1990)Jones, Baker, and Smith]{key}... + % The essential feature is that the label (the part in brackets) consists + % of the author names, as they should appear in the citation, with the year + % in parentheses following. There must be no space before the opening + % parenthesis! + % With natbib v5.3, a full list of authors may also follow the year. + % In natbib.sty, it is possible to define the type of enclosures that is + % really wanted (brackets or parentheses), but in either case, there must + % be parentheses in the label. + % The \cite command functions as follows: + % \citet{key} ==>> Jones et al. (1990) + % \citet*{key} ==>> Jones, Baker, and Smith (1990) + % \citep{key} ==>> (Jones et al., 1990) + % \citep*{key} ==>> (Jones, Baker, and Smith, 1990) + % \citep[chap. 2]{key} ==>> (Jones et al., 1990, chap. 2) + % \citep[e.g.][]{key} ==>> (e.g. Jones et al., 1990) + % \citep[e.g.][p. 32]{key} ==>> (e.g. Jones et al., p. 32) + % \citeauthor{key} ==>> Jones et al. + % \citeauthor*{key} ==>> Jones, Baker, and Smith + % \citeyear{key} ==>> 1990 + %--------------------------------------------------------------------- + +ENTRY + { address + author + booktitle + chapter + edition + editor + howpublished + institution + journal + key + month + note + number + organization + pages + publisher + school + series + title + type + volume + year + eprint % urlbst + doi % urlbst + pubmed % urlbst + url % urlbst + lastchecked % urlbst + } + {} + { label extra.label sort.label short.list } +INTEGERS { output.state before.all mid.sentence after.sentence after.block } +% urlbst... +% urlbst constants and state variables +STRINGS { urlintro + eprinturl eprintprefix doiprefix doiurl pubmedprefix pubmedurl + citedstring onlinestring linktextstring + openinlinelink closeinlinelink } +INTEGERS { hrefform inlinelinks makeinlinelink + addeprints adddoiresolver addpubmedresolver } +FUNCTION {init.urlbst.variables} +{ + % The following constants may be adjusted by hand, if desired + + % The first set allow you to enable or disable certain functionality. + #1 'addeprints := % 0=no eprints; 1=include eprints + #1 'adddoiresolver := % 0=no DOI resolver; 1=include it + #1 'addpubmedresolver := % 0=no PUBMED resolver; 1=include it + #2 'hrefform := % 0=no crossrefs; 1=hypertex xrefs; 2=hyperref refs + #1 'inlinelinks := % 0=URLs explicit; 1=URLs attached to titles + + % String constants, which you _might_ want to tweak. + "URL: " 'urlintro := % prefix before URL; typically "Available from:" or "URL": + "online" 'onlinestring := % indication that resource is online; typically "online" + "cited " 'citedstring := % indicator of citation date; typically "cited " + "[link]" 'linktextstring := % dummy link text; typically "[link]" + "http://arxiv.org/abs/" 'eprinturl := % prefix to make URL from eprint ref + "arXiv:" 'eprintprefix := % text prefix printed before eprint ref; typically "arXiv:" + "https://doi.org/" 'doiurl := % prefix to make URL from DOI + "doi:" 'doiprefix := % text prefix printed before DOI ref; typically "doi:" + "http://www.ncbi.nlm.nih.gov/pubmed/" 'pubmedurl := % prefix to make URL from PUBMED + "PMID:" 'pubmedprefix := % text prefix printed before PUBMED ref; typically "PMID:" + + % The following are internal state variables, not configuration constants, + % so they shouldn't be fiddled with. + #0 'makeinlinelink := % state variable managed by possibly.setup.inlinelink + "" 'openinlinelink := % ditto + "" 'closeinlinelink := % ditto +} +INTEGERS { + bracket.state + outside.brackets + open.brackets + within.brackets + close.brackets +} +% ...urlbst to here +FUNCTION {init.state.consts} +{ #0 'outside.brackets := % urlbst... + #1 'open.brackets := + #2 'within.brackets := + #3 'close.brackets := % ...urlbst to here + + #0 'before.all := + #1 'mid.sentence := + #2 'after.sentence := + #3 'after.block := +} +STRINGS { s t} +% urlbst +FUNCTION {output.nonnull.original} +{ 's := + output.state mid.sentence = + { ", " * write$ } + { output.state after.block = + { add.period$ write$ + newline$ + "\newblock " write$ + } + { output.state before.all = + 'write$ + { add.period$ " " * write$ } + if$ + } + if$ + mid.sentence 'output.state := + } + if$ + s +} + +% urlbst... +% The following three functions are for handling inlinelink. They wrap +% a block of text which is potentially output with write$ by multiple +% other functions, so we don't know the content a priori. +% They communicate between each other using the variables makeinlinelink +% (which is true if a link should be made), and closeinlinelink (which holds +% the string which should close any current link. They can be called +% at any time, but start.inlinelink will be a no-op unless something has +% previously set makeinlinelink true, and the two ...end.inlinelink functions +% will only do their stuff if start.inlinelink has previously set +% closeinlinelink to be non-empty. +% (thanks to 'ijvm' for suggested code here) +FUNCTION {uand} +{ 'skip$ { pop$ #0 } if$ } % 'and' (which isn't defined at this point in the file) +FUNCTION {possibly.setup.inlinelink} +{ makeinlinelink hrefform #0 > uand + { doi empty$ adddoiresolver uand + { pubmed empty$ addpubmedresolver uand + { eprint empty$ addeprints uand + { url empty$ + { "" } + { url } + if$ } + { eprinturl eprint * } + if$ } + { pubmedurl pubmed * } + if$ } + { doiurl doi * } + if$ + % an appropriately-formatted URL is now on the stack + hrefform #1 = % hypertex + { "\special {html: }{" * 'openinlinelink := + "\special {html:}" 'closeinlinelink := } + { "\href {" swap$ * "} {" * 'openinlinelink := % hrefform=#2 -- hyperref + % the space between "} {" matters: a URL of just the right length can cause "\% newline em" + "}" 'closeinlinelink := } + if$ + #0 'makeinlinelink := + } + 'skip$ + if$ % makeinlinelink +} +FUNCTION {add.inlinelink} +{ openinlinelink empty$ + 'skip$ + { openinlinelink swap$ * closeinlinelink * + "" 'openinlinelink := + } + if$ +} +FUNCTION {output.nonnull} +{ % Save the thing we've been asked to output + 's := + % If the bracket-state is close.brackets, then add a close-bracket to + % what is currently at the top of the stack, and set bracket.state + % to outside.brackets + bracket.state close.brackets = + { "]" * + outside.brackets 'bracket.state := + } + 'skip$ + if$ + bracket.state outside.brackets = + { % We're outside all brackets -- this is the normal situation. + % Write out what's currently at the top of the stack, using the + % original output.nonnull function. + s + add.inlinelink + output.nonnull.original % invoke the original output.nonnull + } + { % Still in brackets. Add open-bracket or (continuation) comma, add the + % new text (in s) to the top of the stack, and move to the close-brackets + % state, ready for next time (unless inbrackets resets it). If we come + % into this branch, then output.state is carefully undisturbed. + bracket.state open.brackets = + { " [" * } + { ", " * } % bracket.state will be within.brackets + if$ + s * + close.brackets 'bracket.state := + } + if$ +} + +% Call this function just before adding something which should be presented in +% brackets. bracket.state is handled specially within output.nonnull. +FUNCTION {inbrackets} +{ bracket.state close.brackets = + { within.brackets 'bracket.state := } % reset the state: not open nor closed + { open.brackets 'bracket.state := } + if$ +} + +FUNCTION {format.lastchecked} +{ lastchecked empty$ + { "" } + { inbrackets citedstring lastchecked * } + if$ +} +% ...urlbst to here +FUNCTION {output} +{ duplicate$ empty$ + 'pop$ + 'output.nonnull + if$ +} +FUNCTION {output.check} +{ 't := + duplicate$ empty$ + { pop$ "empty " t * " in " * cite$ * warning$ } + 'output.nonnull + if$ +} +FUNCTION {fin.entry.original} % urlbst (renamed from fin.entry, so it can be wrapped below) +{ add.period$ + write$ + newline$ +} + +FUNCTION {new.block} +{ output.state before.all = + 'skip$ + { after.block 'output.state := } + if$ +} +FUNCTION {new.sentence} +{ output.state after.block = + 'skip$ + { output.state before.all = + 'skip$ + { after.sentence 'output.state := } + if$ + } + if$ +} +FUNCTION {add.blank} +{ " " * before.all 'output.state := +} + +FUNCTION {date.block} +{ + new.block +} + +FUNCTION {not} +{ { #0 } + { #1 } + if$ +} +FUNCTION {and} +{ 'skip$ + { pop$ #0 } + if$ +} +FUNCTION {or} +{ { pop$ #1 } + 'skip$ + if$ +} +FUNCTION {new.block.checkb} +{ empty$ + swap$ empty$ + and + 'skip$ + 'new.block + if$ +} +FUNCTION {field.or.null} +{ duplicate$ empty$ + { pop$ "" } + 'skip$ + if$ +} +FUNCTION {emphasize} +{ duplicate$ empty$ + { pop$ "" } + { "\emph{" swap$ * "}" * } + if$ +} +FUNCTION {tie.or.space.prefix} +{ duplicate$ text.length$ #3 < + { "~" } + { " " } + if$ + swap$ +} + +FUNCTION {capitalize} +{ "u" change.case$ "t" change.case$ } + +FUNCTION {space.word} +{ " " swap$ * " " * } + % Here are the language-specific definitions for explicit words. + % Each function has a name bbl.xxx where xxx is the English word. + % The language selected here is ENGLISH +FUNCTION {bbl.and} +{ "and"} + +FUNCTION {bbl.etal} +{ "et~al." } + +FUNCTION {bbl.editors} +{ "editors" } + +FUNCTION {bbl.editor} +{ "editor" } + +FUNCTION {bbl.edby} +{ "edited by" } + +FUNCTION {bbl.edition} +{ "edition" } + +FUNCTION {bbl.volume} +{ "volume" } + +FUNCTION {bbl.of} +{ "of" } + +FUNCTION {bbl.number} +{ "number" } + +FUNCTION {bbl.nr} +{ "no." } + +FUNCTION {bbl.in} +{ "in" } + +FUNCTION {bbl.pages} +{ "pages" } + +FUNCTION {bbl.page} +{ "page" } + +FUNCTION {bbl.chapter} +{ "chapter" } + +FUNCTION {bbl.techrep} +{ "Technical Report" } + +FUNCTION {bbl.mthesis} +{ "Master's thesis" } + +FUNCTION {bbl.phdthesis} +{ "Ph.D. thesis" } + +MACRO {jan} {"January"} + +MACRO {feb} {"February"} + +MACRO {mar} {"March"} + +MACRO {apr} {"April"} + +MACRO {may} {"May"} + +MACRO {jun} {"June"} + +MACRO {jul} {"July"} + +MACRO {aug} {"August"} + +MACRO {sep} {"September"} + +MACRO {oct} {"October"} + +MACRO {nov} {"November"} + +MACRO {dec} {"December"} + +MACRO {acmcs} {"ACM Computing Surveys"} + +MACRO {acta} {"Acta Informatica"} + +MACRO {cacm} {"Communications of the ACM"} + +MACRO {ibmjrd} {"IBM Journal of Research and Development"} + +MACRO {ibmsj} {"IBM Systems Journal"} + +MACRO {ieeese} {"IEEE Transactions on Software Engineering"} + +MACRO {ieeetc} {"IEEE Transactions on Computers"} + +MACRO {ieeetcad} + {"IEEE Transactions on Computer-Aided Design of Integrated Circuits"} + +MACRO {ipl} {"Information Processing Letters"} + +MACRO {jacm} {"Journal of the ACM"} + +MACRO {jcss} {"Journal of Computer and System Sciences"} + +MACRO {scp} {"Science of Computer Programming"} + +MACRO {sicomp} {"SIAM Journal on Computing"} + +MACRO {tocs} {"ACM Transactions on Computer Systems"} + +MACRO {tods} {"ACM Transactions on Database Systems"} + +MACRO {tog} {"ACM Transactions on Graphics"} + +MACRO {toms} {"ACM Transactions on Mathematical Software"} + +MACRO {toois} {"ACM Transactions on Office Information Systems"} + +MACRO {toplas} {"ACM Transactions on Programming Languages and Systems"} + +MACRO {tcs} {"Theoretical Computer Science"} +FUNCTION {bibinfo.check} +{ swap$ + duplicate$ missing$ + { + pop$ pop$ + "" + } + { duplicate$ empty$ + { + swap$ pop$ + } + { swap$ + pop$ + } + if$ + } + if$ +} +FUNCTION {bibinfo.warn} +{ swap$ + duplicate$ missing$ + { + swap$ "missing " swap$ * " in " * cite$ * warning$ pop$ + "" + } + { duplicate$ empty$ + { + swap$ "empty " swap$ * " in " * cite$ * warning$ + } + { swap$ + pop$ + } + if$ + } + if$ +} +STRINGS { bibinfo} +INTEGERS { nameptr namesleft numnames } + +FUNCTION {format.names} +{ 'bibinfo := + duplicate$ empty$ 'skip$ { + 's := + "" 't := + #1 'nameptr := + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + duplicate$ #1 > + { "{ff~}{vv~}{ll}{, jj}" } + { "{ff~}{vv~}{ll}{, jj}" } % first name first for first author +% { "{vv~}{ll}{, ff}{, jj}" } % last name first for first author + if$ + format.name$ + bibinfo bibinfo.check + 't := + nameptr #1 > + { + namesleft #1 > + { ", " * t * } + { + numnames #2 > + { "," * } + 'skip$ + if$ + s nameptr "{ll}" format.name$ duplicate$ "others" = + { 't := } + { pop$ } + if$ + t "others" = + { + " " * bbl.etal * + } + { + bbl.and + space.word * t * + } + if$ + } + if$ + } + 't + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ + } if$ +} +FUNCTION {format.names.ed} +{ + 'bibinfo := + duplicate$ empty$ 'skip$ { + 's := + "" 't := + #1 'nameptr := + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + "{ff~}{vv~}{ll}{, jj}" + format.name$ + bibinfo bibinfo.check + 't := + nameptr #1 > + { + namesleft #1 > + { ", " * t * } + { + numnames #2 > + { "," * } + 'skip$ + if$ + s nameptr "{ll}" format.name$ duplicate$ "others" = + { 't := } + { pop$ } + if$ + t "others" = + { + + " " * bbl.etal * + } + { + bbl.and + space.word * t * + } + if$ + } + if$ + } + 't + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ + } if$ +} +FUNCTION {format.key} +{ empty$ + { key field.or.null } + { "" } + if$ +} + +FUNCTION {format.authors} +{ author "author" format.names +} +FUNCTION {get.bbl.editor} +{ editor num.names$ #1 > 'bbl.editors 'bbl.editor if$ } + +FUNCTION {format.editors} +{ editor "editor" format.names duplicate$ empty$ 'skip$ + { + "," * + " " * + get.bbl.editor + * + } + if$ +} +FUNCTION {format.note} +{ + note empty$ + { "" } + { note #1 #1 substring$ + duplicate$ "{" = + 'skip$ + { output.state mid.sentence = + { "l" } + { "u" } + if$ + change.case$ + } + if$ + note #2 global.max$ substring$ * "note" bibinfo.check + } + if$ +} + +FUNCTION {format.title} +{ title + duplicate$ empty$ 'skip$ + { "t" change.case$ } + if$ + "title" bibinfo.check +} +FUNCTION {format.full.names} +{'s := + "" 't := + #1 'nameptr := + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + "{vv~}{ll}" format.name$ + 't := + nameptr #1 > + { + namesleft #1 > + { ", " * t * } + { + s nameptr "{ll}" format.name$ duplicate$ "others" = + { 't := } + { pop$ } + if$ + t "others" = + { + " " * bbl.etal * + } + { + numnames #2 > + { "," * } + 'skip$ + if$ + bbl.and + space.word * t * + } + if$ + } + if$ + } + 't + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ +} + +FUNCTION {author.editor.key.full} +{ author empty$ + { editor empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { editor format.full.names } + if$ + } + { author format.full.names } + if$ +} + +FUNCTION {author.key.full} +{ author empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { author format.full.names } + if$ +} + +FUNCTION {editor.key.full} +{ editor empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { editor format.full.names } + if$ +} + +FUNCTION {make.full.names} +{ type$ "book" = + type$ "inbook" = + or + 'author.editor.key.full + { type$ "proceedings" = + 'editor.key.full + 'author.key.full + if$ + } + if$ +} + +FUNCTION {output.bibitem.original} % urlbst (renamed from output.bibitem, so it can be wrapped below) +{ newline$ + "\bibitem[{" write$ + label write$ + ")" make.full.names duplicate$ short.list = + { pop$ } + { * } + if$ + "}]{" * write$ + cite$ write$ + "}" write$ + newline$ + "" + before.all 'output.state := +} + +FUNCTION {n.dashify} +{ + 't := + "" + { t empty$ not } + { t #1 #1 substring$ "-" = + { t #1 #2 substring$ "--" = not + { "--" * + t #2 global.max$ substring$ 't := + } + { { t #1 #1 substring$ "-" = } + { "-" * + t #2 global.max$ substring$ 't := + } + while$ + } + if$ + } + { t #1 #1 substring$ * + t #2 global.max$ substring$ 't := + } + if$ + } + while$ +} + +FUNCTION {word.in} +{ bbl.in capitalize + " " * } + +FUNCTION {format.date} +{ year "year" bibinfo.check duplicate$ empty$ + { + } + 'skip$ + if$ + extra.label * + before.all 'output.state := + after.sentence 'output.state := +} +FUNCTION {format.btitle} +{ title "title" bibinfo.check + duplicate$ empty$ 'skip$ + { + emphasize + } + if$ +} +FUNCTION {either.or.check} +{ empty$ + 'pop$ + { "can't use both " swap$ * " fields in " * cite$ * warning$ } + if$ +} +FUNCTION {format.bvolume} +{ volume empty$ + { "" } + { bbl.volume volume tie.or.space.prefix + "volume" bibinfo.check * * + series "series" bibinfo.check + duplicate$ empty$ 'pop$ + { swap$ bbl.of space.word * swap$ + emphasize * } + if$ + "volume and number" number either.or.check + } + if$ +} +FUNCTION {format.number.series} +{ volume empty$ + { number empty$ + { series field.or.null } + { series empty$ + { number "number" bibinfo.check } + { output.state mid.sentence = + { bbl.number } + { bbl.number capitalize } + if$ + number tie.or.space.prefix "number" bibinfo.check * * + bbl.in space.word * + series "series" bibinfo.check * + } + if$ + } + if$ + } + { "" } + if$ +} + +FUNCTION {format.edition} +{ edition duplicate$ empty$ 'skip$ + { + output.state mid.sentence = + { "l" } + { "t" } + if$ change.case$ + "edition" bibinfo.check + " " * bbl.edition * + } + if$ +} +INTEGERS { multiresult } +FUNCTION {multi.page.check} +{ 't := + #0 'multiresult := + { multiresult not + t empty$ not + and + } + { t #1 #1 substring$ + duplicate$ "-" = + swap$ duplicate$ "," = + swap$ "+" = + or or + { #1 'multiresult := } + { t #2 global.max$ substring$ 't := } + if$ + } + while$ + multiresult +} +FUNCTION {format.pages} +{ pages duplicate$ empty$ 'skip$ + { duplicate$ multi.page.check + { + bbl.pages swap$ + n.dashify + } + { + bbl.page swap$ + } + if$ + tie.or.space.prefix + "pages" bibinfo.check + * * + } + if$ +} +FUNCTION {format.journal.pages} +{ pages duplicate$ empty$ 'pop$ + { swap$ duplicate$ empty$ + { pop$ pop$ format.pages } + { + ":" * + swap$ + n.dashify + "pages" bibinfo.check + * + } + if$ + } + if$ +} +FUNCTION {format.vol.num.pages} +{ volume field.or.null + duplicate$ empty$ 'skip$ + { + "volume" bibinfo.check + } + if$ + number "number" bibinfo.check duplicate$ empty$ 'skip$ + { + swap$ duplicate$ empty$ + { "there's a number but no volume in " cite$ * warning$ } + 'skip$ + if$ + swap$ + "(" swap$ * ")" * + } + if$ * + format.journal.pages +} + +FUNCTION {format.chapter} +{ chapter empty$ + 'format.pages + { type empty$ + { bbl.chapter } + { type "l" change.case$ + "type" bibinfo.check + } + if$ + chapter tie.or.space.prefix + "chapter" bibinfo.check + * * + } + if$ +} + +FUNCTION {format.chapter.pages} +{ chapter empty$ + 'format.pages + { type empty$ + { bbl.chapter } + { type "l" change.case$ + "type" bibinfo.check + } + if$ + chapter tie.or.space.prefix + "chapter" bibinfo.check + * * + pages empty$ + 'skip$ + { ", " * format.pages * } + if$ + } + if$ +} + +FUNCTION {format.booktitle} +{ + booktitle "booktitle" bibinfo.check + emphasize +} +FUNCTION {format.in.booktitle} +{ format.booktitle duplicate$ empty$ 'skip$ + { + word.in swap$ * + } + if$ +} +FUNCTION {format.in.ed.booktitle} +{ format.booktitle duplicate$ empty$ 'skip$ + { + editor "editor" format.names.ed duplicate$ empty$ 'pop$ + { + "," * + " " * + get.bbl.editor + ", " * + * swap$ + * } + if$ + word.in swap$ * + } + if$ +} +FUNCTION {format.thesis.type} +{ type duplicate$ empty$ + 'pop$ + { swap$ pop$ + "t" change.case$ "type" bibinfo.check + } + if$ +} +FUNCTION {format.tr.number} +{ number "number" bibinfo.check + type duplicate$ empty$ + { pop$ bbl.techrep } + 'skip$ + if$ + "type" bibinfo.check + swap$ duplicate$ empty$ + { pop$ "t" change.case$ } + { tie.or.space.prefix * * } + if$ +} +FUNCTION {format.article.crossref} +{ + word.in + " \cite{" * crossref * "}" * +} +FUNCTION {format.book.crossref} +{ volume duplicate$ empty$ + { "empty volume in " cite$ * "'s crossref of " * crossref * warning$ + pop$ word.in + } + { bbl.volume + capitalize + swap$ tie.or.space.prefix "volume" bibinfo.check * * bbl.of space.word * + } + if$ + " \cite{" * crossref * "}" * +} +FUNCTION {format.incoll.inproc.crossref} +{ + word.in + " \cite{" * crossref * "}" * +} +FUNCTION {format.org.or.pub} +{ 't := + "" + address empty$ t empty$ and + 'skip$ + { + t empty$ + { address "address" bibinfo.check * + } + { t * + address empty$ + 'skip$ + { ", " * address "address" bibinfo.check * } + if$ + } + if$ + } + if$ +} +FUNCTION {format.publisher.address} +{ publisher "publisher" bibinfo.warn format.org.or.pub +} + +FUNCTION {format.organization.address} +{ organization "organization" bibinfo.check format.org.or.pub +} + +% urlbst... +% Functions for making hypertext links. +% In all cases, the stack has (link-text href-url) +% +% make 'null' specials +FUNCTION {make.href.null} +{ + pop$ +} +% make hypertex specials +FUNCTION {make.href.hypertex} +{ + "\special {html: }" * swap$ * + "\special {html:}" * +} +% make hyperref specials +FUNCTION {make.href.hyperref} +{ + "\href {" swap$ * "} {\path{" * swap$ * "}}" * +} +FUNCTION {make.href} +{ hrefform #2 = + 'make.href.hyperref % hrefform = 2 + { hrefform #1 = + 'make.href.hypertex % hrefform = 1 + 'make.href.null % hrefform = 0 (or anything else) + if$ + } + if$ +} + +% If inlinelinks is true, then format.url should be a no-op, since it's +% (a) redundant, and (b) could end up as a link-within-a-link. +FUNCTION {format.url} +{ inlinelinks #1 = url empty$ or + { "" } + { hrefform #1 = + { % special case -- add HyperTeX specials + urlintro "\url{" url * "}" * url make.href.hypertex * } + { urlintro "\url{" * url * "}" * } + if$ + } + if$ +} + +FUNCTION {format.eprint} +{ eprint empty$ + { "" } + { eprintprefix eprint * eprinturl eprint * make.href } + if$ +} + +FUNCTION {format.doi} +{ doi empty$ + { "" } + { doiprefix doi * doiurl doi * make.href } + if$ +} + +FUNCTION {format.pubmed} +{ pubmed empty$ + { "" } + { pubmedprefix pubmed * pubmedurl pubmed * make.href } + if$ +} + +% Output a URL. We can't use the more normal idiom (something like +% `format.url output'), because the `inbrackets' within +% format.lastchecked applies to everything between calls to `output', +% so that `format.url format.lastchecked * output' ends up with both +% the URL and the lastchecked in brackets. +FUNCTION {output.url} +{ url empty$ + 'skip$ + { new.block + format.url output + format.lastchecked output + } + if$ +} + +FUNCTION {output.web.refs} +{ + new.block + inlinelinks + 'skip$ % links were inline -- don't repeat them + { + output.url + addeprints eprint empty$ not and + { format.eprint output.nonnull } + 'skip$ + if$ + adddoiresolver doi empty$ not and + { format.doi output.nonnull } + 'skip$ + if$ + addpubmedresolver pubmed empty$ not and + { format.pubmed output.nonnull } + 'skip$ + if$ + } + if$ +} + +% Wrapper for output.bibitem.original. +% If the URL field is not empty, set makeinlinelink to be true, +% so that an inline link will be started at the next opportunity +FUNCTION {output.bibitem} +{ outside.brackets 'bracket.state := + output.bibitem.original + inlinelinks url empty$ not doi empty$ not or pubmed empty$ not or eprint empty$ not or and + { #1 'makeinlinelink := } + { #0 'makeinlinelink := } + if$ +} + +% Wrapper for fin.entry.original +FUNCTION {fin.entry} +{ output.web.refs % urlbst + makeinlinelink % ooops, it appears we didn't have a title for inlinelink + { possibly.setup.inlinelink % add some artificial link text here, as a fallback + linktextstring output.nonnull } + 'skip$ + if$ + bracket.state close.brackets = % urlbst + { "]" * } + 'skip$ + if$ + fin.entry.original +} + +% Webpage entry type. +% Title and url fields required; +% author, note, year, month, and lastchecked fields optional +% See references +% ISO 690-2 http://www.nlc-bnc.ca/iso/tc46sc9/standard/690-2e.htm +% http://www.classroom.net/classroom/CitingNetResources.html +% http://neal.ctstateu.edu/history/cite.html +% http://www.cas.usf.edu/english/walker/mla.html +% for citation formats for web pages. +FUNCTION {webpage} +{ output.bibitem + author empty$ + { editor empty$ + 'skip$ % author and editor both optional + { format.editors output.nonnull } + if$ + } + { editor empty$ + { format.authors output.nonnull } + { "can't use both author and editor fields in " cite$ * warning$ } + if$ + } + if$ + new.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ + format.title "title" output.check + inbrackets onlinestring output + new.block + year empty$ + 'skip$ + { format.date "year" output.check } + if$ + % We don't need to output the URL details ('lastchecked' and 'url'), + % because fin.entry does that for us, using output.web.refs. The only + % reason we would want to put them here is if we were to decide that + % they should go in front of the rather miscellaneous information in 'note'. + new.block + note output + fin.entry +} +% ...urlbst to here + + +FUNCTION {article} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title "title" output.check + new.block + crossref missing$ + { + journal + "journal" bibinfo.check + emphasize + "journal" output.check + possibly.setup.inlinelink format.vol.num.pages output% urlbst + } + { format.article.crossref output.nonnull + format.pages output + } + if$ + new.block + format.note output + fin.entry +} +FUNCTION {book} +{ output.bibitem + author empty$ + { format.editors "author and editor" output.check + editor format.key output + } + { format.authors output.nonnull + crossref missing$ + { "author and editor" editor either.or.check } + 'skip$ + if$ + } + if$ + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.btitle "title" output.check + format.edition output + crossref missing$ + { format.bvolume output + new.block + format.number.series output + new.sentence + format.publisher.address output + } + { + new.block + format.book.crossref output.nonnull + } + if$ + new.block + format.note output + fin.entry +} +FUNCTION {booklet} +{ output.bibitem + format.authors output + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title "title" output.check + new.block + howpublished "howpublished" bibinfo.check output + address "address" bibinfo.check output + new.block + format.note output + fin.entry +} + +FUNCTION {inbook} +{ output.bibitem + author empty$ + { format.editors "author and editor" output.check + editor format.key output + } + { format.authors output.nonnull + crossref missing$ + { "author and editor" editor either.or.check } + 'skip$ + if$ + } + if$ + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.btitle "title" output.check + format.edition output + crossref missing$ + { + format.bvolume output + format.number.series output + format.chapter "chapter" output.check + new.sentence + format.publisher.address output + new.block + } + { + format.chapter "chapter" output.check + new.block + format.book.crossref output.nonnull + } + if$ + new.block + format.note output + fin.entry +} + +FUNCTION {incollection} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title "title" output.check + new.block + crossref missing$ + { format.in.ed.booktitle "booktitle" output.check + format.edition output + format.bvolume output + format.number.series output + format.chapter.pages output + new.sentence + format.publisher.address output + } + { format.incoll.inproc.crossref output.nonnull + format.chapter.pages output + } + if$ + new.block + format.note output + fin.entry +} +FUNCTION {inproceedings} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title "title" output.check + new.block + crossref missing$ + { format.in.booktitle "booktitle" output.check + format.bvolume output + format.number.series output + format.pages output + address "address" bibinfo.check output + new.sentence + organization "organization" bibinfo.check output + publisher "publisher" bibinfo.check output + } + { format.incoll.inproc.crossref output.nonnull + format.pages output + } + if$ + new.block + format.note output + fin.entry +} +FUNCTION {conference} { inproceedings } +FUNCTION {manual} +{ output.bibitem + format.authors output + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.btitle "title" output.check + format.edition output + organization address new.block.checkb + organization "organization" bibinfo.check output + address "address" bibinfo.check output + new.block + format.note output + fin.entry +} + +FUNCTION {mastersthesis} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title + "title" output.check + new.block + bbl.mthesis format.thesis.type output.nonnull + school "school" bibinfo.warn output + address "address" bibinfo.check output + month "month" bibinfo.check output + new.block + format.note output + fin.entry +} + +FUNCTION {misc} +{ output.bibitem + format.authors output + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title output + new.block + howpublished "howpublished" bibinfo.check output + new.block + format.note output + fin.entry +} +FUNCTION {phdthesis} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.btitle + "title" output.check + new.block + bbl.phdthesis format.thesis.type output.nonnull + school "school" bibinfo.warn output + address "address" bibinfo.check output + new.block + format.note output + fin.entry +} + +FUNCTION {proceedings} +{ output.bibitem + format.editors output + editor format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.btitle "title" output.check + format.bvolume output + format.number.series output + new.sentence + publisher empty$ + { format.organization.address output } + { organization "organization" bibinfo.check output + new.sentence + format.publisher.address output + } + if$ + new.block + format.note output + fin.entry +} + +FUNCTION {techreport} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title + "title" output.check + new.block + format.tr.number output.nonnull + institution "institution" bibinfo.warn output + address "address" bibinfo.check output + new.block + format.note output + fin.entry +} + +FUNCTION {unpublished} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title "title" output.check + new.block + format.note "note" output.check + fin.entry +} + +FUNCTION {default.type} { misc } +READ +FUNCTION {sortify} +{ purify$ + "l" change.case$ +} +INTEGERS { len } +FUNCTION {chop.word} +{ 's := + 'len := + s #1 len substring$ = + { s len #1 + global.max$ substring$ } + 's + if$ +} +FUNCTION {format.lab.names} +{ 's := + "" 't := + s #1 "{vv~}{ll}" format.name$ + s num.names$ duplicate$ + #2 > + { pop$ + " " * bbl.etal * + } + { #2 < + 'skip$ + { s #2 "{ff }{vv }{ll}{ jj}" format.name$ "others" = + { + " " * bbl.etal * + } + { bbl.and space.word * s #2 "{vv~}{ll}" format.name$ + * } + if$ + } + if$ + } + if$ +} + +FUNCTION {author.key.label} +{ author empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { author format.lab.names } + if$ +} + +FUNCTION {author.editor.key.label} +{ author empty$ + { editor empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { editor format.lab.names } + if$ + } + { author format.lab.names } + if$ +} + +FUNCTION {editor.key.label} +{ editor empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { editor format.lab.names } + if$ +} + +FUNCTION {calc.short.authors} +{ type$ "book" = + type$ "inbook" = + or + 'author.editor.key.label + { type$ "proceedings" = + 'editor.key.label + 'author.key.label + if$ + } + if$ + 'short.list := +} + +FUNCTION {calc.label} +{ calc.short.authors + short.list + "(" + * + year duplicate$ empty$ + short.list key field.or.null = or + { pop$ "" } + 'skip$ + if$ + * + 'label := +} + +FUNCTION {sort.format.names} +{ 's := + #1 'nameptr := + "" + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + "{ll{ }}{ ff{ }}{ jj{ }}" + format.name$ 't := + nameptr #1 > + { + " " * + namesleft #1 = t "others" = and + { "zzzzz" * } + { t sortify * } + if$ + } + { t sortify * } + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ +} + +FUNCTION {sort.format.title} +{ 't := + "A " #2 + "An " #3 + "The " #4 t chop.word + chop.word + chop.word + sortify + #1 global.max$ substring$ +} +FUNCTION {author.sort} +{ author empty$ + { key empty$ + { "to sort, need author or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { author sort.format.names } + if$ +} +FUNCTION {author.editor.sort} +{ author empty$ + { editor empty$ + { key empty$ + { "to sort, need author, editor, or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { editor sort.format.names } + if$ + } + { author sort.format.names } + if$ +} +FUNCTION {editor.sort} +{ editor empty$ + { key empty$ + { "to sort, need editor or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { editor sort.format.names } + if$ +} +FUNCTION {presort} +{ calc.label + label sortify + " " + * + type$ "book" = + type$ "inbook" = + or + 'author.editor.sort + { type$ "proceedings" = + 'editor.sort + 'author.sort + if$ + } + if$ + #1 entry.max$ substring$ + 'sort.label := + sort.label + * + " " + * + title field.or.null + sort.format.title + * + #1 entry.max$ substring$ + 'sort.key$ := +} + +ITERATE {presort} +SORT +STRINGS { last.label next.extra } +INTEGERS { last.extra.num number.label } +FUNCTION {initialize.extra.label.stuff} +{ #0 int.to.chr$ 'last.label := + "" 'next.extra := + #0 'last.extra.num := + #0 'number.label := +} +FUNCTION {forward.pass} +{ last.label label = + { last.extra.num #1 + 'last.extra.num := + last.extra.num int.to.chr$ 'extra.label := + } + { "a" chr.to.int$ 'last.extra.num := + "" 'extra.label := + label 'last.label := + } + if$ + number.label #1 + 'number.label := +} +FUNCTION {reverse.pass} +{ next.extra "b" = + { "a" 'extra.label := } + 'skip$ + if$ + extra.label 'next.extra := + extra.label + duplicate$ empty$ + 'skip$ + { year field.or.null #-1 #1 substring$ chr.to.int$ #65 < + { "{\natexlab{" swap$ * "}}" * } + { "{(\natexlab{" swap$ * "})}" * } + if$ } + if$ + 'extra.label := + label extra.label * 'label := +} +EXECUTE {initialize.extra.label.stuff} +ITERATE {forward.pass} +REVERSE {reverse.pass} +FUNCTION {bib.sort.order} +{ sort.label + " " + * + year field.or.null sortify + * + " " + * + title field.or.null + sort.format.title + * + #1 entry.max$ substring$ + 'sort.key$ := +} +ITERATE {bib.sort.order} +SORT +FUNCTION {begin.bib} +{ preamble$ empty$ + 'skip$ + { preamble$ write$ newline$ } + if$ + "\begin{thebibliography}{" number.label int.to.str$ * "}" * + write$ newline$ + "\expandafter\ifx\csname natexlab\endcsname\relax\def\natexlab#1{#1}\fi" + write$ newline$ +} +EXECUTE {begin.bib} +EXECUTE {init.urlbst.variables} % urlbst +EXECUTE {init.state.consts} +ITERATE {call.type$} +FUNCTION {end.bib} +{ newline$ + "\end{thebibliography}" write$ newline$ +} +EXECUTE {end.bib} +%% End of customized bst file +%% +%% End of file `compling.bst'. diff --git a/references/2019.arxiv.conneau/source/XLMR Paper/appendix.tex b/references/2019.arxiv.conneau/source/XLMR Paper/appendix.tex new file mode 100644 index 0000000000000000000000000000000000000000..c4e6f3983dfebfac53b7c11a894ed59112a1757c --- /dev/null +++ b/references/2019.arxiv.conneau/source/XLMR Paper/appendix.tex @@ -0,0 +1,45 @@ +\documentclass[11pt,a4paper]{article} +\usepackage[hyperref]{acl2020} +\usepackage{times} +\usepackage{latexsym} +\renewcommand{\UrlFont}{\ttfamily\small} + +% This is not strictly necessary, and may be commented out, +% but it will improve the layout of the manuscript, +% and will typically save some space. +\usepackage{microtype} +\usepackage{graphicx} +\usepackage{subfigure} +\usepackage{booktabs} % for professional tables +\usepackage{url} +\usepackage{times} +\usepackage{latexsym} +\usepackage{array} +\usepackage{adjustbox} +\usepackage{multirow} +% \usepackage{subcaption} +\usepackage{hyperref} +\usepackage{longtable} +\usepackage{bibentry} +\newcommand{\xlmr}{\textit{XLM-R}\xspace} +\newcommand{\mbert}{mBERT\xspace} +\input{content/tables} + +\begin{document} +\nobibliography{acl2020} +\bibliographystyle{acl_natbib} +\appendix +\onecolumn +\section*{Supplementary materials} +\section{Languages and statistics for CC-100 used by \xlmr} +In this section we present the list of languages in the CC-100 corpus we created for training \xlmr. We also report statistics such as the number of tokens and the size of each monolingual corpus. +\label{sec:appendix_A} +\insertDataStatistics + +\newpage +\section{Model Architectures and Sizes} +As we showed in section 5, capacity is an important parameter for learning strong cross-lingual representations. In the table below, we list multiple monolingual and multilingual models used by the research community and summarize their architectures and total number of parameters. +\label{sec:appendix_B} + +\insertParameters +\end{document} \ No newline at end of file diff --git a/references/2019.arxiv.conneau/source/XLMR Paper/content/batchsize.pdf b/references/2019.arxiv.conneau/source/XLMR Paper/content/batchsize.pdf new file mode 100644 index 0000000000000000000000000000000000000000..712fff8991344922137bc52d374e3b0366c5eebb --- /dev/null +++ b/references/2019.arxiv.conneau/source/XLMR Paper/content/batchsize.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0e0c4e1c156379efeba93f0c1a6717bb12ab0b2aa0bdd361a7fda362ff01442e +size 14673 diff --git a/references/2019.arxiv.conneau/source/XLMR Paper/content/capacity.pdf b/references/2019.arxiv.conneau/source/XLMR Paper/content/capacity.pdf new file mode 100644 index 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mode 100644 index 0000000000000000000000000000000000000000..f2827efcc9dd6ebc86df63dea27589b82b108db2 --- /dev/null +++ b/references/2019.arxiv.conneau/source/XLMR Paper/content/tables.tex @@ -0,0 +1,398 @@ + + + +\newcommand{\insertXNLItable}{ + \begin{table*}[h!] + \begin{center} + % \scriptsize + \resizebox{1\linewidth}{!}{ + \begin{tabular}[b]{l ccc ccccccccccccccc c} + \toprule + {\bf Model} & {\bf D }& {\bf \#M} & {\bf \#lg} & {\bf en} & {\bf fr} & {\bf es} & {\bf de} & {\bf el} & {\bf bg} & {\bf ru} & {\bf tr} & {\bf ar} & {\bf vi} & {\bf th} & {\bf zh} & {\bf hi} & {\bf sw} & {\bf ur} & {\bf Avg}\\ + \midrule + %\cmidrule(r){1-1} + %\cmidrule(lr){2-4} + %\cmidrule(lr){5-19} + %\cmidrule(l){20-20} + + \multicolumn{19}{l}{\it Fine-tune multilingual model on English training set (Cross-lingual Transfer)} \\ + %\midrule + \midrule + \citet{lample2019cross} & Wiki+MT & N & 15 & 85.0 & 78.7 & 78.9 & 77.8 & 76.6 & 77.4 & 75.3 & 72.5 & 73.1 & 76.1 & 73.2 & 76.5 & 69.6 & 68.4 & 67.3 & 75.1 \\ + \citet{huang2019unicoder} & Wiki+MT & N & 15 & 85.1 & 79.0 & 79.4 & 77.8 & 77.2 & 77.2 & 76.3 & 72.8 & 73.5 & 76.4 & 73.6 & 76.2 & 69.4 & 69.7 & 66.7 & 75.4 \\ + %\midrule + \citet{devlin2018bert} & Wiki & N & 102 & 82.1 & 73.8 & 74.3 & 71.1 & 66.4 & 68.9 & 69.0 & 61.6 & 64.9 & 69.5 & 55.8 & 69.3 & 60.0 & 50.4 & 58.0 & 66.3 \\ + \citet{lample2019cross} & Wiki & N & 100 & 83.7 & 76.2 & 76.6 & 73.7 & 72.4 & 73.0 & 72.1 & 68.1 & 68.4 & 72.0 & 68.2 & 71.5 & 64.5 & 58.0 & 62.4 & 71.3 \\ + \citet{lample2019cross} & Wiki & 1 & 100 & 83.2 & 76.7 & 77.7 & 74.0 & 72.7 & 74.1 & 72.7 & 68.7 & 68.6 & 72.9 & 68.9 & 72.5 & 65.6 & 58.2 & 62.4 & 70.7 \\ + \bf XLM-R\textsubscript{Base} & CC & 1 & 100 & 85.8 & 79.7 & 80.7 & 78.7 & 77.5 & 79.6 & 78.1 & 74.2 & 73.8 & 76.5 & 74.6 & 76.7 & 72.4 & 66.5 & 68.3 & 76.2 \\ + \bf XLM-R & CC & 1 & 100 & \bf 89.1 & \bf 84.1 & \bf 85.1 & \bf 83.9 & \bf 82.9 & \bf 84.0 & \bf 81.2 & \bf 79.6 & \bf 79.8 & \bf 80.8 & \bf 78.1 & \bf 80.2 & \bf 76.9 & \bf 73.9 & \bf 73.8 & \bf 80.9 \\ + \midrule + \multicolumn{19}{l}{\it Translate everything to English and use English-only model (TRANSLATE-TEST)} \\ + \midrule + BERT-en & Wiki & 1 & 1 & 88.8 & 81.4 & 82.3 & 80.1 & 80.3 & 80.9 & 76.2 & 76.0 & 75.4 & 72.0 & 71.9 & 75.6 & 70.0 & 65.8 & 65.8 & 76.2 \\ + RoBERTa & Wiki+CC & 1 & 1 & \underline{\bf 91.3} & 82.9 & 84.3 & 81.2 & 81.7 & 83.1 & 78.3 & 76.8 & 76.6 & 74.2 & 74.1 & 77.5 & 70.9 & 66.7 & 66.8 & 77.8 \\ + % XLM-en & Wiki & 1 & 1 & 00.0 & 00.0 & 00.0 & 00.0 & 00.0 & 00.0 & 00.0 \\ + \midrule + \multicolumn{19}{l}{\it Fine-tune multilingual model on each training set (TRANSLATE-TRAIN)} \\ + \midrule + \citet{lample2019cross} & Wiki & N & 100 & 82.9 & 77.6 & 77.9 & 77.9 & 77.1 & 75.7 & 75.5 & 72.6 & 71.2 & 75.8 & 73.1 & 76.2 & 70.4 & 66.5 & 62.4 & 74.2 \\ + \midrule + \multicolumn{19}{l}{\it Fine-tune multilingual model on all training sets (TRANSLATE-TRAIN-ALL)} \\ + \midrule + \citet{lample2019cross}$^{\dagger}$ & Wiki+MT & 1 & 15 & 85.0 & 80.8 & 81.3 & 80.3 & 79.1 & 80.9 & 78.3 & 75.6 & 77.6 & 78.5 & 76.0 & 79.5 & 72.9 & 72.8 & 68.5 & 77.8 \\ + \citet{huang2019unicoder} & Wiki+MT & 1 & 15 & 85.6 & 81.1 & 82.3 & 80.9 & 79.5 & 81.4 & 79.7 & 76.8 & 78.2 & 77.9 & 77.1 & 80.5 & 73.4 & 73.8 & 69.6 & 78.5 \\ + %\midrule + \citet{lample2019cross} & Wiki & 1 & 100 & 84.5 & 80.1 & 81.3 & 79.3 & 78.6 & 79.4 & 77.5 & 75.2 & 75.6 & 78.3 & 75.7 & 78.3 & 72.1 & 69.2 & 67.7 & 76.9 \\ + \bf XLM-R\textsubscript{Base} & CC & 1 & 100 & 85.4 & 81.4 & 82.2 & 80.3 & 80.4 & 81.3 & 79.7 & 78.6 & 77.3 & 79.7 & 77.9 & 80.2 & 76.1 & 73.1 & 73.0 & 79.1 \\ + \bf XLM-R & CC & 1 & 100 & \bf 89.1 & \underline{\bf 85.1} & \underline{\bf 86.6} & \underline{\bf 85.7} & \underline{\bf 85.3} & \underline{\bf 85.9} & \underline{\bf 83.5} & \underline{\bf 83.2} & \underline{\bf 83.1} & \underline{\bf 83.7} & \underline{\bf 81.5} & \underline{\bf 83.7} & \underline{\bf 81.6} & \underline{\bf 78.0} & \underline{\bf 78.1} & \underline{\bf 83.6} \\ + \bottomrule + \end{tabular} + } + \caption{\textbf{Results on cross-lingual classification.} We report the accuracy on each of the 15 XNLI languages and the average accuracy. We specify the dataset D used for pretraining, the number of models \#M the approach requires and the number of languages \#lg the model handles. Our \xlmr results are averaged over five different seeds. We show that using the translate-train-all approach which leverages training sets from multiple languages, \xlmr obtains a new state of the art on XNLI of $83.6$\% average accuracy. Results with $^{\dagger}$ are from \citet{huang2019unicoder}. %It also outperforms previous methods on cross-lingual transfer. + \label{tab:xnli}} + \end{center} +% \vspace{-0.4cm} + \end{table*} +} + +% Evolution of performance w.r.t number of languages +\newcommand{\insertLanguagesize}{ + \begin{table*}[h!] + \begin{minipage}{0.49\textwidth} + \includegraphics[scale=0.4]{content/wiki_vs_cc.pdf} + \end{minipage} + \hfill + \begin{minipage}{0.4\textwidth} + \captionof{figure}{\textbf{Distribution of the amount of data (in MB) per language for Wikipedia and CommonCrawl.} The Wikipedia data used in open-source mBERT and XLM is not sufficient for the model to develop an understanding of low-resource languages. The CommonCrawl data we collect alleviates that issue and creates the conditions for a single model to understand text coming from multiple languages. \label{fig:lgs}} + \end{minipage} +% \vspace{-0.5cm} + \end{table*} +} + +% Evolution of performance w.r.t number of languages +\newcommand{\insertXLMmorelanguages}{ + \begin{table*}[h!] + \begin{minipage}{0.49\textwidth} + \includegraphics[scale=0.4]{content/evolution_languages} + \end{minipage} + \hfill + \begin{minipage}{0.4\textwidth} + \captionof{figure}{\textbf{Evolution of XLM performance on SeqLab, XNLI and GLUE as the number of languages increases.} While there are subtlteties as to what languages lose more accuracy than others as we add more languages, we observe a steady decrease of the overall monolingual and cross-lingual performance. \label{fig:lgsunused}} + \end{minipage} +% \vspace{-0.5cm} + \end{table*} +} + +\newcommand{\insertMLQA}{ +\begin{table*}[h!] + \begin{center} + % \scriptsize + \resizebox{1\linewidth}{!}{ + \begin{tabular}[h]{l cc ccccccc c} + \toprule + {\bf Model} & {\bf train} & {\bf \#lgs} & {\bf en} & {\bf es} & {\bf de} & {\bf ar} & {\bf hi} & {\bf vi} & {\bf zh} & {\bf Avg} \\ + \midrule + BERT-Large$^{\dagger}$ & en & 1 & 80.2 / 67.4 & - & - & - & - & - & - & - \\ + mBERT$^{\dagger}$ & en & 102 & 77.7 / 65.2 & 64.3 / 46.6 & 57.9 / 44.3 & 45.7 / 29.8 & 43.8 / 29.7 & 57.1 / 38.6 & 57.5 / 37.3 & 57.7 / 41.6 \\ + XLM-15$^{\dagger}$ & en & 15 & 74.9 / 62.4 & 68.0 / 49.8 & 62.2 / 47.6 & 54.8 / 36.3 & 48.8 / 27.3 & 61.4 / 41.8 & 61.1 / 39.6 & 61.6 / 43.5 \\ + XLM-R\textsubscript{Base} & en & 100 & 77.1 / 64.6 & 67.4 / 49.6 & 60.9 / 46.7 & 54.9 / 36.6 & 59.4 / 42.9 & 64.5 / 44.7 & 61.8 / 39.3 & 63.7 / 46.3 \\ + \bf XLM-R & en & 100 & \bf 80.6 / 67.8 & \bf 74.1 / 56.0 & \bf 68.5 / 53.6 & \bf 63.1 / 43.5 & \bf 69.2 / 51.6 & \bf 71.3 / 50.9 & \bf 68.0 / 45.4 & \bf 70.7 / 52.7 \\ + \bottomrule + \end{tabular} + } + \caption{\textbf{Results on MLQA question answering} We report the F1 and EM (exact match) scores for zero-shot classification where models are fine-tuned on the English Squad dataset and evaluated on the 7 languages of MLQA. Results with $\dagger$ are taken from the original MLQA paper \citet{lewis2019mlqa}. + \label{tab:mlqa}} + \end{center} +\end{table*} +} + +\newcommand{\insertNER}{ +\begin{table}[t] + \begin{center} + % \scriptsize + \resizebox{1\linewidth}{!}{ + \begin{tabular}[b]{l cc cccc c} + \toprule + {\bf Model} & {\bf train} & {\bf \#M} & {\bf en} & {\bf nl} & {\bf es} & {\bf de} & {\bf Avg}\\ + \midrule + \citet{lample-etal-2016-neural} & each & N & 90.74 & 81.74 & 85.75 & 78.76 & 84.25 \\ + \citet{akbik2018coling} & each & N & \bf 93.18 & 90.44 & - & \bf 88.27 & - \\ + \midrule + \multirow{2}{*}{mBERT$^{\dagger}$} & each & N & 91.97 & 90.94 & 87.38 & 82.82 & 88.28\\ + & en & 1 & 91.97 & 77.57 & 74.96 & 69.56 & 78.52\\ + \midrule + \multirow{3}{*}{XLM-R\textsubscript{Base}} & each & N & 92.25 & 90.39 & 87.99 & 84.60 & 88.81\\ + & en & 1 & 92.25 & 78.08 & 76.53 & 69.60 & 79.11\\ + & all & 1 & 91.08 & 89.09 & 87.28 & 83.17 & 87.66 \\ + \midrule + \multirow{3}{*}{\bf XLM-R} & each & N & 92.92 & \bf 92.53 & \bf 89.72 & 85.81 & 90.24\\ + & en & 1 & 92.92 & 80.80 & 78.64 & 71.40 & 80.94\\ + & all & 1 & 92.00 & 91.60 & 89.52 & 84.60 & 89.43 \\ + \bottomrule + \end{tabular} + } + \caption{\textbf{Results on named entity recognition} on CoNLL-2002 and CoNLL-2003 (F1 score). Results with $\dagger$ are from \citet{wu2019beto}. Note that mBERT and \xlmr do not use a linear-chain CRF, as opposed to \citet{akbik2018coling} and \citet{lample-etal-2016-neural}. + \label{tab:ner}} + \end{center} + \vspace{-0.6cm} + \end{table} +} + + +\newcommand{\insertAblationone}{ +\begin{table*}[h!] + \begin{minipage}[t]{0.3\linewidth} + \begin{center} + %\includegraphics[width=\linewidth]{content/xlmroberta_transfer_dilution.pdf} + \includegraphics{content/dilution} + \captionof{figure}{The transfer-interference trade-off: Low-resource languages benefit from scaling to more languages, until dilution (interference) kicks in and degrades overall performance.} + \label{fig:transfer_dilution} + \vspace{-0.2cm} + \end{center} + \end{minipage} + \hfill + \begin{minipage}[t]{0.3\linewidth} + \begin{center} + %\includegraphics[width=\linewidth]{content/xlmroberta_evolution.pdf} + \includegraphics{content/wikicc} + \captionof{figure}{Wikipedia versus CommonCrawl: An XLM-7 obtains significantly better performance when trained on CC, in particular on low-resource languages.} + \label{fig:curse} + \end{center} + \end{minipage} + \hfill + \begin{minipage}[t]{0.3\linewidth} + \begin{center} + % \includegraphics[width=\linewidth]{content/xlmroberta_evolution.pdf} + \includegraphics{content/capacity} + \captionof{figure}{Adding more capacity to the model alleviates the curse of multilinguality, but remains an issue for models of moderate size.} + \label{fig:capacity} + \end{center} + \end{minipage} + \vspace{-0.2cm} +\end{table*} +} + + +\newcommand{\insertAblationtwo}{ +\begin{table*}[h!] + \begin{minipage}[t]{0.3\linewidth} + \begin{center} + %\includegraphics[width=\columnwidth]{content/xlmroberta_alpha_tradeoff.pdf} + \includegraphics{content/langsampling} + \captionof{figure}{On the high-resource versus low-resource trade-off: impact of batch language sampling for XLM-100. + \label{fig:alpha}} + \end{center} + \end{minipage} + \hfill + \begin{minipage}[t]{0.3\linewidth} + \begin{center} + %\includegraphics[width=\columnwidth]{content/xlmroberta_vocab.pdf} + \includegraphics{content/vocabsize.pdf} + \captionof{figure}{On the impact of vocabulary size at fixed capacity and with increasing capacity for XLM-100. + \label{fig:vocab}} + \end{center} + \end{minipage} + \hfill + \begin{minipage}[t]{0.3\linewidth} + \begin{center} + %\includegraphics[width=\columnwidth]{content/xlmroberta_batch_and_tok.pdf} + \includegraphics{content/batchsize.pdf} + \captionof{figure}{On the impact of large-scale training, and preprocessing simplification from BPE with tokenization to SPM on raw text data. + \label{fig:batch}} + \end{center} + \end{minipage} + \vspace{-0.2cm} +\end{table*} +} + + +% Multilingual vs monolingual +\newcommand{\insertMultiMono}{ + \begin{table}[h!] + \begin{center} + % \scriptsize + \resizebox{1\linewidth}{!}{ + \begin{tabular}[b]{l cc ccccccc c} + \toprule + {\bf Model} & {\bf D } & {\bf \#vocab} & {\bf en} & {\bf fr} & {\bf de} & {\bf ru} & {\bf zh} & {\bf sw} & {\bf ur} & {\bf Avg}\\ + \midrule + \multicolumn{11}{l}{\it Monolingual baselines}\\ + \midrule + \multirow{2}{*}{BERT} & Wiki & 40k & 84.5 & 78.6 & 80.0 & 75.5 & 77.7 & 60.1 & 57.3 & 73.4 \\ + & CC & 40k & 86.7 & 81.2 & 81.2 & 78.2 & 79.5 & 70.8 & 65.1 & 77.5 \\ + \midrule + \multicolumn{11}{l}{\it Multilingual models (cross-lingual transfer)}\\ + \midrule + \multirow{2}{*}{XLM-7} & Wiki & 150k & 82.3 & 76.8 & 74.7 & 72.5 & 73.1 & 60.8 & 62.3 & 71.8 \\ + & CC & 150k & 85.7 & 78.6 & 79.5 & 76.4 & 74.8 & 71.2 & 66.9 & 76.2 \\ + \midrule + \multicolumn{11}{l}{\it Multilingual models (translate-train-all)}\\ + \midrule + \multirow{2}{*}{XLM-7} & Wiki & 150k & 84.6 & 80.1 & 80.2 & 75.7 & 78 & 68.7 & 66.7 & 76.3 \\ + & CC & 150k & \bf 87.2 & \bf 82.5 & \bf 82.9 & \bf 79.7 & \bf 80.4 & \bf 75.7 & \bf 71.5 & \bf 80.0 \\ + % \midrule + % XLM (sw,ar) & CC & 60k & N & 2-3 & - & - & - & - & - & 00.0 & - & 00.0 \\ + % XLM (ur,hi,ar) & CC & 60k & N & 2-3 & - & - & - & - & - & - & 00.0 & 00.0 \\ + \bottomrule + \end{tabular} + } + \caption{\textbf{Multilingual versus monolingual models (BERT-BASE).} We compare the performance of monolingual models (BERT) versus multilingual models (XLM) on seven languages, using a BERT-BASE architecture. We choose a vocabulary size of 40k and 150k for monolingual and multilingual models. + \label{tab:multimono}} + \end{center} + \vspace{-0.4cm} + \end{table} +} + +% GLUE benchmark results +\newcommand{\insertGlue}{ + \begin{table}[h!] + \begin{center} + % \scriptsize + \resizebox{1\linewidth}{!}{ + \begin{tabular}[b]{l|c|cccccc|c} + \toprule + {\bf Model} & {\bf \#lgs} & {\bf MNLI-m/mm} & {\bf QNLI} & {\bf QQP} & {\bf SST} & {\bf MRPC} & {\bf STS-B} & {\bf Avg}\\ + \midrule + BERT\textsubscript{Large}$^{\dagger}$ & 1 & 86.6/- & 92.3 & 91.3 & 93.2 & 88.0 & 90.0 & 90.2 \\ + XLNet\textsubscript{Large}$^{\dagger}$ & 1 & 89.8/- & 93.9 & 91.8 & 95.6 & 89.2 & 91.8 & 92.0 \\ + RoBERTa$^{\dagger}$ & 1 & 90.2/90.2 & 94.7 & 92.2 & 96.4 & 90.9 & 92.4 & 92.8 \\ + XLM-R & 100 & 88.9/89.0 & 93.8 & 92.3 & 95.0 & 89.5 & 91.2 & 91.8 \\ + \bottomrule + \end{tabular} + } + \caption{\textbf{GLUE dev results.} Results with $^{\dagger}$ are from \citet{roberta2019}. We compare the performance of \xlmr to BERT\textsubscript{Large}, XLNet and RoBERTa on the English GLUE benchmark. + \label{tab:glue}} + \end{center} + \vspace{-0.4cm} + \end{table} +} + + +% Wiki vs CommonCrawl statistics +\newcommand{\insertWikivsCC}{ + \begin{table*}[h] + \begin{center} + %\includegraphics[width=\linewidth]{content/wiki_vs_cc.pdf} + \includegraphics{content/datasize.pdf} + \captionof{figure}{Amount of data in GiB (log-scale) for the 88 languages that appear in both the Wiki-100 corpus used for mBERT and XLM-100, and the CC-100 used for XLM-R. CC-100 increases the amount of data by several orders of magnitude, in particular for low-resource languages. + \label{fig:wikivscc}} + \end{center} +% \vspace{-0.4cm} + \end{table*} +} + +% Corpus statistics for CC-100 +\newcommand{\insertDataStatistics}{ +%\resizebox{1\linewidth}{!}{ +\begin{table}[h!] +\begin{center} +\small +\begin{tabular}[b]{clrrclrr} +\toprule +\textbf{ISO code} & \textbf{Language} & \textbf{Tokens} (M) & \textbf{Size} (GiB) & \textbf{ISO code} & \textbf{Language} & \textbf{Tokens} (M) & \textbf{Size} (GiB)\\ +\cmidrule(r){1-4}\cmidrule(l){5-8} +{\bf af }& Afrikaans & 242 & 1.3 &{\bf lo }& Lao & 17 & 0.6 \\ +{\bf am }& Amharic & 68 & 0.8 &{\bf lt }& Lithuanian & 1835 & 13.7 \\ +{\bf ar }& Arabic & 2869 & 28.0 &{\bf lv }& Latvian & 1198 & 8.8 \\ +{\bf as }& Assamese & 5 & 0.1 &{\bf mg }& Malagasy & 25 & 0.2 \\ +{\bf az }& Azerbaijani & 783 & 6.5 &{\bf mk }& Macedonian & 449 & 4.8 \\ +{\bf be }& Belarusian & 362 & 4.3 &{\bf ml }& Malayalam & 313 & 7.6 \\ +{\bf bg }& Bulgarian & 5487 & 57.5 &{\bf mn }& Mongolian & 248 & 3.0 \\ +{\bf bn }& Bengali & 525 & 8.4 &{\bf mr }& Marathi & 175 & 2.8 \\ +{\bf - }& Bengali Romanized & 77 & 0.5 &{\bf ms }& Malay & 1318 & 8.5 \\ +{\bf br }& Breton & 16 & 0.1 &{\bf my }& Burmese & 15 & 0.4 \\ +{\bf bs }& Bosnian & 14 & 0.1 &{\bf my }& Burmese & 56 & 1.6 \\ +{\bf ca }& Catalan & 1752 & 10.1 &{\bf ne }& Nepali & 237 & 3.8 \\ +{\bf cs }& Czech & 2498 & 16.3 &{\bf nl }& Dutch & 5025 & 29.3 \\ +{\bf cy }& Welsh & 141 & 0.8 &{\bf no }& Norwegian & 8494 & 49.0 \\ +{\bf da }& Danish & 7823 & 45.6 &{\bf om }& Oromo & 8 & 0.1 \\ +{\bf de }& German & 10297 & 66.6 &{\bf or }& Oriya & 36 & 0.6 \\ +{\bf el }& Greek & 4285 & 46.9 &{\bf pa }& Punjabi & 68 & 0.8 \\ +{\bf en }& English & 55608 & 300.8 &{\bf pl }& Polish & 6490 & 44.6 \\ +{\bf eo }& Esperanto & 157 & 0.9 &{\bf ps }& Pashto & 96 & 0.7 \\ +{\bf es }& Spanish & 9374 & 53.3 &{\bf pt }& Portuguese & 8405 & 49.1 \\ +{\bf et }& Estonian & 843 & 6.1 &{\bf ro }& Romanian & 10354 & 61.4 \\ +{\bf eu }& Basque & 270 & 2.0 &{\bf ru }& Russian & 23408 & 278.0 \\ +{\bf fa }& Persian & 13259 & 111.6 &{\bf sa }& Sanskrit & 17 & 0.3 \\ +{\bf fi }& Finnish & 6730 & 54.3 &{\bf sd }& Sindhi & 50 & 0.4 \\ +{\bf fr }& French & 9780 & 56.8 &{\bf si }& Sinhala & 243 & 3.6 \\ +{\bf fy }& Western Frisian & 29 & 0.2 &{\bf sk }& Slovak & 3525 & 23.2 \\ +{\bf ga }& Irish & 86 & 0.5 &{\bf sl }& Slovenian & 1669 & 10.3 \\ +{\bf gd }& Scottish Gaelic & 21 & 0.1 &{\bf so }& Somali & 62 & 0.4 \\ +{\bf gl }& Galician & 495 & 2.9 &{\bf sq }& Albanian & 918 & 5.4 \\ +{\bf gu }& Gujarati & 140 & 1.9 &{\bf sr }& Serbian & 843 & 9.1 \\ +{\bf ha }& Hausa & 56 & 0.3 &{\bf su }& Sundanese & 10 & 0.1 \\ +{\bf he }& Hebrew & 3399 & 31.6 &{\bf sv }& Swedish & 77.8 & 12.1 \\ +{\bf hi }& Hindi & 1715 & 20.2 &{\bf sw }& Swahili & 275 & 1.6 \\ +{\bf - }& Hindi Romanized & 88 & 0.5 &{\bf ta }& Tamil & 595 & 12.2 \\ +{\bf hr }& Croatian & 3297 & 20.5 &{\bf - }& Tamil Romanized & 36 & 0.3 \\ +{\bf hu }& Hungarian & 7807 & 58.4 &{\bf te }& Telugu & 249 & 4.7 \\ +{\bf hy }& Armenian & 421 & 5.5 &{\bf - }& Telugu Romanized & 39 & 0.3 \\ +{\bf id }& Indonesian & 22704 & 148.3 &{\bf th }& Thai & 1834 & 71.7 \\ +{\bf is }& Icelandic & 505 & 3.2 &{\bf tl }& Filipino & 556 & 3.1 \\ +{\bf it }& Italian & 4983 & 30.2 &{\bf tr }& Turkish & 2736 & 20.9 \\ +{\bf ja }& Japanese & 530 & 69.3 &{\bf ug }& Uyghur & 27 & 0.4 \\ +{\bf jv }& Javanese & 24 & 0.2 &{\bf uk }& Ukrainian & 6.5 & 84.6 \\ +{\bf ka }& Georgian & 469 & 9.1 &{\bf ur }& Urdu & 730 & 5.7 \\ +{\bf kk }& Kazakh & 476 & 6.4 &{\bf - }& Urdu Romanized & 85 & 0.5 \\ +{\bf km }& Khmer & 36 & 1.5 &{\bf uz }& Uzbek & 91 & 0.7 \\ +{\bf kn }& Kannada & 169 & 3.3 &{\bf vi }& Vietnamese & 24757 & 137.3 \\ +{\bf ko }& Korean & 5644 & 54.2 &{\bf xh }& Xhosa & 13 & 0.1 \\ +{\bf ku }& Kurdish (Kurmanji) & 66 & 0.4 &{\bf yi }& Yiddish & 34 & 0.3 \\ +{\bf ky }& Kyrgyz & 94 & 1.2 &{\bf zh }& Chinese (Simplified) & 259 & 46.9 \\ +{\bf la }& Latin & 390 & 2.5 &{\bf zh }& Chinese (Traditional) & 176 & 16.6 \\ + +\bottomrule +\end{tabular} +\caption{\textbf{Languages and statistics of the CC-100 corpus.} We report the list of 100 languages and include the number of tokens (Millions) and the size of the data (in GiB) for each language. Note that we also include romanized variants of some non latin languages such as Bengali, Hindi, Tamil, Telugu and Urdu.\label{tab:datastats}} +\end{center} +\end{table} +%} +} + + +% Comparison of parameters for different models +\newcommand{\insertParameters}{ + \begin{table*}[h!] + \begin{center} + % \scriptsize + %\resizebox{1\linewidth}{!}{ + \begin{tabular}[b]{lrcrrrrrc} + \toprule + \textbf{Model} & \textbf{\#lgs} & \textbf{tokenization} & \textbf{L} & \textbf{$H_{m}$} & \textbf{$H_{ff}$} & \textbf{A} & \textbf{V} & \textbf{\#params}\\ + \cmidrule(r){1-1} + \cmidrule(lr){2-3} + \cmidrule(lr){4-8} + \cmidrule(l){9-9} + % TODO: rank by number of parameters + BERT\textsubscript{Base} & 1 & WordPiece & 12 & 768 & 3072 & 12 & 30k & 110M \\ + BERT\textsubscript{Large} & 1 & WordPiece & 24 & 1024 & 4096 & 16 & 30k & 335M \\ + mBERT & 104 & WordPiece & 12 & 768 & 3072 & 12 & 110k & 172M \\ + RoBERTa\textsubscript{Base} & 1 & bBPE & 12 & 768 & 3072 & 8 & 50k & 125M \\ + RoBERTa & 1 & bBPE & 24 & 1024 & 4096 & 16 & 50k & 355M \\ + XLM-15 & 15 & BPE & 12 & 1024 & 4096 & 8 & 95k & 250M \\ + XLM-17 & 17 & BPE & 16 & 1280 & 5120 & 16 & 200k & 570M \\ + XLM-100 & 100 & BPE & 16 & 1280 & 5120 & 16 & 200k & 570M \\ + Unicoder & 15 & BPE & 12 & 1024 & 4096 & 8 & 95k & 250M \\ + \xlmr\textsubscript{Base} & 100 & SPM & 12 & 768 & 3072 & 12 & 250k & 270M \\ + \xlmr & 100 & SPM & 24 & 1024 & 4096 & 16 & 250k & 550M \\ + GPT2 & 1 & bBPE & 48 & 1600 & 6400 & 32 & 50k & 1.5B \\ + wide-mmNMT & 103 & SPM & 12 & 2048 & 16384 & 32 & 64k & 3B \\ + deep-mmNMT & 103 & SPM & 24 & 1024 & 16384 & 32 & 64k & 3B \\ + T5-3B & 1 & WordPiece & 24 & 1024 & 16384 & 32 & 32k & 3B \\ + T5-11B & 1 & WordPiece & 24 & 1024 & 65536 & 32 & 32k & 11B \\ + % XLNet\textsubscript{Large}$^{\dagger}$ & 1 & 89.8/- & 93.9 & 91.8 & 95.6 & 89.2 & 91.8 & 92.0 \\ + % RoBERTa$^{\dagger}$ & 1 & 90.2/90.2 & 94.7 & 92.2 & 96.4 & 90.9 & 92.4 & 92.8 \\ + % XLM-R & 100 & 88.4/88.5 & 93.1 & 92.2 & 95.1 & 89.7 & 90.4 & 91.5 \\ + \bottomrule + \end{tabular} + %} + \caption{\textbf{Details on model sizes.} + We show the tokenization used by each Transformer model, the number of layers L, the number of hidden states of the model $H_{m}$, the dimension of the feed-forward layer $H_{ff}$, the number of attention heads A, the size of the vocabulary V and the total number of parameters \#params. + For Transformer encoders, the number of parameters can be approximated by $4LH_m^2 + 2LH_m H_{ff} + VH_m$. + GPT2 numbers are from \citet{radford2019language}, mm-NMT models are from the work of \citet{arivazhagan2019massively} on massively multilingual neural machine translation (mmNMT), and T5 numbers are from \citet{raffel2019exploring}. While \xlmr is among the largest models partly due to its large embedding layer, it has a similar number of parameters than XLM-100, and remains significantly smaller that recently introduced Transformer models for multilingual MT and transfer learning. While this table gives more hindsight on the difference of capacity of each model, note it does not highlight other critical differences between the models. + \label{tab:parameters}} + \end{center} + \vspace{-0.4cm} + \end{table*} +} \ No newline at end of file diff --git a/references/2019.arxiv.conneau/source/XLMR Paper/content/vocabsize.pdf b/references/2019.arxiv.conneau/source/XLMR Paper/content/vocabsize.pdf new file mode 100644 index 0000000000000000000000000000000000000000..b6aaa736ba18593c6809ba7f7d403920f348ef4d --- /dev/null +++ b/references/2019.arxiv.conneau/source/XLMR Paper/content/vocabsize.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e45090856dc149265ada0062c8c2456c3057902dfaaade60aa80905785563506 +size 15677 diff --git a/references/2019.arxiv.conneau/source/XLMR Paper/content/wikicc.pdf b/references/2019.arxiv.conneau/source/XLMR Paper/content/wikicc.pdf new file mode 100644 index 0000000000000000000000000000000000000000..b0271d1a2d035ec2e76fe25e62eca0ff48038ffa --- /dev/null +++ b/references/2019.arxiv.conneau/source/XLMR Paper/content/wikicc.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f0d7e959db8240f283922c3ca7c6de6f5ad3750681f27f4fcf35d161506a7a21 +size 16304 diff --git a/references/2019.arxiv.conneau/source/XLMR Paper/texput.log b/references/2019.arxiv.conneau/source/XLMR Paper/texput.log new file mode 100644 index 0000000000000000000000000000000000000000..3b64d04a3048c2da93d41ce759b4dca2fa85dbe2 --- /dev/null +++ b/references/2019.arxiv.conneau/source/XLMR Paper/texput.log @@ -0,0 +1,21 @@ +This is pdfTeX, Version 3.14159265-2.6-1.40.20 (TeX Live 2019) (preloaded format=pdflatex 2019.5.8) 7 APR 2020 17:41 +entering extended mode + restricted \write18 enabled. + %&-line parsing enabled. +**acl2020.tex + +! Emergency stop. +<*> acl2020.tex + +*** (job aborted, file error in nonstop mode) + + +Here is how much of TeX's memory you used: + 3 strings out of 492616 + 102 string characters out of 6129482 + 57117 words of memory out of 5000000 + 4025 multiletter control sequences out of 15000+600000 + 3640 words of font info for 14 fonts, out of 8000000 for 9000 + 1141 hyphenation exceptions out of 8191 + 0i,0n,0p,1b,6s stack positions out of 5000i,500n,10000p,200000b,80000s +! ==> Fatal error occurred, no output PDF file produced! diff --git a/references/2019.arxiv.conneau/source/XLMR Paper/xlmr.bbl b/references/2019.arxiv.conneau/source/XLMR Paper/xlmr.bbl new file mode 100644 index 0000000000000000000000000000000000000000..264cff4aa65ba39bd36e75804c0b6cf22c037fba --- /dev/null +++ b/references/2019.arxiv.conneau/source/XLMR Paper/xlmr.bbl @@ -0,0 +1,285 @@ +\begin{thebibliography}{40} +\expandafter\ifx\csname natexlab\endcsname\relax\def\natexlab#1{#1}\fi + +\bibitem[{Akbik et~al.(2018)Akbik, Blythe, and Vollgraf}]{akbik2018coling} +Alan Akbik, Duncan Blythe, and Roland Vollgraf. 2018. +\newblock Contextual string embeddings for sequence labeling. +\newblock In \emph{COLING}, pages 1638--1649. + +\bibitem[{Arivazhagan et~al.(2019)Arivazhagan, Bapna, Firat, Lepikhin, Johnson, + Krikun, Chen, Cao, Foster, Cherry et~al.}]{arivazhagan2019massively} +Naveen Arivazhagan, Ankur Bapna, Orhan Firat, Dmitry Lepikhin, Melvin Johnson, + Maxim Krikun, Mia~Xu Chen, Yuan Cao, George Foster, Colin Cherry, et~al. + 2019. +\newblock Massively multilingual neural machine translation in the wild: + Findings and challenges. +\newblock \emph{arXiv preprint arXiv:1907.05019}. + +\bibitem[{Bowman et~al.(2015)Bowman, Angeli, Potts, and + Manning}]{bowman2015large} +Samuel~R. 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Please do not make the titlebox +% smaller than 5cm (the original size); we will check this +% in the camera-ready version and ask you to change it back. + +\newcommand\BibTeX{B\textsc{ib}\TeX} +\usepackage{xspace} +\newcommand{\xlmr}{\textit{XLM-R}\xspace} +\newcommand{\mbert}{mBERT\xspace} +\newcommand{\XX}{\textcolor{red}{XX}\xspace} + +\newcommand{\note}[3]{{\color{#2}[#1: #3]}} +\newcommand{\ves}[1]{\note{ves}{red}{#1}} +\newcommand{\luke}[1]{\note{luke}{green}{#1}} +\newcommand{\myle}[1]{\note{myle}{cyan}{#1}} +\newcommand{\paco}[1]{\note{paco}{blue}{#1}} +\newcommand{\eg}[1]{\note{edouard}{orange}{#1}} +\newcommand{\kk}[1]{\note{kartikay}{pink}{#1}} + +\renewcommand{\UrlFont}{\scriptsize} +\title{Unsupervised Cross-lingual Representation Learning at Scale} + +\author{Alexis Conneau\thanks{\ \ Equal contribution.} \space\space\space + Kartikay Khandelwal\footnotemark[1] \space\space\space \AND + \bf Naman Goyal \space\space\space + Vishrav Chaudhary \space\space\space + Guillaume Wenzek \space\space\space + Francisco Guzm\'an \space\space\space \AND + \bf Edouard Grave \space\space\space + Myle Ott \space\space\space + Luke Zettlemoyer \space\space\space + Veselin Stoyanov \space\space\space \\ \\ \\ + \bf Facebook AI + } + +\date{} + +\begin{document} +\maketitle +\begin{abstract} +This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed \xlmr, significantly outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +14.6\% average accuracy on XNLI, +13\% average F1 score on MLQA, and +2.4\% F1 score on NER. \xlmr performs particularly well on low-resource languages, improving 15.7\% in XNLI accuracy for Swahili and 11.4\% for Urdu over previous XLM models. We also present a detailed empirical analysis of the key factors that are required to achieve these gains, including the trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing per-language performance; \xlmr is very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We will make our code, data and models publicly available.{\let\thefootnote\relax\footnotetext{\scriptsize Correspondence to {\tt \{aconneau,kartikayk\}@fb.com}}}\footnote{\url{https://github.com/facebookresearch/(fairseq-py,pytext,xlm)}} +\end{abstract} + + +\section{Introduction} + +The goal of this paper is to improve cross-lingual language understanding (XLU), by carefully studying the effects of training unsupervised cross-lingual representations at a very large scale. +We present \xlmr a transformer-based multilingual masked language model pre-trained on text in 100 languages, which obtains state-of-the-art performance on cross-lingual classification, sequence labeling and question answering. + +Multilingual masked language models (MLM) like \mbert ~\cite{devlin2018bert} and XLM \cite{lample2019cross} have pushed the state-of-the-art on cross-lingual understanding tasks by jointly pretraining large Transformer models~\cite{transformer17} on many languages. These models allow for effective cross-lingual transfer, as seen in a number of benchmarks including cross-lingual natural language inference ~\cite{bowman2015large,williams2017broad,conneau2018xnli}, question answering~\cite{rajpurkar-etal-2016-squad,lewis2019mlqa}, and named entity recognition~\cite{Pires2019HowMI,wu2019beto}. +However, all of these studies pre-train on Wikipedia, which provides a relatively limited scale especially for lower resource languages. + + +In this paper, we first present a comprehensive analysis of the trade-offs and limitations of multilingual language models at scale, inspired by recent monolingual scaling efforts~\cite{roberta2019}. +We measure the trade-off between high-resource and low-resource languages and the impact of language sampling and vocabulary size. +%By training models with an increasing number of languages, +The experiments expose a trade-off as we scale the number of languages for a fixed model capacity: more languages leads to better cross-lingual performance on low-resource languages up until a point, after which the overall performance on monolingual and cross-lingual benchmarks degrades. We refer to this tradeoff as the \emph{curse of multilinguality}, and show that it can be alleviated by simply increasing model capacity. +We argue, however, that this remains an important limitation for future XLU systems which may aim to improve performance with more modest computational budgets. + +Our best model XLM-RoBERTa (\xlmr) outperforms \mbert on cross-lingual classification by up to 23\% accuracy on low-resource languages. +%like Swahili and Urdu. +It outperforms the previous state of the art by 5.1\% average accuracy on XNLI, 2.42\% average F1-score on Named Entity Recognition, and 9.1\% average F1-score on cross-lingual Question Answering. We also evaluate monolingual fine tuning on the GLUE and XNLI benchmarks, where \xlmr obtains results competitive with state-of-the-art monolingual models, including RoBERTa \cite{roberta2019}. +These results demonstrate, for the first time, that it is possible to have a single large model for all languages, without sacrificing per-language performance. +We will make our code, models and data publicly available, with the hope that this will help research in multilingual NLP and low-resource language understanding. + +\section{Related Work} +From pretrained word embeddings~\citep{mikolov2013distributed, pennington2014glove} to pretrained contextualized representations~\citep{peters2018deep,schuster2019cross} and transformer based language models~\citep{radford2018improving,devlin2018bert}, unsupervised representation learning has significantly improved the state of the art in natural language understanding. Parallel work on cross-lingual understanding~\citep{mikolov2013exploiting,schuster2019cross,lample2019cross} extends these systems to more languages and to the cross-lingual setting in which a model is learned in one language and applied in other languages. + +Most recently, \citet{devlin2018bert} and \citet{lample2019cross} introduced \mbert and XLM - masked language models trained on multiple languages, without any cross-lingual supervision. +\citet{lample2019cross} propose translation language modeling (TLM) as a way to leverage parallel data and obtain a new state of the art on the cross-lingual natural language inference (XNLI) benchmark~\cite{conneau2018xnli}. +They further show strong improvements on unsupervised machine translation and pretraining for sequence generation. \citet{wu2019emerging} shows that monolingual BERT representations are similar across languages, explaining in part the natural emergence of multilinguality in bottleneck architectures. Separately, \citet{Pires2019HowMI} demonstrated the effectiveness of multilingual models like \mbert on sequence labeling tasks. \citet{huang2019unicoder} showed gains over XLM using cross-lingual multi-task learning, and \citet{singh2019xlda} demonstrated the efficiency of cross-lingual data augmentation for cross-lingual NLI. However, all of this work was at a relatively modest scale, in terms of the amount of training data, as compared to our approach. + +\insertWikivsCC + +The benefits of scaling language model pretraining by increasing the size of the model as well as the training data has been extensively studied in the literature. For the monolingual case, \citet{jozefowicz2016exploring} show how large-scale LSTM models can obtain much stronger performance on language modeling benchmarks when trained on billions of tokens. +%[Kartikay: TODO; CHange the reference to GPT2] +GPT~\cite{radford2018improving} also highlights the importance of scaling the amount of data and RoBERTa \cite{roberta2019} shows that training BERT longer on more data leads to significant boost in performance. Inspired by RoBERTa, we show that mBERT and XLM are undertuned, and that simple improvements in the learning procedure of unsupervised MLM leads to much better performance. We train on cleaned CommonCrawls~\cite{wenzek2019ccnet}, which increase the amount of data for low-resource languages by two orders of magnitude on average. Similar data has also been shown to be effective for learning high quality word embeddings in multiple languages~\cite{grave2018learning}. + + +Several efforts have trained massively multilingual machine translation models from large parallel corpora. They uncover the high and low resource trade-off and the problem of capacity dilution~\citep{johnson2017google,tan2019multilingual}. The work most similar to ours is \citet{arivazhagan2019massively}, which trains a single model in 103 languages on over 25 billion parallel sentences. +\citet{siddhant2019evaluating} further analyze the representations obtained by the encoder of a massively multilingual machine translation system and show that it obtains similar results to mBERT on cross-lingual NLI. +%, which performs much wors that the XLM models we study. +Our work, in contrast, focuses on the unsupervised learning of cross-lingual representations and their transfer to discriminative tasks. + + +\section{Model and Data} +\label{sec:model+data} + +In this section, we present the training objective, languages, and data we use. We follow the XLM approach~\cite{lample2019cross} as closely as possible, only introducing changes that improve performance at scale. + +\paragraph{Masked Language Models.} +We use a Transformer model~\cite{transformer17} trained with the multilingual MLM objective~\cite{devlin2018bert,lample2019cross} using only monolingual data. We sample streams of text from each language and train the model to predict the masked tokens in the input. +We apply subword tokenization directly on raw text data using Sentence Piece~\cite{kudo2018sentencepiece} with a unigram language model~\cite{kudo2018subword}. We sample batches from different languages using the same sampling distribution as \citet{lample2019cross}, but with $\alpha=0.3$. Unlike \citet{lample2019cross}, we do not use language embeddings, which allows our model to better deal with code-switching. We use a large vocabulary size of 250K with a full softmax and train two different models: \xlmr\textsubscript{Base} (L = 12, H = 768, A = 12, 270M params) and \xlmr (L = 24, H = 1024, A = 16, 550M params). For all of our ablation studies, we use a BERT\textsubscript{Base} architecture with a vocabulary of 150K tokens. Appendix~\ref{sec:appendix_B} goes into more details about the architecture of the different models referenced in this paper. + +\paragraph{Scaling to a hundred languages.} +\xlmr is trained on 100 languages; +we provide a full list of languages and associated statistics in Appendix~\ref{sec:appendix_A}. Figure~\ref{fig:wikivscc} specifies the iso codes of 88 languages that are shared across \xlmr and XLM-100, the model from~\citet{lample2019cross} trained on Wikipedia text in 100 languages. + +Compared to previous work, we replace some languages with more commonly used ones such as romanized Hindi and traditional Chinese. In our ablation studies, we always include the 7 languages for which we have classification and sequence labeling evaluation benchmarks: English, French, German, Russian, Chinese, Swahili and Urdu. We chose this set as it covers a suitable range of language families and includes low-resource languages such as Swahili and Urdu. +We also consider larger sets of 15, 30, 60 and all 100 languages. When reporting results on high-resource and low-resource, we refer to the average of English and French results, and the average of Swahili and Urdu results respectively. + +\paragraph{Scaling the Amount of Training Data.} +Following~\citet{wenzek2019ccnet}~\footnote{\url{https://github.com/facebookresearch/cc_net}}, we build a clean CommonCrawl Corpus in 100 languages. We use an internal language identification model in combination with the one from fastText~\cite{joulin2017bag}. We train language models in each language and use it to filter documents as described in \citet{wenzek2019ccnet}. We consider one CommonCrawl dump for English and twelve dumps for all other languages, which significantly increases dataset sizes, especially for low-resource languages like Burmese and Swahili. + +Figure~\ref{fig:wikivscc} shows the difference in size between the Wikipedia Corpus used by mBERT and XLM-100, and the CommonCrawl Corpus we use. As we show in Section~\ref{sec:multimono}, monolingual Wikipedia corpora are too small to enable unsupervised representation learning. Based on our experiments, we found that a few hundred MiB of text data is usually a minimal size for learning a BERT model. + +\section{Evaluation} +We consider four evaluation benchmarks. +For cross-lingual understanding, we use cross-lingual natural language inference, named entity recognition, and question answering. We use the GLUE benchmark to evaluate the English performance of \xlmr and compare it to other state-of-the-art models. + +\paragraph{Cross-lingual Natural Language Inference (XNLI).} +The XNLI dataset comes with ground-truth dev and test sets in 15 languages, and a ground-truth English training set. The training set has been machine-translated to the remaining 14 languages, providing synthetic training data for these languages as well. We evaluate our model on cross-lingual transfer from English to other languages. We also consider three machine translation baselines: (i) \textit{translate-test}: dev and test sets are machine-translated to English and a single English model is used (ii) \textit{translate-train} (per-language): the English training set is machine-translated to each language and we fine-tune a multiligual model on each training set (iii) \textit{translate-train-all} (multi-language): we fine-tune a multilingual model on the concatenation of all training sets from translate-train. For the translations, we use the official data provided by the XNLI project. +% In case we want to add more details about the CC-100 corpora : We train language models in each language and use it to filter documents as described in Wenzek et al. (2019). We additionally apply a filter based on type-token ratio score of 0.6. We consider one CommonCrawl snapshot (December, 2018) for English and twelve snapshots from all months of 2018 for all other languages, which significantly increases dataset sizes, especially for low-resource languages like Burmese and Swahili. + +\paragraph{Named Entity Recognition.} +% WikiAnn http://nlp.cs.rpi.edu/wikiann/ +For NER, we consider the CoNLL-2002~\cite{sang2002introduction} and CoNLL-2003~\cite{tjong2003introduction} datasets in English, Dutch, Spanish and German. We fine-tune multilingual models either (1) on the English set to evaluate cross-lingual transfer, (2) on each set to evaluate per-language performance, or (3) on all sets to evaluate multilingual learning. We report the F1 score, and compare to baselines from \citet{lample-etal-2016-neural} and \citet{akbik2018coling}. + +\paragraph{Cross-lingual Question Answering.} +We use the MLQA benchmark from \citet{lewis2019mlqa}, which extends the English SQuAD benchmark to Spanish, German, Arabic, Hindi, Vietnamese and Chinese. We report the F1 score as well as the exact match (EM) score for cross-lingual transfer from English. + +\paragraph{GLUE Benchmark.} +Finally, we evaluate the English performance of our model on the GLUE benchmark~\cite{wang2018glue} which gathers multiple classification tasks, such as MNLI~\cite{williams2017broad}, SST-2~\cite{socher2013recursive}, or QNLI~\cite{rajpurkar2018know}. We use BERT\textsubscript{Large} and RoBERTa as baselines. + +\section{Analysis and Results} +\label{sec:analysis} + +In this section, we perform a comprehensive analysis of multilingual masked language models. We conduct most of the analysis on XNLI, which we found to be representative of our findings on other tasks. We then present the results of \xlmr on cross-lingual understanding and GLUE. Finally, we compare multilingual and monolingual models, and present results on low-resource languages. + +\subsection{Improving and Understanding Multilingual Masked Language Models} +% prior analysis necessary to build \xlmr +\insertAblationone +\insertAblationtwo + +Much of the work done on understanding the cross-lingual effectiveness of \mbert or XLM~\cite{Pires2019HowMI,wu2019beto,lewis2019mlqa} has focused on analyzing the performance of fixed pretrained models on downstream tasks. In this section, we present a comprehensive study of different factors that are important to \textit{pretraining} large scale multilingual models. We highlight the trade-offs and limitations of these models as we scale to one hundred languages. + +\paragraph{Transfer-dilution Trade-off and Curse of Multilinguality.} +Model capacity (i.e. the number of parameters in the model) is constrained due to practical considerations such as memory and speed during training and inference. For a fixed sized model, the per-language capacity decreases as we increase the number of languages. While low-resource language performance can be improved by adding similar higher-resource languages during pretraining, the overall downstream performance suffers from this capacity dilution~\cite{arivazhagan2019massively}. Positive transfer and capacity dilution have to be traded off against each other. + +We illustrate this trade-off in Figure~\ref{fig:transfer_dilution}, which shows XNLI performance vs the number of languages the model is pretrained on. Initially, as we go from 7 to 15 languages, the model is able to take advantage of positive transfer which improves performance, especially on low resource languages. Beyond this point the {\em curse of multilinguality} +kicks in and degrades performance across all languages. Specifically, the overall XNLI accuracy decreases from 71.8\% to 67.7\% as we go from XLM-7 to XLM-100. The same trend can be observed for models trained on the larger CommonCrawl Corpus. + +The issue is even more prominent when the capacity of the model is small. To show this, we pretrain models on Wikipedia Data in 7, 30 and 100 languages. As we add more languages, we make the Transformer wider by increasing the hidden size from 768 to 960 to 1152. In Figure~\ref{fig:capacity}, we show that the added capacity allows XLM-30 to be on par with XLM-7, thus overcoming the curse of multilinguality. The added capacity for XLM-100, however, is not enough +and it still lags behind due to higher vocabulary dilution (recall from Section~\ref{sec:model+data} that we used a fixed vocabulary size of 150K for all models). + +\paragraph{High-resource vs Low-resource Trade-off.} +The allocation of the model capacity across languages is controlled by several parameters: the training set size, the size of the shared subword vocabulary, and the rate at which we sample training examples from each language. We study the effect of sampling on the performance of high-resource (English and French) and low-resource (Swahili and Urdu) languages for an XLM-100 model trained on Wikipedia (we observe a similar trend for the construction of the subword vocab). Specifically, we investigate the impact of varying the $\alpha$ parameter which controls the exponential smoothing of the language sampling rate. Similar to~\citet{lample2019cross}, we use a sampling rate proportional to the number of sentences in each corpus. Models trained with higher values of $\alpha$ see batches of high-resource languages more often. +Figure~\ref{fig:alpha} shows that the higher the value of $\alpha$, the better the performance on high-resource languages, and vice-versa. When considering overall performance, we found $0.3$ to be an optimal value for $\alpha$, and use this for \xlmr. + +\paragraph{Importance of Capacity and Vocabulary.} +In previous sections and in Figure~\ref{fig:capacity}, we showed the importance of scaling the model size as we increase the number of languages. Similar to the overall model size, we argue that scaling the size of the shared vocabulary (the vocabulary capacity) can improve the performance of multilingual models on downstream tasks. To illustrate this effect, we train XLM-100 models on Wikipedia data with different vocabulary sizes. We keep the overall number of parameters constant by adjusting the width of the transformer. Figure~\ref{fig:vocab} shows that even with a fixed capacity, we observe a 2.8\% increase in XNLI average accuracy as we increase the vocabulary size from 32K to 256K. This suggests that multilingual models can benefit from allocating a higher proportion of the total number of parameters to the embedding layer even though this reduces the size of the Transformer. +%With bigger models, we believe that using a vocabulary of up to 2 million tokens with an adaptive softmax~\cite{grave2017efficient,baevski2018adaptive} should improve performance even further, but we leave this exploration to future work. +For simplicity and given the softmax computational constraints, we use a vocabulary of 250k for \xlmr. + +We further illustrate the importance of this parameter, by training three models with the same transformer architecture (BERT\textsubscript{Base}) but with different vocabulary sizes: 128K, 256K and 512K. We observe more than 3\% gains in overall accuracy on XNLI by simply increasing the vocab size from 128k to 512k. + +\paragraph{Larger-scale Datasets and Training.} +As shown in Figure~\ref{fig:wikivscc}, the CommonCrawl Corpus that we collected has significantly more monolingual data than the previously used Wikipedia corpora. Figure~\ref{fig:curse} shows that for the same BERT\textsubscript{Base} architecture, all models trained on CommonCrawl obtain significantly better performance. + +Apart from scaling the training data, \citet{roberta2019} also showed the benefits of training MLMs longer. In our experiments, we observed similar effects of large-scale training, such as increasing batch size (see Figure~\ref{fig:batch}) and training time, on model performance. Specifically, we found that using validation perplexity as a stopping criterion for pretraining caused the multilingual MLM in \citet{lample2019cross} to be under-tuned. In our experience, performance on downstream tasks continues to improve even after validation perplexity has plateaued. Combining this observation with our implementation of the unsupervised XLM-MLM objective, we were able to improve the performance of \citet{lample2019cross} from 71.3\% to more than 75\% average accuracy on XNLI, which was on par with their supervised translation language modeling (TLM) objective. Based on these results, and given our focus on unsupervised learning, we decided to not use the supervised TLM objective for training our models. + + +\paragraph{Simplifying Multilingual Tokenization with Sentence Piece.} +The different language-specific tokenization tools +used by mBERT and XLM-100 make these models more difficult to use on raw text. Instead, we train a Sentence Piece model (SPM) and apply it directly on raw text data for all languages. We did not observe any loss in performance for models trained with SPM when compared to models trained with language-specific preprocessing and byte-pair encoding (see Figure~\ref{fig:batch}) and hence use SPM for \xlmr. + +\subsection{Cross-lingual Understanding Results} +Based on these results, we adapt the setting of \citet{lample2019cross} and use a large Transformer model with 24 layers and 1024 hidden states, with a 250k vocabulary. We use the multilingual MLM loss and train our \xlmr model for 1.5 Million updates on five-hundred 32GB Nvidia V100 GPUs with a batch size of 8192. We leverage the SPM-preprocessed text data from CommonCrawl in 100 languages and sample languages with $\alpha=0.3$. In this section, we show that it outperforms all previous techniques on cross-lingual benchmarks while getting performance on par with RoBERTa on the GLUE benchmark. + + +\insertXNLItable + +\paragraph{XNLI.} +Table~\ref{tab:xnli} shows XNLI results and adds some additional details: (i) the number of models the approach induces (\#M), (ii) the data on which the model was trained (D), and (iii) the number of languages the model was pretrained on (\#lg). As we show in our results, these parameters significantly impact performance. Column \#M specifies whether model selection was done separately on the dev set of each language ($N$ models), or on the joint dev set of all the languages (single model). We observe a 0.6 decrease in overall accuracy when we go from $N$ models to a single model - going from 71.3 to 70.7. We encourage the community to adopt this setting. For cross-lingual transfer, while this approach is not fully zero-shot transfer, we argue that in real applications, a small amount of supervised data is often available for validation in each language. + +\xlmr sets a new state of the art on XNLI. +On cross-lingual transfer, \xlmr obtains 80.9\% accuracy, outperforming the XLM-100 and \mbert open-source models by 10.2\% and 14.6\% average accuracy. On the Swahili and Urdu low-resource languages, \xlmr outperforms XLM-100 by 15.7\% and 11.4\%, and \mbert by 23.5\% and 15.8\%. While \xlmr handles 100 languages, we also show that it outperforms the former state of the art Unicoder~\citep{huang2019unicoder} and XLM (MLM+TLM), which handle only 15 languages, by 5.5\% and 5.8\% average accuracy respectively. Using the multilingual training of translate-train-all, \xlmr further improves performance and reaches 83.6\% accuracy, a new overall state of the art for XNLI, outperforming Unicoder by 5.1\%. Multilingual training is similar to practical applications where training sets are available in various languages for the same task. In the case of XNLI, datasets have been translated, and translate-train-all can be seen as some form of cross-lingual data augmentation~\cite{singh2019xlda}, similar to back-translation~\cite{xie2019unsupervised}. + +\insertNER +\paragraph{Named Entity Recognition.} +In Table~\ref{tab:ner}, we report results of \xlmr and \mbert on CoNLL-2002 and CoNLL-2003. We consider the LSTM + CRF approach from \citet{lample-etal-2016-neural} and the Flair model from \citet{akbik2018coling} as baselines. We evaluate the performance of the model on each of the target languages in three different settings: (i) train on English data only (en) (ii) train on data in target language (each) (iii) train on data in all languages (all). Results of \mbert are reported from \citet{wu2019beto}. Note that we do not use a linear-chain CRF on top of \xlmr and \mbert representations, which gives an advantage to \citet{akbik2018coling}. Without the CRF, our \xlmr model still performs on par with the state of the art, outperforming \citet{akbik2018coling} on Dutch by $2.09$ points. On this task, \xlmr also outperforms \mbert by 2.42 F1 on average for cross-lingual transfer, and 1.86 F1 when trained on each language. Training on all languages leads to an average F1 score of 89.43\%, outperforming cross-lingual transfer approach by 8.49\%. + +\paragraph{Question Answering.} +We also obtain new state of the art results on the MLQA cross-lingual question answering benchmark, introduced by \citet{lewis2019mlqa}. We follow their procedure by training on the English training data and evaluating on the 7 languages of the dataset. +We report results in Table~\ref{tab:mlqa}. +\xlmr obtains F1 and accuracy scores of 70.7\% and 52.7\% while the previous state of the art was 61.6\% and 43.5\%. \xlmr also outperforms \mbert by 13.0\% F1-score and 11.1\% accuracy. It even outperforms BERT-Large on English, confirming its strong monolingual performance. + +\insertMLQA + +\subsection{Multilingual versus Monolingual} +\label{sec:multimono} +In this section, we present results of multilingual XLM models against monolingual BERT models. + +\paragraph{GLUE: \xlmr versus RoBERTa.} +Our goal is to obtain a multilingual model with strong performance on both, cross-lingual understanding tasks as well as natural language understanding tasks for each language. To that end, we evaluate \xlmr on the GLUE benchmark. We show in Table~\ref{tab:glue}, that \xlmr obtains better average dev performance than BERT\textsubscript{Large} by 1.6\% and reaches performance on par with XLNet\textsubscript{Large}. The RoBERTa model outperforms \xlmr by only 1.0\% on average. We believe future work can reduce this gap even further by alleviating the curse of multilinguality and vocabulary dilution. These results demonstrate the possibility of learning one model for many languages while maintaining strong performance on per-language downstream tasks. + +\insertGlue + +\paragraph{XNLI: XLM versus BERT.} +A recurrent criticism against multilingual models is that they obtain worse performance than their monolingual counterparts. In addition to the comparison of \xlmr and RoBERTa, we provide the first comprehensive study to assess this claim on the XNLI benchmark. We extend our comparison between multilingual XLM models and monolingual BERT models on 7 languages and compare performance in Table~\ref{tab:multimono}. We train 14 monolingual BERT models on Wikipedia and CommonCrawl (capped at 60 GiB), +%\footnote{For simplicity, we use a reduced version of our corpus by capping the size of each monolingual dataset to 60 GiB.} +and two XLM-7 models. We increase the vocabulary size of the multilingual model for a better comparison. +% To our surprise - and backed by further study on internal benchmarks - +We found that \textit{multilingual models can outperform their monolingual BERT counterparts}. Specifically, in Table~\ref{tab:multimono}, we show that for cross-lingual transfer, monolingual baselines outperform XLM-7 for both Wikipedia and CC by 1.6\% and 1.3\% average accuracy. However, by making use of multilingual training (translate-train-all) and leveraging training sets coming from multiple languages, XLM-7 can outperform the BERT models: our XLM-7 trained on CC obtains 80.0\% average accuracy on the 7 languages, while the average performance of BERT models trained on CC is 77.5\%. This is a surprising result that shows that the capacity of multilingual models to leverage training data coming from multiple languages for a particular task can overcome the capacity dilution problem to obtain better overall performance. + + +\insertMultiMono + +\subsection{Representation Learning for Low-resource Languages} +We observed in Table~\ref{tab:multimono} that pretraining on Wikipedia for Swahili and Urdu performed similarly to a randomly initialized model; most likely due to the small size of the data for these languages. On the other hand, pretraining on CC improved performance by up to 10 points. This confirms our assumption that mBERT and XLM-100 rely heavily on cross-lingual transfer but do not model the low-resource languages as well as \xlmr. Specifically, in the translate-train-all setting, we observe that the biggest gains for XLM models trained on CC, compared to their Wikipedia counterparts, are on low-resource languages; 7\% and 4.8\% improvement on Swahili and Urdu respectively. + +\section{Conclusion} +In this work, we introduced \xlmr, our new state of the art multilingual masked language model trained on 2.5 TB of newly created clean CommonCrawl data in 100 languages. We show that it provides strong gains over previous multilingual models like \mbert and XLM on classification, sequence labeling and question answering. We exposed the limitations of multilingual MLMs, in particular by uncovering the high-resource versus low-resource trade-off, the curse of multilinguality and the importance of key hyperparameters. We also expose the surprising effectiveness of multilingual models over monolingual models, and show strong improvements on low-resource languages. +% \section*{Acknowledgements} + + +\bibliography{acl2020} +\bibliographystyle{acl_natbib} + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% DELETE THIS PART. DO NOT PLACE CONTENT AFTER THE REFERENCES! +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + \newpage + \clearpage + \appendix + \onecolumn + \section*{Appendix} + \section{Languages and statistics for CC-100 used by \xlmr} + In this section we present the list of languages in the CC-100 corpus we created for training \xlmr. We also report statistics such as the number of tokens and the size of each monolingual corpus. + \label{sec:appendix_A} + \insertDataStatistics + +% \newpage + \section{Model Architectures and Sizes} + As we showed in section~\ref{sec:analysis}, capacity is an important parameter for learning strong cross-lingual representations. In the table below, we list multiple monolingual and multilingual models used by the research community and summarize their architectures and total number of parameters. + \label{sec:appendix_B} + +\insertParameters + +% \section{Do \emph{not} have an appendix here} + +% \textbf{\emph{Do not put content after the references.}} +% % +% Put anything that you might normally include after the references in a separate +% supplementary file. + +% We recommend that you build supplementary material in a separate document. +% If you must create one PDF and cut it up, please be careful to use a tool that +% doesn't alter the margins, and that doesn't aggressively rewrite the PDF file. +% pdftk usually works fine. + +% \textbf{Please do not use Apple's preview to cut off supplementary material.} In +% previous years it has altered margins, and created headaches at the camera-ready +% stage. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 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Bowman}, + Booktitle = {NAACL}, + year = {2017} +} + +@article{paszke2017automatic, + title={Automatic differentiation in pytorch}, + author={Paszke, Adam and Gross, Sam and Chintala, Soumith and Chanan, Gregory and Yang, Edward and DeVito, Zachary and Lin, Zeming and Desmaison, Alban and Antiga, Luca and Lerer, Adam}, + journal={NIPS 2017 Autodiff Workshop}, + year={2017} +} + +@inproceedings{conneau2018craminto, + title={What you can cram into a single vector: Probing sentence embeddings for linguistic properties}, + author={Conneau, Alexis and Kruszewski, German and Lample, Guillaume and Barrault, Lo{\"\i}c and Baroni, Marco}, + booktitle = {ACL}, + year={2018} +} + +@inproceedings{Conneau:2018:iclr_muse, + title={Word Translation without Parallel Data}, + author={Alexis Conneau and Guillaume Lample and {Marc'Aurelio} Ranzato and Ludovic Denoyer and Hervé Jegou}, + booktitle = {ICLR}, + year={2018} +} + +@article{johnson2017google, + title={Google’s multilingual neural machine translation system: Enabling zero-shot translation}, + author={Johnson, Melvin and Schuster, Mike and Le, Quoc V and Krikun, Maxim and Wu, Yonghui and Chen, Zhifeng and Thorat, Nikhil and Vi{\'e}gas, Fernanda and Wattenberg, Martin and Corrado, Greg and others}, + journal={TACL}, + volume={5}, + pages={339--351}, + year={2017}, + publisher={MIT Press} +} + +@article{radford2019language, + title={Language models are unsupervised multitask learners}, + author={Radford, Alec and Wu, Jeffrey and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya}, + journal={OpenAI Blog}, + volume={1}, + number={8}, + year={2019} +} + +@inproceedings{unsupNMTlample, +title = {Unsupervised machine translation using monolingual corpora only}, +author = {Lample, Guillaume and Conneau, Alexis and Denoyer, Ludovic and Ranzato, Marc'Aurelio}, +booktitle = {ICLR}, +year = {2018} +} + +@inproceedings{lample2018phrase, + title={Phrase-Based \& Neural Unsupervised Machine Translation}, + author={Lample, Guillaume and Ott, Myle and Conneau, Alexis and Denoyer, Ludovic and Ranzato, Marc'Aurelio}, + booktitle={EMNLP}, + year={2018} +} + +@article{hendrycks2016bridging, + title={Bridging nonlinearities and stochastic regularizers with Gaussian error linear units}, + author={Hendrycks, Dan and Gimpel, Kevin}, + journal={arXiv preprint arXiv:1606.08415}, + year={2016} +} + +@inproceedings{chang2008optimizing, + title={Optimizing Chinese word segmentation for machine translation performance}, + author={Chang, Pi-Chuan and Galley, Michel and Manning, Christopher D}, + booktitle={Proceedings of the third workshop on statistical machine translation}, + pages={224--232}, + year={2008} +} + +@inproceedings{rajpurkar-etal-2016-squad, + title = "{SQ}u{AD}: 100,000+ Questions for Machine Comprehension of Text", + author = "Rajpurkar, Pranav and + Zhang, Jian and + Lopyrev, Konstantin and + Liang, Percy", + booktitle = "EMNLP", + month = nov, + year = "2016", + address = "Austin, Texas", + publisher = "Association for Computational Linguistics", + url = "https://www.aclweb.org/anthology/D16-1264", + doi = "10.18653/v1/D16-1264", + pages = "2383--2392", +} + +@article{lewis2019mlqa, + title={MLQA: Evaluating Cross-lingual Extractive Question Answering}, + author={Lewis, Patrick and O\u{g}uz, Barlas and Rinott, Ruty and Riedel, Sebastian and Schwenk, Holger}, + journal={arXiv preprint arXiv:1910.07475}, + year={2019} +} + +@inproceedings{sennrich2015neural, + title={Neural machine translation of rare words with subword units}, + author={Sennrich, Rico and Haddow, Barry and Birch, Alexandra}, + booktitle={Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics}, + pages = {1715-1725}, + year={2015} +} + +@article{eriguchi2018zero, + title={Zero-shot cross-lingual classification using multilingual neural machine translation}, + author={Eriguchi, Akiko and Johnson, Melvin and Firat, Orhan and Kazawa, Hideto and Macherey, Wolfgang}, + journal={arXiv preprint arXiv:1809.04686}, + year={2018} +} + +@article{smith2017offline, + title={Offline bilingual word vectors, orthogonal transformations and the inverted softmax}, + author={Smith, Samuel L and Turban, David HP and Hamblin, Steven and Hammerla, Nils Y}, + journal={International Conference on Learning Representations}, + year={2017} +} + +@article{artetxe2016learning, + title={Learning principled bilingual mappings of word embeddings while preserving monolingual invariance}, + author={Artetxe, Mikel and Labaka, Gorka and Agirre, Eneko}, + journal={Proceedings of EMNLP}, + year={2016} +} + +@article{ammar2016massively, + title={Massively multilingual word embeddings}, + author={Ammar, Waleed and Mulcaire, George and Tsvetkov, Yulia and Lample, Guillaume and Dyer, Chris and Smith, Noah A}, + journal={arXiv preprint arXiv:1602.01925}, + year={2016} +} + +@article{marcobaroni2015hubness, + title={Hubness and pollution: Delving into cross-space mapping for zero-shot learning}, + author={Lazaridou, Angeliki and Dinu, Georgiana and Baroni, Marco}, + journal={Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics}, + year={2015} +} + +@article{xing2015normalized, + title={Normalized Word Embedding and Orthogonal Transform for Bilingual Word Translation}, + author={Xing, Chao and Wang, Dong and Liu, Chao and Lin, Yiye}, + journal={Proceedings of NAACL}, + year={2015} +} + +@article{faruqui2014improving, + title={Improving Vector Space Word Representations Using Multilingual Correlation}, + author={Faruqui, Manaal and Dyer, Chris}, + journal={Proceedings of EACL}, + year={2014} +} + +@article{taylor1953cloze, + title={“Cloze procedure”: A new tool for measuring readability}, + author={Taylor, Wilson L}, + journal={Journalism Bulletin}, + volume={30}, + number={4}, + pages={415--433}, + year={1953}, + publisher={SAGE Publications Sage CA: Los Angeles, CA} +} + +@inproceedings{mikolov2013distributed, + title={Distributed representations of words and phrases and their compositionality}, + author={Mikolov, Tomas and Sutskever, Ilya and Chen, Kai and Corrado, Greg S and Dean, Jeff}, + booktitle={NIPS}, + pages={3111--3119}, + year={2013} +} + +@article{mikolov2013exploiting, + title={Exploiting similarities among languages for machine translation}, + author={Mikolov, Tomas and Le, Quoc V and Sutskever, Ilya}, + journal={arXiv preprint arXiv:1309.4168}, + year={2013} +} + +@article{artetxe2018massively, + title={Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond}, + author={Artetxe, Mikel and Schwenk, Holger}, + journal={arXiv preprint arXiv:1812.10464}, + year={2018} +} + +@article{williams2017broad, + title={A broad-coverage challenge corpus for sentence understanding through inference}, + author={Williams, Adina and Nangia, Nikita and Bowman, Samuel R}, + journal={Proceedings of the 2nd Workshop on Evaluating Vector-Space Representations for NLP}, + year={2017} +} + +@InProceedings{conneau2018xnli, + author = "Conneau, Alexis + and Rinott, Ruty + and Lample, Guillaume + and Williams, Adina + and Bowman, Samuel R. + and Schwenk, Holger + and Stoyanov, Veselin", + title = "XNLI: Evaluating Cross-lingual Sentence Representations", + booktitle = "EMNLP", + year = "2018", + publisher = "Association for Computational Linguistics", + location = "Brussels, Belgium", +} + +@article{wada2018unsupervised, + title={Unsupervised Cross-lingual Word Embedding by Multilingual Neural Language Models}, + author={Wada, Takashi and Iwata, Tomoharu}, + journal={arXiv preprint arXiv:1809.02306}, + year={2018} +} + +@article{xu2013cross, + title={Cross-lingual language modeling for low-resource speech recognition}, + author={Xu, Ping and Fung, Pascale}, + journal={IEEE Transactions on Audio, Speech, and Language Processing}, + volume={21}, + number={6}, + pages={1134--1144}, + year={2013}, + publisher={IEEE} +} + +@article{hermann2014multilingual, + title={Multilingual models for compositional distributed semantics}, + author={Hermann, Karl Moritz and Blunsom, Phil}, + journal={arXiv preprint arXiv:1404.4641}, + year={2014} +} + +@inproceedings{transformer17, +title = {Attention is all you need}, +author = {Ashish Vaswani and Noam Shazeer and Niki Parmar and Jakob Uszkoreit and Llion Jones and Aidan N. Gomez and Lukasz Kaiser and Illia Polosukhin}, +booktitle={Advances in Neural Information Processing Systems}, +pages={6000--6010}, +year = {2017} +} + +@article{liu2019multi, + title={Multi-task deep neural networks for natural language understanding}, + author={Liu, Xiaodong and He, Pengcheng and Chen, Weizhu and Gao, Jianfeng}, + journal={arXiv preprint arXiv:1901.11504}, + year={2019} +} + +@article{wang2018glue, + title={GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding}, + author={Wang, Alex and Singh, Amapreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R}, + journal={arXiv preprint arXiv:1804.07461}, + year={2018} +} + +@article{radford2018improving, + title={Improving language understanding by generative pre-training}, + author={Radford, Alec and Narasimhan, Karthik and Salimans, Tim and Sutskever, Ilya}, + journal={URL https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language\_understanding\_paper.pdf}, + url={https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf}, + year={2018} +} + +@article{conneau2018senteval, + title={SentEval: An Evaluation Toolkit for Universal Sentence Representations}, + author={Conneau, Alexis and Kiela, Douwe}, + journal={LREC}, + year={2018} +} + +@article{devlin2018bert, + title={Bert: Pre-training of deep bidirectional transformers for language understanding}, + author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina}, + journal={NAACL}, + year={2018} +} + +@article{peters2018deep, + title={Deep contextualized word representations}, + author={Peters, Matthew E and Neumann, Mark and Iyyer, Mohit and Gardner, Matt and Clark, Christopher and Lee, Kenton and Zettlemoyer, Luke}, + journal={NAACL}, + year={2018} +} + +@article{ramachandran2016unsupervised, + title={Unsupervised pretraining for sequence to sequence learning}, + author={Ramachandran, Prajit and Liu, Peter J and Le, Quoc V}, + journal={arXiv preprint arXiv:1611.02683}, + year={2016} +} + +@inproceedings{kunchukuttan2018iit, + title={The IIT Bombay English-Hindi Parallel Corpus}, + author={Kunchukuttan Anoop and Mehta Pratik and Bhattacharyya Pushpak}, + booktitle={LREC}, + year={2018} +} + +@article{wu2019beto, + title={Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT}, + author={Wu, Shijie and Dredze, Mark}, + journal={EMNLP}, + year={2019} +} + +@inproceedings{lample-etal-2016-neural, + title = "Neural Architectures for Named Entity Recognition", + author = "Lample, Guillaume and + Ballesteros, Miguel and + Subramanian, Sandeep and + Kawakami, Kazuya and + Dyer, Chris", + booktitle = "NAACL", + month = jun, + year = "2016", + address = "San Diego, California", + publisher = "Association for Computational Linguistics", + url = "https://www.aclweb.org/anthology/N16-1030", + doi = "10.18653/v1/N16-1030", + pages = "260--270", +} + +@inproceedings{akbik2018coling, + title={Contextual String Embeddings for Sequence Labeling}, + author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland}, + booktitle = {COLING}, + pages = {1638--1649}, + year = {2018} +} + +@inproceedings{tjong-kim-sang-de-meulder-2003-introduction, + title = "Introduction to the {C}o{NLL}-2003 Shared Task: Language-Independent Named Entity Recognition", + author = "Tjong Kim Sang, Erik F. and + De Meulder, Fien", + booktitle = "Proceedings of the Seventh Conference on Natural Language Learning at {HLT}-{NAACL} 2003", + year = "2003", + url = "https://www.aclweb.org/anthology/W03-0419", + pages = "142--147", +} + +@inproceedings{tjong-kim-sang-2002-introduction, + title = "Introduction to the {C}o{NLL}-2002 Shared Task: Language-Independent Named Entity Recognition", + author = "Tjong Kim Sang, Erik F.", + booktitle = "{COLING}-02: The 6th Conference on Natural Language Learning 2002 ({C}o{NLL}-2002)", + year = "2002", + url = "https://www.aclweb.org/anthology/W02-2024", +} + +@InProceedings{TIEDEMANN12.463, + author = {Jörg Tiedemann}, + title = {Parallel Data, Tools and Interfaces in OPUS}, + booktitle = {LREC}, + year = {2012}, + month = {may}, + date = {23-25}, + address = {Istanbul, Turkey}, + editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis}, + publisher = {European Language Resources Association (ELRA)}, + isbn = {978-2-9517408-7-7}, + language = {english} + } + +@inproceedings{ziemski2016united, + title={The United Nations Parallel Corpus v1. 0.}, + author={Ziemski, Michal and Junczys-Dowmunt, Marcin and Pouliquen, Bruno}, + booktitle={LREC}, + year={2016} +} + +@article{roberta2019, + author = {Yinhan Liu and + Myle Ott and + Naman Goyal and + Jingfei Du and + Mandar Joshi and + Danqi Chen and + Omer Levy and + Mike Lewis and + Luke Zettlemoyer and + Veselin Stoyanov}, + title = {RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach}, + journal = {arXiv preprint arXiv:1907.11692}, + year = {2019} +} + + +@article{tan2019multilingual, + title={Multilingual neural machine translation with knowledge distillation}, + author={Tan, Xu and Ren, Yi and He, Di and Qin, Tao and Zhao, Zhou and Liu, Tie-Yan}, + journal={ICLR}, + year={2019} +} + +@article{siddhant2019evaluating, + title={Evaluating the Cross-Lingual Effectiveness of Massively Multilingual Neural Machine Translation}, + author={Siddhant, Aditya and Johnson, Melvin and Tsai, Henry and Arivazhagan, Naveen and Riesa, Jason and Bapna, Ankur and Firat, Orhan and Raman, Karthik}, + journal={AAAI}, + year={2019} +} + +@inproceedings{camacho2017semeval, + title={Semeval-2017 task 2: Multilingual and cross-lingual semantic word similarity}, + author={Camacho-Collados, Jose and Pilehvar, Mohammad Taher and Collier, Nigel and Navigli, Roberto}, + booktitle={Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)}, + pages={15--26}, + year={2017} +} + +@inproceedings{Pires2019HowMI, + title={How Multilingual is Multilingual BERT?}, + author={Telmo Pires and Eva Schlinger and Dan Garrette}, + booktitle={ACL}, + year={2019} +} + +@article{lample2019cross, + title={Cross-lingual language model pretraining}, + author={Lample, Guillaume and Conneau, Alexis}, + journal={NeurIPS}, + year={2019} +} + +@article{schuster2019cross, + title={Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing}, + author={Schuster, Tal and Ram, Ori and Barzilay, Regina and Globerson, Amir}, + journal={NAACL}, + year={2019} +} + +@inproceedings{chang2008optimizing, + title={Optimizing Chinese word segmentation for machine translation performance}, + author={Chang, Pi-Chuan and Galley, Michel and Manning, Christopher D}, + booktitle={Proceedings of the third workshop on statistical machine translation}, + pages={224--232}, + year={2008} +} + +@inproceedings{koehn2007moses, + title={Moses: Open source toolkit for statistical machine translation}, + author={Koehn, Philipp and Hoang, Hieu and Birch, Alexandra and Callison-Burch, Chris and Federico, Marcello and Bertoldi, Nicola and Cowan, Brooke and Shen, Wade and Moran, Christine and Zens, Richard and others}, + booktitle={Proceedings of the 45th annual meeting of the ACL on interactive poster and demonstration sessions}, + pages={177--180}, + year={2007}, + organization={Association for Computational Linguistics} +} + +@article{wenzek2019ccnet, + title={CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data}, + author={Wenzek, Guillaume and Lachaux, Marie-Anne and Conneau, Alexis and Chaudhary, Vishrav and Guzman, Francisco and Joulin, Armand and Grave, Edouard}, + journal={arXiv preprint arXiv:1911.00359}, + year={2019} +} + +@inproceedings{zhou2016cross, + title={Cross-lingual sentiment classification with bilingual document representation learning}, + author={Zhou, Xinjie and Wan, Xiaojun and Xiao, Jianguo}, + booktitle={Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, + pages={1403--1412}, + year={2016} +} + +@article{goyal2017accurate, + title={Accurate, large minibatch sgd: Training imagenet in 1 hour}, + author={Goyal, Priya and Doll{\'a}r, Piotr and Girshick, Ross and Noordhuis, Pieter and Wesolowski, Lukasz and Kyrola, Aapo and Tulloch, Andrew and Jia, Yangqing and He, Kaiming}, + journal={arXiv preprint arXiv:1706.02677}, + year={2017} +} + +@article{arivazhagan2019massively, + title={Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges}, + author={Arivazhagan, Naveen and Bapna, Ankur and Firat, Orhan and Lepikhin, Dmitry and Johnson, Melvin and Krikun, Maxim and Chen, Mia Xu and Cao, Yuan and Foster, George and Cherry, Colin and others}, + journal={arXiv preprint arXiv:1907.05019}, + year={2019} +} + +@inproceedings{pan2017cross, + title={Cross-lingual name tagging and linking for 282 languages}, + author={Pan, Xiaoman and Zhang, Boliang and May, Jonathan and Nothman, Joel and Knight, Kevin and Ji, Heng}, + booktitle={Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, + volume={1}, + pages={1946--1958}, + year={2017} +} + +@article{raffel2019exploring, + title={Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer}, + author={Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu}, + year={2019}, + journal={arXiv preprint arXiv:1910.10683}, +} + +@inproceedings{pennington2014glove, + author = {Jeffrey Pennington and Richard Socher and Christopher D. Manning}, + booktitle = {EMNLP}, + title = {GloVe: Global Vectors for Word Representation}, + year = {2014}, + pages = {1532--1543}, + url = {http://www.aclweb.org/anthology/D14-1162}, +} + +@article{kudo2018sentencepiece, + title={Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing}, + author={Kudo, Taku and Richardson, John}, + journal={EMNLP}, + year={2018} +} + +@article{rajpurkar2018know, + title={Know What You Don't Know: Unanswerable Questions for SQuAD}, + author={Rajpurkar, Pranav and Jia, Robin and Liang, Percy}, + journal={ACL}, + year={2018} +} + +@article{joulin2017bag, + title={Bag of Tricks for Efficient Text Classification}, + author={Joulin, Armand and Grave, Edouard and Mikolov, Piotr Bojanowski Tomas}, + journal={EACL 2017}, + pages={427}, + year={2017} +} + +@inproceedings{kudo2018subword, + title={Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates}, + author={Kudo, Taku}, + booktitle={ACL}, + pages={66--75}, + year={2018} +} + +@inproceedings{grave2018learning, + title={Learning Word Vectors for 157 Languages}, + author={Grave, Edouard and Bojanowski, Piotr and Gupta, Prakhar and Joulin, Armand and Mikolov, Tomas}, + booktitle={LREC}, + year={2018} +} \ No newline at end of file diff --git a/references/2019.arxiv.conneau/source/acl2020.sty b/references/2019.arxiv.conneau/source/acl2020.sty new file mode 100644 index 0000000000000000000000000000000000000000..b738cde2100e93d1db696511398c5b71790c22db --- /dev/null +++ b/references/2019.arxiv.conneau/source/acl2020.sty @@ -0,0 +1,560 @@ +% This is the LaTex style file for ACL 2020, based off of ACL 2019. + +% Addressing bibtex issues mentioned in https://github.com/acl-org/acl-pub/issues/2 +% Other major modifications include +% changing the color of the line numbers to a light gray; changing font size of abstract to be 10pt; changing caption font size to be 10pt. +% -- M Mitchell and Stephanie Lukin + +% 2017: modified to support DOI links in bibliography. Now uses +% natbib package rather than defining citation commands in this file. +% Use with acl_natbib.bst bib style. -- Dan Gildea + +% This is the LaTeX style for ACL 2016. It contains Margaret Mitchell's +% line number adaptations (ported by Hai Zhao and Yannick Versley). + +% It is nearly identical to the style files for ACL 2015, +% ACL 2014, EACL 2006, ACL2005, ACL 2002, ACL 2001, ACL 2000, +% EACL 95 and EACL 99. +% +% Changes made include: adapt layout to A4 and centimeters, widen abstract + +% This is the LaTeX style file for ACL 2000. It is nearly identical to the +% style files for EACL 95 and EACL 99. Minor changes include editing the +% instructions to reflect use of \documentclass rather than \documentstyle +% and removing the white space before the title on the first page +% -- John Chen, June 29, 2000 + +% This is the LaTeX style file for EACL-95. It is identical to the +% style file for ANLP '94 except that the margins are adjusted for A4 +% paper. -- abney 13 Dec 94 + +% The ANLP '94 style file is a slightly modified +% version of the style used for AAAI and IJCAI, using some changes +% prepared by Fernando Pereira and others and some minor changes +% by Paul Jacobs. + +% Papers prepared using the aclsub.sty file and acl.bst bibtex style +% should be easily converted to final format using this style. +% (1) Submission information (\wordcount, \subject, and \makeidpage) +% should be removed. +% (2) \summary should be removed. The summary material should come +% after \maketitle and should be in the ``abstract'' environment +% (between \begin{abstract} and \end{abstract}). +% (3) Check all citations. This style should handle citations correctly +% and also allows multiple citations separated by semicolons. +% (4) Check figures and examples. Because the final format is double- +% column, some adjustments may have to be made to fit text in the column +% or to choose full-width (\figure*} figures. + +% Place this in a file called aclap.sty in the TeX search path. +% (Placing it in the same directory as the paper should also work.) + +% Prepared by Peter F. Patel-Schneider, liberally using the ideas of +% other style hackers, including Barbara Beeton. +% This style is NOT guaranteed to work. It is provided in the hope +% that it will make the preparation of papers easier. +% +% There are undoubtably bugs in this style. If you make bug fixes, +% improvements, etc. please let me know. My e-mail address is: +% pfps@research.att.com + +% Papers are to be prepared using the ``acl_natbib'' bibliography style, +% as follows: +% \documentclass[11pt]{article} +% \usepackage{acl2000} +% \title{Title} +% \author{Author 1 \and Author 2 \\ Address line \\ Address line \And +% Author 3 \\ Address line \\ Address line} +% \begin{document} +% ... +% \bibliography{bibliography-file} +% \bibliographystyle{acl_natbib} +% \end{document} + +% Author information can be set in various styles: +% For several authors from the same institution: +% \author{Author 1 \and ... \and Author n \\ +% Address line \\ ... \\ Address line} +% if the names do not fit well on one line use +% Author 1 \\ {\bf Author 2} \\ ... \\ {\bf Author n} \\ +% For authors from different institutions: +% \author{Author 1 \\ Address line \\ ... \\ Address line +% \And ... \And +% Author n \\ Address line \\ ... \\ Address line} +% To start a seperate ``row'' of authors use \AND, as in +% \author{Author 1 \\ Address line \\ ... \\ Address line +% \AND +% Author 2 \\ Address line \\ ... \\ Address line \And +% Author 3 \\ Address line \\ ... \\ Address line} + +% If the title and author information does not fit in the area allocated, +% place \setlength\titlebox{} right after +% \usepackage{acl2015} +% where can be something larger than 5cm + +% include hyperref, unless user specifies nohyperref option like this: +% \usepackage[nohyperref]{naaclhlt2018} +\newif\ifacl@hyperref +\DeclareOption{hyperref}{\acl@hyperreftrue} +\DeclareOption{nohyperref}{\acl@hyperreffalse} +\ExecuteOptions{hyperref} % default is to use hyperref +\ProcessOptions\relax +\ifacl@hyperref + \RequirePackage{hyperref} + \usepackage{xcolor} % make links dark blue + \definecolor{darkblue}{rgb}{0, 0, 0.5} + \hypersetup{colorlinks=true,citecolor=darkblue, linkcolor=darkblue, urlcolor=darkblue} +\else + % This definition is used if the hyperref package is not loaded. + % It provides a backup, no-op definiton of \href. + % This is necessary because \href command is used in the acl_natbib.bst file. + \def\href#1#2{{#2}} + % We still need to load xcolor in this case because the lighter line numbers require it. (SC/KG/WL) + \usepackage{xcolor} +\fi + +\typeout{Conference Style for ACL 2019} + +% NOTE: Some laser printers have a serious problem printing TeX output. +% These printing devices, commonly known as ``write-white'' laser +% printers, tend to make characters too light. To get around this +% problem, a darker set of fonts must be created for these devices. +% + +\newcommand{\Thanks}[1]{\thanks{\ #1}} + +% A4 modified by Eneko; again modified by Alexander for 5cm titlebox +\setlength{\paperwidth}{21cm} % A4 +\setlength{\paperheight}{29.7cm}% A4 +\setlength\topmargin{-0.5cm} +\setlength\oddsidemargin{0cm} +\setlength\textheight{24.7cm} +\setlength\textwidth{16.0cm} +\setlength\columnsep{0.6cm} +\newlength\titlebox +\setlength\titlebox{5cm} +\setlength\headheight{5pt} +\setlength\headsep{0pt} +\thispagestyle{empty} +\pagestyle{empty} + + +\flushbottom \twocolumn \sloppy + +% We're never going to need a table of contents, so just flush it to +% save space --- suggested by drstrip@sandia-2 +\def\addcontentsline#1#2#3{} + +\newif\ifaclfinal +\aclfinalfalse +\def\aclfinalcopy{\global\aclfinaltrue} + +%% ----- Set up hooks to repeat content on every page of the output doc, +%% necessary for the line numbers in the submitted version. --MM +%% +%% Copied from CVPR 2015's cvpr_eso.sty, which appears to be largely copied from everyshi.sty. +%% +%% Original cvpr_eso.sty available at: http://www.pamitc.org/cvpr15/author_guidelines.php +%% Original evershi.sty available at: https://www.ctan.org/pkg/everyshi +%% +%% Copyright (C) 2001 Martin Schr\"oder: +%% +%% Martin Schr"oder +%% Cr"usemannallee 3 +%% D-28213 Bremen +%% Martin.Schroeder@ACM.org +%% +%% This program may be redistributed and/or modified under the terms +%% of the LaTeX Project Public License, either version 1.0 of this +%% license, or (at your option) any later version. +%% The latest version of this license is in +%% CTAN:macros/latex/base/lppl.txt. +%% +%% Happy users are requested to send [Martin] a postcard. :-) +%% +\newcommand{\@EveryShipoutACL@Hook}{} +\newcommand{\@EveryShipoutACL@AtNextHook}{} +\newcommand*{\EveryShipoutACL}[1] + {\g@addto@macro\@EveryShipoutACL@Hook{#1}} +\newcommand*{\AtNextShipoutACL@}[1] + {\g@addto@macro\@EveryShipoutACL@AtNextHook{#1}} +\newcommand{\@EveryShipoutACL@Shipout}{% + \afterassignment\@EveryShipoutACL@Test + \global\setbox\@cclv= % + } +\newcommand{\@EveryShipoutACL@Test}{% + \ifvoid\@cclv\relax + \aftergroup\@EveryShipoutACL@Output + \else + \@EveryShipoutACL@Output + \fi% + } +\newcommand{\@EveryShipoutACL@Output}{% + \@EveryShipoutACL@Hook% + \@EveryShipoutACL@AtNextHook% + \gdef\@EveryShipoutACL@AtNextHook{}% + \@EveryShipoutACL@Org@Shipout\box\@cclv% + } +\newcommand{\@EveryShipoutACL@Org@Shipout}{} +\newcommand*{\@EveryShipoutACL@Init}{% + \message{ABD: EveryShipout initializing macros}% + \let\@EveryShipoutACL@Org@Shipout\shipout + \let\shipout\@EveryShipoutACL@Shipout + } +\AtBeginDocument{\@EveryShipoutACL@Init} + +%% ----- Set up for placing additional items into the submitted version --MM +%% +%% Based on eso-pic.sty +%% +%% Original available at: https://www.ctan.org/tex-archive/macros/latex/contrib/eso-pic +%% Copyright (C) 1998-2002 by Rolf Niepraschk +%% +%% Which may be distributed and/or modified under the conditions of +%% the LaTeX Project Public License, either version 1.2 of this license +%% or (at your option) any later version. The latest version of this +%% license is in: +%% +%% http://www.latex-project.org/lppl.txt +%% +%% and version 1.2 or later is part of all distributions of LaTeX version +%% 1999/12/01 or later. +%% +%% In contrast to the original, we do not include the definitions for/using: +%% gridpicture, div[2], isMEMOIR[1], gridSetup[6][], subgridstyle{dotted}, labelfactor{}, gap{}, gridunitname{}, gridunit{}, gridlines{\thinlines}, subgridlines{\thinlines}, the {keyval} package, evenside margin, nor any definitions with 'color'. +%% +%% These are beyond what is needed for the NAACL/ACL style. +%% +\newcommand\LenToUnit[1]{#1\@gobble} +\newcommand\AtPageUpperLeft[1]{% + \begingroup + \@tempdima=0pt\relax\@tempdimb=\ESO@yoffsetI\relax + \put(\LenToUnit{\@tempdima},\LenToUnit{\@tempdimb}){#1}% + \endgroup +} +\newcommand\AtPageLowerLeft[1]{\AtPageUpperLeft{% + \put(0,\LenToUnit{-\paperheight}){#1}}} +\newcommand\AtPageCenter[1]{\AtPageUpperLeft{% + \put(\LenToUnit{.5\paperwidth},\LenToUnit{-.5\paperheight}){#1}}} +\newcommand\AtPageLowerCenter[1]{\AtPageUpperLeft{% + \put(\LenToUnit{.5\paperwidth},\LenToUnit{-\paperheight}){#1}}}% +\newcommand\AtPageLowishCenter[1]{\AtPageUpperLeft{% + \put(\LenToUnit{.5\paperwidth},\LenToUnit{-.96\paperheight}){#1}}} +\newcommand\AtTextUpperLeft[1]{% + \begingroup + \setlength\@tempdima{1in}% + \advance\@tempdima\oddsidemargin% + \@tempdimb=\ESO@yoffsetI\relax\advance\@tempdimb-1in\relax% + \advance\@tempdimb-\topmargin% + \advance\@tempdimb-\headheight\advance\@tempdimb-\headsep% + \put(\LenToUnit{\@tempdima},\LenToUnit{\@tempdimb}){#1}% + \endgroup +} +\newcommand\AtTextLowerLeft[1]{\AtTextUpperLeft{% + \put(0,\LenToUnit{-\textheight}){#1}}} +\newcommand\AtTextCenter[1]{\AtTextUpperLeft{% + \put(\LenToUnit{.5\textwidth},\LenToUnit{-.5\textheight}){#1}}} +\newcommand{\ESO@HookI}{} \newcommand{\ESO@HookII}{} +\newcommand{\ESO@HookIII}{} +\newcommand{\AddToShipoutPicture}{% + \@ifstar{\g@addto@macro\ESO@HookII}{\g@addto@macro\ESO@HookI}} +\newcommand{\ClearShipoutPicture}{\global\let\ESO@HookI\@empty} +\newcommand{\@ShipoutPicture}{% + \bgroup + \@tempswafalse% + \ifx\ESO@HookI\@empty\else\@tempswatrue\fi% + \ifx\ESO@HookII\@empty\else\@tempswatrue\fi% + \ifx\ESO@HookIII\@empty\else\@tempswatrue\fi% + \if@tempswa% + \@tempdima=1in\@tempdimb=-\@tempdima% + \advance\@tempdimb\ESO@yoffsetI% + \unitlength=1pt% + \global\setbox\@cclv\vbox{% + \vbox{\let\protect\relax + \pictur@(0,0)(\strip@pt\@tempdima,\strip@pt\@tempdimb)% + \ESO@HookIII\ESO@HookI\ESO@HookII% + \global\let\ESO@HookII\@empty% + \endpicture}% + \nointerlineskip% + \box\@cclv}% + \fi + \egroup +} +\EveryShipoutACL{\@ShipoutPicture} +\newif\ifESO@dvips\ESO@dvipsfalse +\newif\ifESO@grid\ESO@gridfalse +\newif\ifESO@texcoord\ESO@texcoordfalse +\newcommand*\ESO@griddelta{}\newcommand*\ESO@griddeltaY{} +\newcommand*\ESO@gridDelta{}\newcommand*\ESO@gridDeltaY{} +\newcommand*\ESO@yoffsetI{}\newcommand*\ESO@yoffsetII{} +\ifESO@texcoord + \def\ESO@yoffsetI{0pt}\def\ESO@yoffsetII{-\paperheight} + \edef\ESO@griddeltaY{-\ESO@griddelta}\edef\ESO@gridDeltaY{-\ESO@gridDelta} +\else + \def\ESO@yoffsetI{\paperheight}\def\ESO@yoffsetII{0pt} + \edef\ESO@griddeltaY{\ESO@griddelta}\edef\ESO@gridDeltaY{\ESO@gridDelta} +\fi + + +%% ----- Submitted version markup: Page numbers, ruler, and confidentiality. Using ideas/code from cvpr.sty 2015. --MM + +\font\aclhv = phvb at 8pt + +%% Define vruler %% + +%\makeatletter +\newbox\aclrulerbox +\newcount\aclrulercount +\newdimen\aclruleroffset +\newdimen\cv@lineheight +\newdimen\cv@boxheight +\newbox\cv@tmpbox +\newcount\cv@refno +\newcount\cv@tot +% NUMBER with left flushed zeros \fillzeros[] +\newcount\cv@tmpc@ \newcount\cv@tmpc +\def\fillzeros[#1]#2{\cv@tmpc@=#2\relax\ifnum\cv@tmpc@<0\cv@tmpc@=-\cv@tmpc@\fi +\cv@tmpc=1 % +\loop\ifnum\cv@tmpc@<10 \else \divide\cv@tmpc@ by 10 \advance\cv@tmpc by 1 \fi + \ifnum\cv@tmpc@=10\relax\cv@tmpc@=11\relax\fi \ifnum\cv@tmpc@>10 \repeat +\ifnum#2<0\advance\cv@tmpc1\relax-\fi +\loop\ifnum\cv@tmpc<#1\relax0\advance\cv@tmpc1\relax\fi \ifnum\cv@tmpc<#1 \repeat +\cv@tmpc@=#2\relax\ifnum\cv@tmpc@<0\cv@tmpc@=-\cv@tmpc@\fi \relax\the\cv@tmpc@}% +% \makevruler[][][][][] +\def\makevruler[#1][#2][#3][#4][#5]{\begingroup\offinterlineskip +\textheight=#5\vbadness=10000\vfuzz=120ex\overfullrule=0pt% +\global\setbox\aclrulerbox=\vbox to \textheight{% +{\parskip=0pt\hfuzz=150em\cv@boxheight=\textheight +\color{gray} +\cv@lineheight=#1\global\aclrulercount=#2% +\cv@tot\cv@boxheight\divide\cv@tot\cv@lineheight\advance\cv@tot2% +\cv@refno1\vskip-\cv@lineheight\vskip1ex% +\loop\setbox\cv@tmpbox=\hbox to0cm{{\aclhv\hfil\fillzeros[#4]\aclrulercount}}% +\ht\cv@tmpbox\cv@lineheight\dp\cv@tmpbox0pt\box\cv@tmpbox\break +\advance\cv@refno1\global\advance\aclrulercount#3\relax +\ifnum\cv@refno<\cv@tot\repeat}}\endgroup}% +%\makeatother + + +\def\aclpaperid{***} +\def\confidential{\textcolor{black}{ACL 2020 Submission~\aclpaperid. Confidential Review Copy. DO NOT DISTRIBUTE.}} + +%% Page numbering, Vruler and Confidentiality %% +% \makevruler[][][][][] + +% SC/KG/WL - changed line numbering to gainsboro +\definecolor{gainsboro}{rgb}{0.8, 0.8, 0.8} +%\def\aclruler#1{\makevruler[14.17pt][#1][1][3][\textheight]\usebox{\aclrulerbox}} %% old line +\def\aclruler#1{\textcolor{gainsboro}{\makevruler[14.17pt][#1][1][3][\textheight]\usebox{\aclrulerbox}}} + +\def\leftoffset{-2.1cm} %original: -45pt +\def\rightoffset{17.5cm} %original: 500pt +\ifaclfinal\else\pagenumbering{arabic} +\AddToShipoutPicture{% +\ifaclfinal\else +\AtPageLowishCenter{\textcolor{black}{\thepage}} +\aclruleroffset=\textheight +\advance\aclruleroffset4pt + \AtTextUpperLeft{% + \put(\LenToUnit{\leftoffset},\LenToUnit{-\aclruleroffset}){%left ruler + \aclruler{\aclrulercount}} + \put(\LenToUnit{\rightoffset},\LenToUnit{-\aclruleroffset}){%right ruler + \aclruler{\aclrulercount}} + } + \AtTextUpperLeft{%confidential + \put(0,\LenToUnit{1cm}){\parbox{\textwidth}{\centering\aclhv\confidential}} + } +\fi +} + +%%%% ----- End settings for placing additional items into the submitted version --MM ----- %%%% + +%%%% ----- Begin settings for both submitted and camera-ready version ----- %%%% + +%% Title and Authors %% + +\newcommand\outauthor{ + \begin{tabular}[t]{c} + \ifaclfinal + \bf\@author + \else + % Avoiding common accidental de-anonymization issue. --MM + \bf Anonymous ACL submission + \fi + \end{tabular}} + +% Changing the expanded titlebox for submissions to 2.5 in (rather than 6.5cm) +% and moving it to the style sheet, rather than within the example tex file. --MM +\ifaclfinal +\else + \addtolength\titlebox{.25in} +\fi +% Mostly taken from deproc. +\def\maketitle{\par + \begingroup + \def\thefootnote{\fnsymbol{footnote}} + \def\@makefnmark{\hbox to 0pt{$^{\@thefnmark}$\hss}} + \twocolumn[\@maketitle] \@thanks + \endgroup + \setcounter{footnote}{0} + \let\maketitle\relax \let\@maketitle\relax + \gdef\@thanks{}\gdef\@author{}\gdef\@title{}\let\thanks\relax} +\def\@maketitle{\vbox to \titlebox{\hsize\textwidth + \linewidth\hsize \vskip 0.125in minus 0.125in \centering + {\Large\bf \@title \par} \vskip 0.2in plus 1fil minus 0.1in + {\def\and{\unskip\enspace{\rm and}\enspace}% + \def\And{\end{tabular}\hss \egroup \hskip 1in plus 2fil + \hbox to 0pt\bgroup\hss \begin{tabular}[t]{c}\bf}% + \def\AND{\end{tabular}\hss\egroup \hfil\hfil\egroup + \vskip 0.25in plus 1fil minus 0.125in + \hbox to \linewidth\bgroup\large \hfil\hfil + \hbox to 0pt\bgroup\hss \begin{tabular}[t]{c}\bf} + \hbox to \linewidth\bgroup\large \hfil\hfil + \hbox to 0pt\bgroup\hss + \outauthor + \hss\egroup + \hfil\hfil\egroup} + \vskip 0.3in plus 2fil minus 0.1in +}} + +% margins and font size for abstract +\renewenvironment{abstract}% + {\centerline{\large\bf Abstract}% + \begin{list}{}% + {\setlength{\rightmargin}{0.6cm}% + \setlength{\leftmargin}{0.6cm}}% + \item[]\ignorespaces% + \@setsize\normalsize{12pt}\xpt\@xpt + }% + {\unskip\end{list}} + +%\renewenvironment{abstract}{\centerline{\large\bf +% Abstract}\vspace{0.5ex}\begin{quote}}{\par\end{quote}\vskip 1ex} + +% Resizing figure and table captions - SL +\newcommand{\figcapfont}{\rm} +\newcommand{\tabcapfont}{\rm} +\renewcommand{\fnum@figure}{\figcapfont Figure \thefigure} +\renewcommand{\fnum@table}{\tabcapfont Table \thetable} +\renewcommand{\figcapfont}{\@setsize\normalsize{12pt}\xpt\@xpt} +\renewcommand{\tabcapfont}{\@setsize\normalsize{12pt}\xpt\@xpt} +% Support for interacting with the caption, subfigure, and subcaption packages - SL +\usepackage{caption} +\DeclareCaptionFont{10pt}{\fontsize{10pt}{12pt}\selectfont} +\captionsetup{font=10pt} + +\RequirePackage{natbib} +% for citation commands in the .tex, authors can use: +% \citep, \citet, and \citeyearpar for compatibility with natbib, or +% \cite, \newcite, and \shortcite for compatibility with older ACL .sty files +\renewcommand\cite{\citep} % to get "(Author Year)" with natbib +\newcommand\shortcite{\citeyearpar}% to get "(Year)" with natbib +\newcommand\newcite{\citet} % to get "Author (Year)" with natbib + +% DK/IV: Workaround for annoying hyperref pagewrap bug +% \RequirePackage{etoolbox} +% \patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{\errmessage{\noexpand patch failed}} + +% bibliography + +\def\@up#1{\raise.2ex\hbox{#1}} + +% Don't put a label in the bibliography at all. Just use the unlabeled format +% instead. +\def\thebibliography#1{\vskip\parskip% +\vskip\baselineskip% +\def\baselinestretch{1}% +\ifx\@currsize\normalsize\@normalsize\else\@currsize\fi% +\vskip-\parskip% +\vskip-\baselineskip% +\section*{References\@mkboth + {References}{References}}\list + {}{\setlength{\labelwidth}{0pt}\setlength{\leftmargin}{\parindent} + \setlength{\itemindent}{-\parindent}} + \def\newblock{\hskip .11em plus .33em minus -.07em} + \sloppy\clubpenalty4000\widowpenalty4000 + \sfcode`\.=1000\relax} +\let\endthebibliography=\endlist + + +% Allow for a bibliography of sources of attested examples +\def\thesourcebibliography#1{\vskip\parskip% +\vskip\baselineskip% +\def\baselinestretch{1}% +\ifx\@currsize\normalsize\@normalsize\else\@currsize\fi% +\vskip-\parskip% +\vskip-\baselineskip% +\section*{Sources of Attested Examples\@mkboth + {Sources of Attested Examples}{Sources of Attested Examples}}\list + {}{\setlength{\labelwidth}{0pt}\setlength{\leftmargin}{\parindent} + \setlength{\itemindent}{-\parindent}} + \def\newblock{\hskip .11em plus .33em minus -.07em} + \sloppy\clubpenalty4000\widowpenalty4000 + \sfcode`\.=1000\relax} +\let\endthesourcebibliography=\endlist + +% sections with less space +\def\section{\@startsection {section}{1}{\z@}{-2.0ex plus + -0.5ex minus -.2ex}{1.5ex plus 0.3ex minus .2ex}{\large\bf\raggedright}} +\def\subsection{\@startsection{subsection}{2}{\z@}{-1.8ex plus + -0.5ex minus -.2ex}{0.8ex plus .2ex}{\normalsize\bf\raggedright}} +%% changed by KO to - values to get teh initial parindent right +\def\subsubsection{\@startsection{subsubsection}{3}{\z@}{-1.5ex plus + -0.5ex minus -.2ex}{0.5ex plus .2ex}{\normalsize\bf\raggedright}} +\def\paragraph{\@startsection{paragraph}{4}{\z@}{1.5ex plus + 0.5ex minus .2ex}{-1em}{\normalsize\bf}} +\def\subparagraph{\@startsection{subparagraph}{5}{\parindent}{1.5ex plus + 0.5ex minus .2ex}{-1em}{\normalsize\bf}} + +% Footnotes +\footnotesep 6.65pt % +\skip\footins 9pt plus 4pt minus 2pt +\def\footnoterule{\kern-3pt \hrule width 5pc \kern 2.6pt } +\setcounter{footnote}{0} + +% Lists and paragraphs +\parindent 1em +\topsep 4pt plus 1pt minus 2pt +\partopsep 1pt plus 0.5pt minus 0.5pt +\itemsep 2pt plus 1pt minus 0.5pt +\parsep 2pt plus 1pt minus 0.5pt + +\leftmargin 2em \leftmargini\leftmargin \leftmarginii 2em +\leftmarginiii 1.5em \leftmarginiv 1.0em \leftmarginv .5em \leftmarginvi .5em +\labelwidth\leftmargini\advance\labelwidth-\labelsep \labelsep 5pt + +\def\@listi{\leftmargin\leftmargini} +\def\@listii{\leftmargin\leftmarginii + \labelwidth\leftmarginii\advance\labelwidth-\labelsep + \topsep 2pt plus 1pt minus 0.5pt + \parsep 1pt plus 0.5pt minus 0.5pt + \itemsep \parsep} +\def\@listiii{\leftmargin\leftmarginiii + \labelwidth\leftmarginiii\advance\labelwidth-\labelsep + \topsep 1pt plus 0.5pt minus 0.5pt + \parsep \z@ \partopsep 0.5pt plus 0pt minus 0.5pt + \itemsep \topsep} +\def\@listiv{\leftmargin\leftmarginiv + \labelwidth\leftmarginiv\advance\labelwidth-\labelsep} +\def\@listv{\leftmargin\leftmarginv + \labelwidth\leftmarginv\advance\labelwidth-\labelsep} +\def\@listvi{\leftmargin\leftmarginvi + \labelwidth\leftmarginvi\advance\labelwidth-\labelsep} + +\abovedisplayskip 7pt plus2pt minus5pt% +\belowdisplayskip \abovedisplayskip +\abovedisplayshortskip 0pt plus3pt% +\belowdisplayshortskip 4pt plus3pt minus3pt% + +% Less leading in most fonts (due to the narrow columns) +% The choices were between 1-pt and 1.5-pt leading +\def\@normalsize{\@setsize\normalsize{11pt}\xpt\@xpt} +\def\small{\@setsize\small{10pt}\ixpt\@ixpt} +\def\footnotesize{\@setsize\footnotesize{10pt}\ixpt\@ixpt} +\def\scriptsize{\@setsize\scriptsize{8pt}\viipt\@viipt} +\def\tiny{\@setsize\tiny{7pt}\vipt\@vipt} +\def\large{\@setsize\large{14pt}\xiipt\@xiipt} +\def\Large{\@setsize\Large{16pt}\xivpt\@xivpt} +\def\LARGE{\@setsize\LARGE{20pt}\xviipt\@xviipt} +\def\huge{\@setsize\huge{23pt}\xxpt\@xxpt} +\def\Huge{\@setsize\Huge{28pt}\xxvpt\@xxvpt} diff --git a/references/2019.arxiv.conneau/source/acl_natbib.bst b/references/2019.arxiv.conneau/source/acl_natbib.bst new file mode 100644 index 0000000000000000000000000000000000000000..821195d8bbb77f882afb308a31e5f9da81720f6b --- /dev/null +++ b/references/2019.arxiv.conneau/source/acl_natbib.bst @@ -0,0 +1,1975 @@ +%%% acl_natbib.bst +%%% Modification of BibTeX style file acl_natbib_nourl.bst +%%% ... by urlbst, version 0.7 (marked with "% urlbst") +%%% See +%%% Added webpage entry type, and url and lastchecked fields. +%%% Added eprint support. +%%% Added DOI support. +%%% Added PUBMED support. +%%% Added hyperref support. +%%% Original headers follow... + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% BibTeX style file acl_natbib_nourl.bst +% +% intended as input to urlbst script +% $ ./urlbst --hyperref --inlinelinks acl_natbib_nourl.bst > acl_natbib.bst +% +% adapted from compling.bst +% in order to mimic the style files for ACL conferences prior to 2017 +% by making the following three changes: +% - for @incollection, page numbers now follow volume title. +% - for @inproceedings, address now follows conference name. +% (address is intended as location of conference, +% not address of publisher.) +% - for papers with three authors, use et al. in citation +% Dan Gildea 2017/06/08 +% - fixed a bug with format.chapter - error given if chapter is empty +% with inbook. +% Shay Cohen 2018/02/16 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% BibTeX style file compling.bst +% +% Intended for the journal Computational Linguistics (ACL/MIT Press) +% Created by Ron Artstein on 2005/08/22 +% For use with for author-year citations. +% +% I created this file in order to allow submissions to the journal +% Computational Linguistics using the package for author-year +% citations, which offers a lot more flexibility than , CL's +% official citation package. This file adheres strictly to the official +% style guide available from the MIT Press: +% +% http://mitpress.mit.edu/journals/coli/compling_style.pdf +% +% This includes all the various quirks of the style guide, for example: +% - a chapter from a monograph (@inbook) has no page numbers. +% - an article from an edited volume (@incollection) has page numbers +% after the publisher and address. +% - an article from a proceedings volume (@inproceedings) has page +% numbers before the publisher and address. +% +% Where the style guide was inconsistent or not specific enough I +% looked at actual published articles and exercised my own judgment. +% I noticed two inconsistencies in the style guide: +% +% - The style guide gives one example of an article from an edited +% volume with the editor's name spelled out in full, and another +% with the editors' names abbreviated. I chose to accept the first +% one as correct, since the style guide generally shuns abbreviations, +% and editors' names are also spelled out in some recently published +% articles. +% +% - The style guide gives one example of a reference where the word +% "and" between two authors is preceded by a comma. This is most +% likely a typo, since in all other cases with just two authors or +% editors there is no comma before the word "and". +% +% One case where the style guide is not being specific is the placement +% of the edition number, for which no example is given. I chose to put +% it immediately after the title, which I (subjectively) find natural, +% and is also the place of the edition in a few recently published +% articles. +% +% This file correctly reproduces all of the examples in the official +% style guide, except for the two inconsistencies noted above. I even +% managed to get it to correctly format the proceedings example which +% has an organization, a publisher, and two addresses (the conference +% location and the publisher's address), though I cheated a bit by +% putting the conference location and month as part of the title field; +% I feel that in this case the conference location and month can be +% considered as part of the title, and that adding a location field +% is not justified. Note also that a location field is not standard, +% so entries made with this field would not port nicely to other styles. +% However, if authors feel that there's a need for a location field +% then tell me and I'll see what I can do. +% +% The file also produces to my satisfaction all the bibliographical +% entries in my recent (joint) submission to CL (this was the original +% motivation for creating the file). I also tested it by running it +% on a larger set of entries and eyeballing the results. There may of +% course still be errors, especially with combinations of fields that +% are not that common, or with cross-references (which I seldom use). +% If you find such errors please write to me. +% +% I hope people find this file useful. Please email me with comments +% and suggestions. +% +% Ron Artstein +% artstein [at] essex.ac.uk +% August 22, 2005. +% +% Some technical notes. +% +% This file is based on a file generated with the package +% by Patrick W. Daly (see selected options below), which was then +% manually customized to conform with certain CL requirements which +% cannot be met by . Departures from the generated file +% include: +% +% Function inbook: moved publisher and address to the end; moved +% edition after title; replaced function format.chapter.pages by +% new function format.chapter to output chapter without pages. +% +% Function inproceedings: moved publisher and address to the end; +% replaced function format.in.ed.booktitle by new function +% format.in.booktitle to output the proceedings title without +% the editor. +% +% Functions book, incollection, manual: moved edition after title. +% +% Function mastersthesis: formatted title as for articles (unlike +% phdthesis which is formatted as book) and added month. +% +% Function proceedings: added new.sentence between organization and +% publisher when both are present. +% +% Function format.lab.names: modified so that it gives all the +% authors' surnames for in-text citations for one, two and three +% authors and only uses "et. al" for works with four authors or more +% (thanks to Ken Shan for convincing me to go through the trouble of +% modifying this function rather than using unreliable hacks). +% +% Changes: +% +% 2006-10-27: Changed function reverse.pass so that the extra label is +% enclosed in parentheses when the year field ends in an uppercase or +% lowercase letter (change modeled after Uli Sauerland's modification +% of nals.bst). RA. +% +% +% The preamble of the generated file begins below: +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% +%% This is file `compling.bst', +%% generated with the docstrip utility. +%% +%% The original source files were: +%% +%% merlin.mbs (with options: `ay,nat,vonx,nm-revv1,jnrlst,keyxyr,blkyear,dt-beg,yr-per,note-yr,num-xser,pre-pub,xedn,nfss') +%% ---------------------------------------- +%% *** Intended for the journal Computational Linguistics *** +%% +%% Copyright 1994-2002 Patrick W Daly + % =============================================================== + % IMPORTANT NOTICE: + % This bibliographic style (bst) file has been generated from one or + % more master bibliographic style (mbs) files, listed above. + % + % This generated file can be redistributed and/or modified under the terms + % of the LaTeX Project Public License Distributed from CTAN + % archives in directory macros/latex/base/lppl.txt; either + % version 1 of the License, or any later version. + % =============================================================== + % Name and version information of the main mbs file: + % \ProvidesFile{merlin.mbs}[2002/10/21 4.05 (PWD, AO, DPC)] + % For use with BibTeX version 0.99a or later + %------------------------------------------------------------------- + % This bibliography style file is intended for texts in ENGLISH + % This is an author-year citation style bibliography. As such, it is + % non-standard LaTeX, and requires a special package file to function properly. + % Such a package is natbib.sty by Patrick W. Daly + % The form of the \bibitem entries is + % \bibitem[Jones et al.(1990)]{key}... + % \bibitem[Jones et al.(1990)Jones, Baker, and Smith]{key}... + % The essential feature is that the label (the part in brackets) consists + % of the author names, as they should appear in the citation, with the year + % in parentheses following. There must be no space before the opening + % parenthesis! + % With natbib v5.3, a full list of authors may also follow the year. + % In natbib.sty, it is possible to define the type of enclosures that is + % really wanted (brackets or parentheses), but in either case, there must + % be parentheses in the label. + % The \cite command functions as follows: + % \citet{key} ==>> Jones et al. (1990) + % \citet*{key} ==>> Jones, Baker, and Smith (1990) + % \citep{key} ==>> (Jones et al., 1990) + % \citep*{key} ==>> (Jones, Baker, and Smith, 1990) + % \citep[chap. 2]{key} ==>> (Jones et al., 1990, chap. 2) + % \citep[e.g.][]{key} ==>> (e.g. Jones et al., 1990) + % \citep[e.g.][p. 32]{key} ==>> (e.g. Jones et al., p. 32) + % \citeauthor{key} ==>> Jones et al. + % \citeauthor*{key} ==>> Jones, Baker, and Smith + % \citeyear{key} ==>> 1990 + %--------------------------------------------------------------------- + +ENTRY + { address + author + booktitle + chapter + edition + editor + howpublished + institution + journal + key + month + note + number + organization + pages + publisher + school + series + title + type + volume + year + eprint % urlbst + doi % urlbst + pubmed % urlbst + url % urlbst + lastchecked % urlbst + } + {} + { label extra.label sort.label short.list } +INTEGERS { output.state before.all mid.sentence after.sentence after.block } +% urlbst... +% urlbst constants and state variables +STRINGS { urlintro + eprinturl eprintprefix doiprefix doiurl pubmedprefix pubmedurl + citedstring onlinestring linktextstring + openinlinelink closeinlinelink } +INTEGERS { hrefform inlinelinks makeinlinelink + addeprints adddoiresolver addpubmedresolver } +FUNCTION {init.urlbst.variables} +{ + % The following constants may be adjusted by hand, if desired + + % The first set allow you to enable or disable certain functionality. + #1 'addeprints := % 0=no eprints; 1=include eprints + #1 'adddoiresolver := % 0=no DOI resolver; 1=include it + #1 'addpubmedresolver := % 0=no PUBMED resolver; 1=include it + #2 'hrefform := % 0=no crossrefs; 1=hypertex xrefs; 2=hyperref refs + #1 'inlinelinks := % 0=URLs explicit; 1=URLs attached to titles + + % String constants, which you _might_ want to tweak. + "URL: " 'urlintro := % prefix before URL; typically "Available from:" or "URL": + "online" 'onlinestring := % indication that resource is online; typically "online" + "cited " 'citedstring := % indicator of citation date; typically "cited " + "[link]" 'linktextstring := % dummy link text; typically "[link]" + "http://arxiv.org/abs/" 'eprinturl := % prefix to make URL from eprint ref + "arXiv:" 'eprintprefix := % text prefix printed before eprint ref; typically "arXiv:" + "https://doi.org/" 'doiurl := % prefix to make URL from DOI + "doi:" 'doiprefix := % text prefix printed before DOI ref; typically "doi:" + "http://www.ncbi.nlm.nih.gov/pubmed/" 'pubmedurl := % prefix to make URL from PUBMED + "PMID:" 'pubmedprefix := % text prefix printed before PUBMED ref; typically "PMID:" + + % The following are internal state variables, not configuration constants, + % so they shouldn't be fiddled with. + #0 'makeinlinelink := % state variable managed by possibly.setup.inlinelink + "" 'openinlinelink := % ditto + "" 'closeinlinelink := % ditto +} +INTEGERS { + bracket.state + outside.brackets + open.brackets + within.brackets + close.brackets +} +% ...urlbst to here +FUNCTION {init.state.consts} +{ #0 'outside.brackets := % urlbst... + #1 'open.brackets := + #2 'within.brackets := + #3 'close.brackets := % ...urlbst to here + + #0 'before.all := + #1 'mid.sentence := + #2 'after.sentence := + #3 'after.block := +} +STRINGS { s t} +% urlbst +FUNCTION {output.nonnull.original} +{ 's := + output.state mid.sentence = + { ", " * write$ } + { output.state after.block = + { add.period$ write$ + newline$ + "\newblock " write$ + } + { output.state before.all = + 'write$ + { add.period$ " " * write$ } + if$ + } + if$ + mid.sentence 'output.state := + } + if$ + s +} + +% urlbst... +% The following three functions are for handling inlinelink. They wrap +% a block of text which is potentially output with write$ by multiple +% other functions, so we don't know the content a priori. +% They communicate between each other using the variables makeinlinelink +% (which is true if a link should be made), and closeinlinelink (which holds +% the string which should close any current link. They can be called +% at any time, but start.inlinelink will be a no-op unless something has +% previously set makeinlinelink true, and the two ...end.inlinelink functions +% will only do their stuff if start.inlinelink has previously set +% closeinlinelink to be non-empty. +% (thanks to 'ijvm' for suggested code here) +FUNCTION {uand} +{ 'skip$ { pop$ #0 } if$ } % 'and' (which isn't defined at this point in the file) +FUNCTION {possibly.setup.inlinelink} +{ makeinlinelink hrefform #0 > uand + { doi empty$ adddoiresolver uand + { pubmed empty$ addpubmedresolver uand + { eprint empty$ addeprints uand + { url empty$ + { "" } + { url } + if$ } + { eprinturl eprint * } + if$ } + { pubmedurl pubmed * } + if$ } + { doiurl doi * } + if$ + % an appropriately-formatted URL is now on the stack + hrefform #1 = % hypertex + { "\special {html: }{" * 'openinlinelink := + "\special {html:}" 'closeinlinelink := } + { "\href {" swap$ * "} {" * 'openinlinelink := % hrefform=#2 -- hyperref + % the space between "} {" matters: a URL of just the right length can cause "\% newline em" + "}" 'closeinlinelink := } + if$ + #0 'makeinlinelink := + } + 'skip$ + if$ % makeinlinelink +} +FUNCTION {add.inlinelink} +{ openinlinelink empty$ + 'skip$ + { openinlinelink swap$ * closeinlinelink * + "" 'openinlinelink := + } + if$ +} +FUNCTION {output.nonnull} +{ % Save the thing we've been asked to output + 's := + % If the bracket-state is close.brackets, then add a close-bracket to + % what is currently at the top of the stack, and set bracket.state + % to outside.brackets + bracket.state close.brackets = + { "]" * + outside.brackets 'bracket.state := + } + 'skip$ + if$ + bracket.state outside.brackets = + { % We're outside all brackets -- this is the normal situation. + % Write out what's currently at the top of the stack, using the + % original output.nonnull function. + s + add.inlinelink + output.nonnull.original % invoke the original output.nonnull + } + { % Still in brackets. Add open-bracket or (continuation) comma, add the + % new text (in s) to the top of the stack, and move to the close-brackets + % state, ready for next time (unless inbrackets resets it). If we come + % into this branch, then output.state is carefully undisturbed. + bracket.state open.brackets = + { " [" * } + { ", " * } % bracket.state will be within.brackets + if$ + s * + close.brackets 'bracket.state := + } + if$ +} + +% Call this function just before adding something which should be presented in +% brackets. bracket.state is handled specially within output.nonnull. +FUNCTION {inbrackets} +{ bracket.state close.brackets = + { within.brackets 'bracket.state := } % reset the state: not open nor closed + { open.brackets 'bracket.state := } + if$ +} + +FUNCTION {format.lastchecked} +{ lastchecked empty$ + { "" } + { inbrackets citedstring lastchecked * } + if$ +} +% ...urlbst to here +FUNCTION {output} +{ duplicate$ empty$ + 'pop$ + 'output.nonnull + if$ +} +FUNCTION {output.check} +{ 't := + duplicate$ empty$ + { pop$ "empty " t * " in " * cite$ * warning$ } + 'output.nonnull + if$ +} +FUNCTION {fin.entry.original} % urlbst (renamed from fin.entry, so it can be wrapped below) +{ add.period$ + write$ + newline$ +} + +FUNCTION {new.block} +{ output.state before.all = + 'skip$ + { after.block 'output.state := } + if$ +} +FUNCTION {new.sentence} +{ output.state after.block = + 'skip$ + { output.state before.all = + 'skip$ + { after.sentence 'output.state := } + if$ + } + if$ +} +FUNCTION {add.blank} +{ " " * before.all 'output.state := +} + +FUNCTION {date.block} +{ + new.block +} + +FUNCTION {not} +{ { #0 } + { #1 } + if$ +} +FUNCTION {and} +{ 'skip$ + { pop$ #0 } + if$ +} +FUNCTION {or} +{ { pop$ #1 } + 'skip$ + if$ +} +FUNCTION {new.block.checkb} +{ empty$ + swap$ empty$ + and + 'skip$ + 'new.block + if$ +} +FUNCTION {field.or.null} +{ duplicate$ empty$ + { pop$ "" } + 'skip$ + if$ +} +FUNCTION {emphasize} +{ duplicate$ empty$ + { pop$ "" } + { "\emph{" swap$ * "}" * } + if$ +} +FUNCTION {tie.or.space.prefix} +{ duplicate$ text.length$ #3 < + { "~" } + { " " } + if$ + swap$ +} + +FUNCTION {capitalize} +{ "u" change.case$ "t" change.case$ } + +FUNCTION {space.word} +{ " " swap$ * " " * } + % Here are the language-specific definitions for explicit words. + % Each function has a name bbl.xxx where xxx is the English word. + % The language selected here is ENGLISH +FUNCTION {bbl.and} +{ "and"} + +FUNCTION {bbl.etal} +{ "et~al." } + +FUNCTION {bbl.editors} +{ "editors" } + +FUNCTION {bbl.editor} +{ "editor" } + +FUNCTION {bbl.edby} +{ "edited by" } + +FUNCTION {bbl.edition} +{ "edition" } + +FUNCTION {bbl.volume} +{ "volume" } + +FUNCTION {bbl.of} +{ "of" } + +FUNCTION {bbl.number} +{ "number" } + +FUNCTION {bbl.nr} +{ "no." } + +FUNCTION {bbl.in} +{ "in" } + +FUNCTION {bbl.pages} +{ "pages" } + +FUNCTION {bbl.page} +{ "page" } + +FUNCTION {bbl.chapter} +{ "chapter" } + +FUNCTION {bbl.techrep} +{ "Technical Report" } + +FUNCTION {bbl.mthesis} +{ "Master's thesis" } + +FUNCTION {bbl.phdthesis} +{ "Ph.D. thesis" } + +MACRO {jan} {"January"} + +MACRO {feb} {"February"} + +MACRO {mar} {"March"} + +MACRO {apr} {"April"} + +MACRO {may} {"May"} + +MACRO {jun} {"June"} + +MACRO {jul} {"July"} + +MACRO {aug} {"August"} + +MACRO {sep} {"September"} + +MACRO {oct} {"October"} + +MACRO {nov} {"November"} + +MACRO {dec} {"December"} + +MACRO {acmcs} {"ACM Computing Surveys"} + +MACRO {acta} {"Acta Informatica"} + +MACRO {cacm} {"Communications of the ACM"} + +MACRO {ibmjrd} {"IBM Journal of Research and Development"} + +MACRO {ibmsj} {"IBM Systems Journal"} + +MACRO {ieeese} {"IEEE Transactions on Software Engineering"} + +MACRO {ieeetc} {"IEEE Transactions on Computers"} + +MACRO {ieeetcad} + {"IEEE Transactions on Computer-Aided Design of Integrated Circuits"} + +MACRO {ipl} {"Information Processing Letters"} + +MACRO {jacm} {"Journal of the ACM"} + +MACRO {jcss} {"Journal of Computer and System Sciences"} + +MACRO {scp} {"Science of Computer Programming"} + +MACRO {sicomp} {"SIAM Journal on Computing"} + +MACRO {tocs} {"ACM Transactions on Computer Systems"} + +MACRO {tods} {"ACM Transactions on Database Systems"} + +MACRO {tog} {"ACM Transactions on Graphics"} + +MACRO {toms} {"ACM Transactions on Mathematical Software"} + +MACRO {toois} {"ACM Transactions on Office Information Systems"} + +MACRO {toplas} {"ACM Transactions on Programming Languages and Systems"} + +MACRO {tcs} {"Theoretical Computer Science"} +FUNCTION {bibinfo.check} +{ swap$ + duplicate$ missing$ + { + pop$ pop$ + "" + } + { duplicate$ empty$ + { + swap$ pop$ + } + { swap$ + pop$ + } + if$ + } + if$ +} +FUNCTION {bibinfo.warn} +{ swap$ + duplicate$ missing$ + { + swap$ "missing " swap$ * " in " * cite$ * warning$ pop$ + "" + } + { duplicate$ empty$ + { + swap$ "empty " swap$ * " in " * cite$ * warning$ + } + { swap$ + pop$ + } + if$ + } + if$ +} +STRINGS { bibinfo} +INTEGERS { nameptr namesleft numnames } + +FUNCTION {format.names} +{ 'bibinfo := + duplicate$ empty$ 'skip$ { + 's := + "" 't := + #1 'nameptr := + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + duplicate$ #1 > + { "{ff~}{vv~}{ll}{, jj}" } + { "{ff~}{vv~}{ll}{, jj}" } % first name first for first author +% { "{vv~}{ll}{, ff}{, jj}" } % last name first for first author + if$ + format.name$ + bibinfo bibinfo.check + 't := + nameptr #1 > + { + namesleft #1 > + { ", " * t * } + { + numnames #2 > + { "," * } + 'skip$ + if$ + s nameptr "{ll}" format.name$ duplicate$ "others" = + { 't := } + { pop$ } + if$ + t "others" = + { + " " * bbl.etal * + } + { + bbl.and + space.word * t * + } + if$ + } + if$ + } + 't + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ + } if$ +} +FUNCTION {format.names.ed} +{ + 'bibinfo := + duplicate$ empty$ 'skip$ { + 's := + "" 't := + #1 'nameptr := + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + "{ff~}{vv~}{ll}{, jj}" + format.name$ + bibinfo bibinfo.check + 't := + nameptr #1 > + { + namesleft #1 > + { ", " * t * } + { + numnames #2 > + { "," * } + 'skip$ + if$ + s nameptr "{ll}" format.name$ duplicate$ "others" = + { 't := } + { pop$ } + if$ + t "others" = + { + + " " * bbl.etal * + } + { + bbl.and + space.word * t * + } + if$ + } + if$ + } + 't + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ + } if$ +} +FUNCTION {format.key} +{ empty$ + { key field.or.null } + { "" } + if$ +} + +FUNCTION {format.authors} +{ author "author" format.names +} +FUNCTION {get.bbl.editor} +{ editor num.names$ #1 > 'bbl.editors 'bbl.editor if$ } + +FUNCTION {format.editors} +{ editor "editor" format.names duplicate$ empty$ 'skip$ + { + "," * + " " * + get.bbl.editor + * + } + if$ +} +FUNCTION {format.note} +{ + note empty$ + { "" } + { note #1 #1 substring$ + duplicate$ "{" = + 'skip$ + { output.state mid.sentence = + { "l" } + { "u" } + if$ + change.case$ + } + if$ + note #2 global.max$ substring$ * "note" bibinfo.check + } + if$ +} + +FUNCTION {format.title} +{ title + duplicate$ empty$ 'skip$ + { "t" change.case$ } + if$ + "title" bibinfo.check +} +FUNCTION {format.full.names} +{'s := + "" 't := + #1 'nameptr := + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + "{vv~}{ll}" format.name$ + 't := + nameptr #1 > + { + namesleft #1 > + { ", " * t * } + { + s nameptr "{ll}" format.name$ duplicate$ "others" = + { 't := } + { pop$ } + if$ + t "others" = + { + " " * bbl.etal * + } + { + numnames #2 > + { "," * } + 'skip$ + if$ + bbl.and + space.word * t * + } + if$ + } + if$ + } + 't + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ +} + +FUNCTION {author.editor.key.full} +{ author empty$ + { editor empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { editor format.full.names } + if$ + } + { author format.full.names } + if$ +} + +FUNCTION {author.key.full} +{ author empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { author format.full.names } + if$ +} + +FUNCTION {editor.key.full} +{ editor empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { editor format.full.names } + if$ +} + +FUNCTION {make.full.names} +{ type$ "book" = + type$ "inbook" = + or + 'author.editor.key.full + { type$ "proceedings" = + 'editor.key.full + 'author.key.full + if$ + } + if$ +} + +FUNCTION {output.bibitem.original} % urlbst (renamed from output.bibitem, so it can be wrapped below) +{ newline$ + "\bibitem[{" write$ + label write$ + ")" make.full.names duplicate$ short.list = + { pop$ } + { * } + if$ + "}]{" * write$ + cite$ write$ + "}" write$ + newline$ + "" + before.all 'output.state := +} + +FUNCTION {n.dashify} +{ + 't := + "" + { t empty$ not } + { t #1 #1 substring$ "-" = + { t #1 #2 substring$ "--" = not + { "--" * + t #2 global.max$ substring$ 't := + } + { { t #1 #1 substring$ "-" = } + { "-" * + t #2 global.max$ substring$ 't := + } + while$ + } + if$ + } + { t #1 #1 substring$ * + t #2 global.max$ substring$ 't := + } + if$ + } + while$ +} + +FUNCTION {word.in} +{ bbl.in capitalize + " " * } + +FUNCTION {format.date} +{ year "year" bibinfo.check duplicate$ empty$ + { + } + 'skip$ + if$ + extra.label * + before.all 'output.state := + after.sentence 'output.state := +} +FUNCTION {format.btitle} +{ title "title" bibinfo.check + duplicate$ empty$ 'skip$ + { + emphasize + } + if$ +} +FUNCTION {either.or.check} +{ empty$ + 'pop$ + { "can't use both " swap$ * " fields in " * cite$ * warning$ } + if$ +} +FUNCTION {format.bvolume} +{ volume empty$ + { "" } + { bbl.volume volume tie.or.space.prefix + "volume" bibinfo.check * * + series "series" bibinfo.check + duplicate$ empty$ 'pop$ + { swap$ bbl.of space.word * swap$ + emphasize * } + if$ + "volume and number" number either.or.check + } + if$ +} +FUNCTION {format.number.series} +{ volume empty$ + { number empty$ + { series field.or.null } + { series empty$ + { number "number" bibinfo.check } + { output.state mid.sentence = + { bbl.number } + { bbl.number capitalize } + if$ + number tie.or.space.prefix "number" bibinfo.check * * + bbl.in space.word * + series "series" bibinfo.check * + } + if$ + } + if$ + } + { "" } + if$ +} + +FUNCTION {format.edition} +{ edition duplicate$ empty$ 'skip$ + { + output.state mid.sentence = + { "l" } + { "t" } + if$ change.case$ + "edition" bibinfo.check + " " * bbl.edition * + } + if$ +} +INTEGERS { multiresult } +FUNCTION {multi.page.check} +{ 't := + #0 'multiresult := + { multiresult not + t empty$ not + and + } + { t #1 #1 substring$ + duplicate$ "-" = + swap$ duplicate$ "," = + swap$ "+" = + or or + { #1 'multiresult := } + { t #2 global.max$ substring$ 't := } + if$ + } + while$ + multiresult +} +FUNCTION {format.pages} +{ pages duplicate$ empty$ 'skip$ + { duplicate$ multi.page.check + { + bbl.pages swap$ + n.dashify + } + { + bbl.page swap$ + } + if$ + tie.or.space.prefix + "pages" bibinfo.check + * * + } + if$ +} +FUNCTION {format.journal.pages} +{ pages duplicate$ empty$ 'pop$ + { swap$ duplicate$ empty$ + { pop$ pop$ format.pages } + { + ":" * + swap$ + n.dashify + "pages" bibinfo.check + * + } + if$ + } + if$ +} +FUNCTION {format.vol.num.pages} +{ volume field.or.null + duplicate$ empty$ 'skip$ + { + "volume" bibinfo.check + } + if$ + number "number" bibinfo.check duplicate$ empty$ 'skip$ + { + swap$ duplicate$ empty$ + { "there's a number but no volume in " cite$ * warning$ } + 'skip$ + if$ + swap$ + "(" swap$ * ")" * + } + if$ * + format.journal.pages +} + +FUNCTION {format.chapter} +{ chapter empty$ + 'format.pages + { type empty$ + { bbl.chapter } + { type "l" change.case$ + "type" bibinfo.check + } + if$ + chapter tie.or.space.prefix + "chapter" bibinfo.check + * * + } + if$ +} + +FUNCTION {format.chapter.pages} +{ chapter empty$ + 'format.pages + { type empty$ + { bbl.chapter } + { type "l" change.case$ + "type" bibinfo.check + } + if$ + chapter tie.or.space.prefix + "chapter" bibinfo.check + * * + pages empty$ + 'skip$ + { ", " * format.pages * } + if$ + } + if$ +} + +FUNCTION {format.booktitle} +{ + booktitle "booktitle" bibinfo.check + emphasize +} +FUNCTION {format.in.booktitle} +{ format.booktitle duplicate$ empty$ 'skip$ + { + word.in swap$ * + } + if$ +} +FUNCTION {format.in.ed.booktitle} +{ format.booktitle duplicate$ empty$ 'skip$ + { + editor "editor" format.names.ed duplicate$ empty$ 'pop$ + { + "," * + " " * + get.bbl.editor + ", " * + * swap$ + * } + if$ + word.in swap$ * + } + if$ +} +FUNCTION {format.thesis.type} +{ type duplicate$ empty$ + 'pop$ + { swap$ pop$ + "t" change.case$ "type" bibinfo.check + } + if$ +} +FUNCTION {format.tr.number} +{ number "number" bibinfo.check + type duplicate$ empty$ + { pop$ bbl.techrep } + 'skip$ + if$ + "type" bibinfo.check + swap$ duplicate$ empty$ + { pop$ "t" change.case$ } + { tie.or.space.prefix * * } + if$ +} +FUNCTION {format.article.crossref} +{ + word.in + " \cite{" * crossref * "}" * +} +FUNCTION {format.book.crossref} +{ volume duplicate$ empty$ + { "empty volume in " cite$ * "'s crossref of " * crossref * warning$ + pop$ word.in + } + { bbl.volume + capitalize + swap$ tie.or.space.prefix "volume" bibinfo.check * * bbl.of space.word * + } + if$ + " \cite{" * crossref * "}" * +} +FUNCTION {format.incoll.inproc.crossref} +{ + word.in + " \cite{" * crossref * "}" * +} +FUNCTION {format.org.or.pub} +{ 't := + "" + address empty$ t empty$ and + 'skip$ + { + t empty$ + { address "address" bibinfo.check * + } + { t * + address empty$ + 'skip$ + { ", " * address "address" bibinfo.check * } + if$ + } + if$ + } + if$ +} +FUNCTION {format.publisher.address} +{ publisher "publisher" bibinfo.warn format.org.or.pub +} + +FUNCTION {format.organization.address} +{ organization "organization" bibinfo.check format.org.or.pub +} + +% urlbst... +% Functions for making hypertext links. +% In all cases, the stack has (link-text href-url) +% +% make 'null' specials +FUNCTION {make.href.null} +{ + pop$ +} +% make hypertex specials +FUNCTION {make.href.hypertex} +{ + "\special {html: }" * swap$ * + "\special {html:}" * +} +% make hyperref specials +FUNCTION {make.href.hyperref} +{ + "\href {" swap$ * "} {\path{" * swap$ * "}}" * +} +FUNCTION {make.href} +{ hrefform #2 = + 'make.href.hyperref % hrefform = 2 + { hrefform #1 = + 'make.href.hypertex % hrefform = 1 + 'make.href.null % hrefform = 0 (or anything else) + if$ + } + if$ +} + +% If inlinelinks is true, then format.url should be a no-op, since it's +% (a) redundant, and (b) could end up as a link-within-a-link. +FUNCTION {format.url} +{ inlinelinks #1 = url empty$ or + { "" } + { hrefform #1 = + { % special case -- add HyperTeX specials + urlintro "\url{" url * "}" * url make.href.hypertex * } + { urlintro "\url{" * url * "}" * } + if$ + } + if$ +} + +FUNCTION {format.eprint} +{ eprint empty$ + { "" } + { eprintprefix eprint * eprinturl eprint * make.href } + if$ +} + +FUNCTION {format.doi} +{ doi empty$ + { "" } + { doiprefix doi * doiurl doi * make.href } + if$ +} + +FUNCTION {format.pubmed} +{ pubmed empty$ + { "" } + { pubmedprefix pubmed * pubmedurl pubmed * make.href } + if$ +} + +% Output a URL. We can't use the more normal idiom (something like +% `format.url output'), because the `inbrackets' within +% format.lastchecked applies to everything between calls to `output', +% so that `format.url format.lastchecked * output' ends up with both +% the URL and the lastchecked in brackets. +FUNCTION {output.url} +{ url empty$ + 'skip$ + { new.block + format.url output + format.lastchecked output + } + if$ +} + +FUNCTION {output.web.refs} +{ + new.block + inlinelinks + 'skip$ % links were inline -- don't repeat them + { + output.url + addeprints eprint empty$ not and + { format.eprint output.nonnull } + 'skip$ + if$ + adddoiresolver doi empty$ not and + { format.doi output.nonnull } + 'skip$ + if$ + addpubmedresolver pubmed empty$ not and + { format.pubmed output.nonnull } + 'skip$ + if$ + } + if$ +} + +% Wrapper for output.bibitem.original. +% If the URL field is not empty, set makeinlinelink to be true, +% so that an inline link will be started at the next opportunity +FUNCTION {output.bibitem} +{ outside.brackets 'bracket.state := + output.bibitem.original + inlinelinks url empty$ not doi empty$ not or pubmed empty$ not or eprint empty$ not or and + { #1 'makeinlinelink := } + { #0 'makeinlinelink := } + if$ +} + +% Wrapper for fin.entry.original +FUNCTION {fin.entry} +{ output.web.refs % urlbst + makeinlinelink % ooops, it appears we didn't have a title for inlinelink + { possibly.setup.inlinelink % add some artificial link text here, as a fallback + linktextstring output.nonnull } + 'skip$ + if$ + bracket.state close.brackets = % urlbst + { "]" * } + 'skip$ + if$ + fin.entry.original +} + +% Webpage entry type. +% Title and url fields required; +% author, note, year, month, and lastchecked fields optional +% See references +% ISO 690-2 http://www.nlc-bnc.ca/iso/tc46sc9/standard/690-2e.htm +% http://www.classroom.net/classroom/CitingNetResources.html +% http://neal.ctstateu.edu/history/cite.html +% http://www.cas.usf.edu/english/walker/mla.html +% for citation formats for web pages. +FUNCTION {webpage} +{ output.bibitem + author empty$ + { editor empty$ + 'skip$ % author and editor both optional + { format.editors output.nonnull } + if$ + } + { editor empty$ + { format.authors output.nonnull } + { "can't use both author and editor fields in " cite$ * warning$ } + if$ + } + if$ + new.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ + format.title "title" output.check + inbrackets onlinestring output + new.block + year empty$ + 'skip$ + { format.date "year" output.check } + if$ + % We don't need to output the URL details ('lastchecked' and 'url'), + % because fin.entry does that for us, using output.web.refs. The only + % reason we would want to put them here is if we were to decide that + % they should go in front of the rather miscellaneous information in 'note'. + new.block + note output + fin.entry +} +% ...urlbst to here + + +FUNCTION {article} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title "title" output.check + new.block + crossref missing$ + { + journal + "journal" bibinfo.check + emphasize + "journal" output.check + possibly.setup.inlinelink format.vol.num.pages output% urlbst + } + { format.article.crossref output.nonnull + format.pages output + } + if$ + new.block + format.note output + fin.entry +} +FUNCTION {book} +{ output.bibitem + author empty$ + { format.editors "author and editor" output.check + editor format.key output + } + { format.authors output.nonnull + crossref missing$ + { "author and editor" editor either.or.check } + 'skip$ + if$ + } + if$ + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.btitle "title" output.check + format.edition output + crossref missing$ + { format.bvolume output + new.block + format.number.series output + new.sentence + format.publisher.address output + } + { + new.block + format.book.crossref output.nonnull + } + if$ + new.block + format.note output + fin.entry +} +FUNCTION {booklet} +{ output.bibitem + format.authors output + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title "title" output.check + new.block + howpublished "howpublished" bibinfo.check output + address "address" bibinfo.check output + new.block + format.note output + fin.entry +} + +FUNCTION {inbook} +{ output.bibitem + author empty$ + { format.editors "author and editor" output.check + editor format.key output + } + { format.authors output.nonnull + crossref missing$ + { "author and editor" editor either.or.check } + 'skip$ + if$ + } + if$ + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.btitle "title" output.check + format.edition output + crossref missing$ + { + format.bvolume output + format.number.series output + format.chapter "chapter" output.check + new.sentence + format.publisher.address output + new.block + } + { + format.chapter "chapter" output.check + new.block + format.book.crossref output.nonnull + } + if$ + new.block + format.note output + fin.entry +} + +FUNCTION {incollection} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title "title" output.check + new.block + crossref missing$ + { format.in.ed.booktitle "booktitle" output.check + format.edition output + format.bvolume output + format.number.series output + format.chapter.pages output + new.sentence + format.publisher.address output + } + { format.incoll.inproc.crossref output.nonnull + format.chapter.pages output + } + if$ + new.block + format.note output + fin.entry +} +FUNCTION {inproceedings} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title "title" output.check + new.block + crossref missing$ + { format.in.booktitle "booktitle" output.check + format.bvolume output + format.number.series output + format.pages output + address "address" bibinfo.check output + new.sentence + organization "organization" bibinfo.check output + publisher "publisher" bibinfo.check output + } + { format.incoll.inproc.crossref output.nonnull + format.pages output + } + if$ + new.block + format.note output + fin.entry +} +FUNCTION {conference} { inproceedings } +FUNCTION {manual} +{ output.bibitem + format.authors output + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.btitle "title" output.check + format.edition output + organization address new.block.checkb + organization "organization" bibinfo.check output + address "address" bibinfo.check output + new.block + format.note output + fin.entry +} + +FUNCTION {mastersthesis} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title + "title" output.check + new.block + bbl.mthesis format.thesis.type output.nonnull + school "school" bibinfo.warn output + address "address" bibinfo.check output + month "month" bibinfo.check output + new.block + format.note output + fin.entry +} + +FUNCTION {misc} +{ output.bibitem + format.authors output + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title output + new.block + howpublished "howpublished" bibinfo.check output + new.block + format.note output + fin.entry +} +FUNCTION {phdthesis} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.btitle + "title" output.check + new.block + bbl.phdthesis format.thesis.type output.nonnull + school "school" bibinfo.warn output + address "address" bibinfo.check output + new.block + format.note output + fin.entry +} + +FUNCTION {proceedings} +{ output.bibitem + format.editors output + editor format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.btitle "title" output.check + format.bvolume output + format.number.series output + new.sentence + publisher empty$ + { format.organization.address output } + { organization "organization" bibinfo.check output + new.sentence + format.publisher.address output + } + if$ + new.block + format.note output + fin.entry +} + +FUNCTION {techreport} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title + "title" output.check + new.block + format.tr.number output.nonnull + institution "institution" bibinfo.warn output + address "address" bibinfo.check output + new.block + format.note output + fin.entry +} + +FUNCTION {unpublished} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title "title" output.check + new.block + format.note "note" output.check + fin.entry +} + +FUNCTION {default.type} { misc } +READ +FUNCTION {sortify} +{ purify$ + "l" change.case$ +} +INTEGERS { len } +FUNCTION {chop.word} +{ 's := + 'len := + s #1 len substring$ = + { s len #1 + global.max$ substring$ } + 's + if$ +} +FUNCTION {format.lab.names} +{ 's := + "" 't := + s #1 "{vv~}{ll}" format.name$ + s num.names$ duplicate$ + #2 > + { pop$ + " " * bbl.etal * + } + { #2 < + 'skip$ + { s #2 "{ff }{vv }{ll}{ jj}" format.name$ "others" = + { + " " * bbl.etal * + } + { bbl.and space.word * s #2 "{vv~}{ll}" format.name$ + * } + if$ + } + if$ + } + if$ +} + +FUNCTION {author.key.label} +{ author empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { author format.lab.names } + if$ +} + +FUNCTION {author.editor.key.label} +{ author empty$ + { editor empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { editor format.lab.names } + if$ + } + { author format.lab.names } + if$ +} + +FUNCTION {editor.key.label} +{ editor empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { editor format.lab.names } + if$ +} + +FUNCTION {calc.short.authors} +{ type$ "book" = + type$ "inbook" = + or + 'author.editor.key.label + { type$ "proceedings" = + 'editor.key.label + 'author.key.label + if$ + } + if$ + 'short.list := +} + +FUNCTION {calc.label} +{ calc.short.authors + short.list + "(" + * + year duplicate$ empty$ + short.list key field.or.null = or + { pop$ "" } + 'skip$ + if$ + * + 'label := +} + +FUNCTION {sort.format.names} +{ 's := + #1 'nameptr := + "" + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + "{ll{ }}{ ff{ }}{ jj{ }}" + format.name$ 't := + nameptr #1 > + { + " " * + namesleft #1 = t "others" = and + { "zzzzz" * } + { t sortify * } + if$ + } + { t sortify * } + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ +} + +FUNCTION {sort.format.title} +{ 't := + "A " #2 + "An " #3 + "The " #4 t chop.word + chop.word + chop.word + sortify + #1 global.max$ substring$ +} +FUNCTION {author.sort} +{ author empty$ + { key empty$ + { "to sort, need author or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { author sort.format.names } + if$ +} +FUNCTION {author.editor.sort} +{ author empty$ + { editor empty$ + { key empty$ + { "to sort, need author, editor, or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { editor sort.format.names } + if$ + } + { author sort.format.names } + if$ +} +FUNCTION {editor.sort} +{ editor empty$ + { key empty$ + { "to sort, need editor or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { editor sort.format.names } + if$ +} +FUNCTION {presort} +{ calc.label + label sortify + " " + * + type$ "book" = + type$ "inbook" = + or + 'author.editor.sort + { type$ "proceedings" = + 'editor.sort + 'author.sort + if$ + } + if$ + #1 entry.max$ substring$ + 'sort.label := + sort.label + * + " " + * + title field.or.null + sort.format.title + * + #1 entry.max$ substring$ + 'sort.key$ := +} + +ITERATE {presort} +SORT +STRINGS { last.label next.extra } +INTEGERS { last.extra.num number.label } +FUNCTION {initialize.extra.label.stuff} +{ #0 int.to.chr$ 'last.label := + "" 'next.extra := + #0 'last.extra.num := + #0 'number.label := +} +FUNCTION {forward.pass} +{ last.label label = + { last.extra.num #1 + 'last.extra.num := + last.extra.num int.to.chr$ 'extra.label := + } + { "a" chr.to.int$ 'last.extra.num := + "" 'extra.label := + label 'last.label := + } + if$ + number.label #1 + 'number.label := +} +FUNCTION {reverse.pass} +{ next.extra "b" = + { "a" 'extra.label := } + 'skip$ + if$ + extra.label 'next.extra := + extra.label + duplicate$ empty$ + 'skip$ + { year field.or.null #-1 #1 substring$ chr.to.int$ #65 < + { "{\natexlab{" swap$ * "}}" * } + { "{(\natexlab{" swap$ * "})}" * } + if$ } + if$ + 'extra.label := + label extra.label * 'label := +} +EXECUTE {initialize.extra.label.stuff} +ITERATE {forward.pass} +REVERSE {reverse.pass} +FUNCTION {bib.sort.order} +{ sort.label + " " + * + year field.or.null sortify + * + " " + * + title field.or.null + sort.format.title + * + #1 entry.max$ substring$ + 'sort.key$ := +} +ITERATE {bib.sort.order} +SORT +FUNCTION {begin.bib} +{ preamble$ empty$ + 'skip$ + { preamble$ write$ newline$ } + if$ + "\begin{thebibliography}{" number.label int.to.str$ * "}" * + write$ newline$ + "\expandafter\ifx\csname natexlab\endcsname\relax\def\natexlab#1{#1}\fi" + write$ newline$ +} +EXECUTE {begin.bib} +EXECUTE {init.urlbst.variables} % urlbst +EXECUTE {init.state.consts} +ITERATE {call.type$} +FUNCTION {end.bib} +{ newline$ + "\end{thebibliography}" write$ newline$ +} +EXECUTE {end.bib} +%% End of customized bst file +%% +%% End of file `compling.bst'. diff --git a/references/2019.arxiv.conneau/source/appendix.tex b/references/2019.arxiv.conneau/source/appendix.tex new file mode 100644 index 0000000000000000000000000000000000000000..c4e6f3983dfebfac53b7c11a894ed59112a1757c --- /dev/null +++ b/references/2019.arxiv.conneau/source/appendix.tex @@ -0,0 +1,45 @@ +\documentclass[11pt,a4paper]{article} +\usepackage[hyperref]{acl2020} +\usepackage{times} +\usepackage{latexsym} +\renewcommand{\UrlFont}{\ttfamily\small} + +% This is not strictly necessary, and may be commented out, +% but it will improve the layout of the manuscript, +% and will typically save some space. +\usepackage{microtype} +\usepackage{graphicx} +\usepackage{subfigure} +\usepackage{booktabs} % for professional tables +\usepackage{url} +\usepackage{times} +\usepackage{latexsym} +\usepackage{array} +\usepackage{adjustbox} +\usepackage{multirow} +% \usepackage{subcaption} +\usepackage{hyperref} +\usepackage{longtable} +\usepackage{bibentry} +\newcommand{\xlmr}{\textit{XLM-R}\xspace} +\newcommand{\mbert}{mBERT\xspace} +\input{content/tables} + +\begin{document} +\nobibliography{acl2020} +\bibliographystyle{acl_natbib} +\appendix +\onecolumn +\section*{Supplementary materials} +\section{Languages and statistics for CC-100 used by \xlmr} +In this section we present the list of languages in the CC-100 corpus we created for training \xlmr. We also report statistics such as the number of tokens and the size of each monolingual corpus. +\label{sec:appendix_A} +\insertDataStatistics + +\newpage +\section{Model Architectures and Sizes} +As we showed in section 5, capacity is an important parameter for learning strong cross-lingual representations. In the table below, we list multiple monolingual and multilingual models used by the research community and summarize their architectures and total number of parameters. +\label{sec:appendix_B} + +\insertParameters +\end{document} \ No newline at end of file diff --git a/references/2019.arxiv.conneau/source/content/batchsize.pdf b/references/2019.arxiv.conneau/source/content/batchsize.pdf new file mode 100644 index 0000000000000000000000000000000000000000..712fff8991344922137bc52d374e3b0366c5eebb --- /dev/null +++ b/references/2019.arxiv.conneau/source/content/batchsize.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0e0c4e1c156379efeba93f0c1a6717bb12ab0b2aa0bdd361a7fda362ff01442e +size 14673 diff --git a/references/2019.arxiv.conneau/source/content/capacity.pdf b/references/2019.arxiv.conneau/source/content/capacity.pdf new file mode 100644 index 0000000000000000000000000000000000000000..0341e5fff6d3d8b4ab9129c074d23562f46ff10f --- /dev/null +++ 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\begin{table*}[h!] + \begin{center} + % \scriptsize + \resizebox{1\linewidth}{!}{ + \begin{tabular}[b]{l ccc ccccccccccccccc c} + \toprule + {\bf Model} & {\bf D }& {\bf \#M} & {\bf \#lg} & {\bf en} & {\bf fr} & {\bf es} & {\bf de} & {\bf el} & {\bf bg} & {\bf ru} & {\bf tr} & {\bf ar} & {\bf vi} & {\bf th} & {\bf zh} & {\bf hi} & {\bf sw} & {\bf ur} & {\bf Avg}\\ + \midrule + %\cmidrule(r){1-1} + %\cmidrule(lr){2-4} + %\cmidrule(lr){5-19} + %\cmidrule(l){20-20} + + \multicolumn{19}{l}{\it Fine-tune multilingual model on English training set (Cross-lingual Transfer)} \\ + %\midrule + \midrule + \citet{lample2019cross} & Wiki+MT & N & 15 & 85.0 & 78.7 & 78.9 & 77.8 & 76.6 & 77.4 & 75.3 & 72.5 & 73.1 & 76.1 & 73.2 & 76.5 & 69.6 & 68.4 & 67.3 & 75.1 \\ + \citet{huang2019unicoder} & Wiki+MT & N & 15 & 85.1 & 79.0 & 79.4 & 77.8 & 77.2 & 77.2 & 76.3 & 72.8 & 73.5 & 76.4 & 73.6 & 76.2 & 69.4 & 69.7 & 66.7 & 75.4 \\ + %\midrule + \citet{devlin2018bert} & Wiki & N & 102 & 82.1 & 73.8 & 74.3 & 71.1 & 66.4 & 68.9 & 69.0 & 61.6 & 64.9 & 69.5 & 55.8 & 69.3 & 60.0 & 50.4 & 58.0 & 66.3 \\ + \citet{lample2019cross} & Wiki & N & 100 & 83.7 & 76.2 & 76.6 & 73.7 & 72.4 & 73.0 & 72.1 & 68.1 & 68.4 & 72.0 & 68.2 & 71.5 & 64.5 & 58.0 & 62.4 & 71.3 \\ + \citet{lample2019cross} & Wiki & 1 & 100 & 83.2 & 76.7 & 77.7 & 74.0 & 72.7 & 74.1 & 72.7 & 68.7 & 68.6 & 72.9 & 68.9 & 72.5 & 65.6 & 58.2 & 62.4 & 70.7 \\ + \bf XLM-R\textsubscript{Base} & CC & 1 & 100 & 85.8 & 79.7 & 80.7 & 78.7 & 77.5 & 79.6 & 78.1 & 74.2 & 73.8 & 76.5 & 74.6 & 76.7 & 72.4 & 66.5 & 68.3 & 76.2 \\ + \bf XLM-R & CC & 1 & 100 & \bf 89.1 & \bf 84.1 & \bf 85.1 & \bf 83.9 & \bf 82.9 & \bf 84.0 & \bf 81.2 & \bf 79.6 & \bf 79.8 & \bf 80.8 & \bf 78.1 & \bf 80.2 & \bf 76.9 & \bf 73.9 & \bf 73.8 & \bf 80.9 \\ + \midrule + \multicolumn{19}{l}{\it Translate everything to English and use English-only model (TRANSLATE-TEST)} \\ + \midrule + BERT-en & Wiki & 1 & 1 & 88.8 & 81.4 & 82.3 & 80.1 & 80.3 & 80.9 & 76.2 & 76.0 & 75.4 & 72.0 & 71.9 & 75.6 & 70.0 & 65.8 & 65.8 & 76.2 \\ + RoBERTa & Wiki+CC & 1 & 1 & \underline{\bf 91.3} & 82.9 & 84.3 & 81.2 & 81.7 & 83.1 & 78.3 & 76.8 & 76.6 & 74.2 & 74.1 & 77.5 & 70.9 & 66.7 & 66.8 & 77.8 \\ + % XLM-en & Wiki & 1 & 1 & 00.0 & 00.0 & 00.0 & 00.0 & 00.0 & 00.0 & 00.0 \\ + \midrule + \multicolumn{19}{l}{\it Fine-tune multilingual model on each training set (TRANSLATE-TRAIN)} \\ + \midrule + \citet{lample2019cross} & Wiki & N & 100 & 82.9 & 77.6 & 77.9 & 77.9 & 77.1 & 75.7 & 75.5 & 72.6 & 71.2 & 75.8 & 73.1 & 76.2 & 70.4 & 66.5 & 62.4 & 74.2 \\ + \midrule + \multicolumn{19}{l}{\it Fine-tune multilingual model on all training sets (TRANSLATE-TRAIN-ALL)} \\ + \midrule + \citet{lample2019cross}$^{\dagger}$ & Wiki+MT & 1 & 15 & 85.0 & 80.8 & 81.3 & 80.3 & 79.1 & 80.9 & 78.3 & 75.6 & 77.6 & 78.5 & 76.0 & 79.5 & 72.9 & 72.8 & 68.5 & 77.8 \\ + \citet{huang2019unicoder} & Wiki+MT & 1 & 15 & 85.6 & 81.1 & 82.3 & 80.9 & 79.5 & 81.4 & 79.7 & 76.8 & 78.2 & 77.9 & 77.1 & 80.5 & 73.4 & 73.8 & 69.6 & 78.5 \\ + %\midrule + \citet{lample2019cross} & Wiki & 1 & 100 & 84.5 & 80.1 & 81.3 & 79.3 & 78.6 & 79.4 & 77.5 & 75.2 & 75.6 & 78.3 & 75.7 & 78.3 & 72.1 & 69.2 & 67.7 & 76.9 \\ + \bf XLM-R\textsubscript{Base} & CC & 1 & 100 & 85.4 & 81.4 & 82.2 & 80.3 & 80.4 & 81.3 & 79.7 & 78.6 & 77.3 & 79.7 & 77.9 & 80.2 & 76.1 & 73.1 & 73.0 & 79.1 \\ + \bf XLM-R & CC & 1 & 100 & \bf 89.1 & \underline{\bf 85.1} & \underline{\bf 86.6} & \underline{\bf 85.7} & \underline{\bf 85.3} & \underline{\bf 85.9} & \underline{\bf 83.5} & \underline{\bf 83.2} & \underline{\bf 83.1} & \underline{\bf 83.7} & \underline{\bf 81.5} & \underline{\bf 83.7} & \underline{\bf 81.6} & \underline{\bf 78.0} & \underline{\bf 78.1} & \underline{\bf 83.6} \\ + \bottomrule + \end{tabular} + } + \caption{\textbf{Results on cross-lingual classification.} We report the accuracy on each of the 15 XNLI languages and the average accuracy. We specify the dataset D used for pretraining, the number of models \#M the approach requires and the number of languages \#lg the model handles. Our \xlmr results are averaged over five different seeds. We show that using the translate-train-all approach which leverages training sets from multiple languages, \xlmr obtains a new state of the art on XNLI of $83.6$\% average accuracy. Results with $^{\dagger}$ are from \citet{huang2019unicoder}. %It also outperforms previous methods on cross-lingual transfer. + \label{tab:xnli}} + \end{center} +% \vspace{-0.4cm} + \end{table*} +} + +% Evolution of performance w.r.t number of languages +\newcommand{\insertLanguagesize}{ + \begin{table*}[h!] + \begin{minipage}{0.49\textwidth} + \includegraphics[scale=0.4]{content/wiki_vs_cc.pdf} + \end{minipage} + \hfill + \begin{minipage}{0.4\textwidth} + \captionof{figure}{\textbf{Distribution of the amount of data (in MB) per language for Wikipedia and CommonCrawl.} The Wikipedia data used in open-source mBERT and XLM is not sufficient for the model to develop an understanding of low-resource languages. The CommonCrawl data we collect alleviates that issue and creates the conditions for a single model to understand text coming from multiple languages. \label{fig:lgs}} + \end{minipage} +% \vspace{-0.5cm} + \end{table*} +} + +% Evolution of performance w.r.t number of languages +\newcommand{\insertXLMmorelanguages}{ + \begin{table*}[h!] + \begin{minipage}{0.49\textwidth} + \includegraphics[scale=0.4]{content/evolution_languages} + \end{minipage} + \hfill + \begin{minipage}{0.4\textwidth} + \captionof{figure}{\textbf{Evolution of XLM performance on SeqLab, XNLI and GLUE as the number of languages increases.} While there are subtlteties as to what languages lose more accuracy than others as we add more languages, we observe a steady decrease of the overall monolingual and cross-lingual performance. \label{fig:lgsunused}} + \end{minipage} +% \vspace{-0.5cm} + \end{table*} +} + +\newcommand{\insertMLQA}{ +\begin{table*}[h!] + \begin{center} + % \scriptsize + \resizebox{1\linewidth}{!}{ + \begin{tabular}[h]{l cc ccccccc c} + \toprule + {\bf Model} & {\bf train} & {\bf \#lgs} & {\bf en} & {\bf es} & {\bf de} & {\bf ar} & {\bf hi} & {\bf vi} & {\bf zh} & {\bf Avg} \\ + \midrule + BERT-Large$^{\dagger}$ & en & 1 & 80.2 / 67.4 & - & - & - & - & - & - & - \\ + mBERT$^{\dagger}$ & en & 102 & 77.7 / 65.2 & 64.3 / 46.6 & 57.9 / 44.3 & 45.7 / 29.8 & 43.8 / 29.7 & 57.1 / 38.6 & 57.5 / 37.3 & 57.7 / 41.6 \\ + XLM-15$^{\dagger}$ & en & 15 & 74.9 / 62.4 & 68.0 / 49.8 & 62.2 / 47.6 & 54.8 / 36.3 & 48.8 / 27.3 & 61.4 / 41.8 & 61.1 / 39.6 & 61.6 / 43.5 \\ + XLM-R\textsubscript{Base} & en & 100 & 77.1 / 64.6 & 67.4 / 49.6 & 60.9 / 46.7 & 54.9 / 36.6 & 59.4 / 42.9 & 64.5 / 44.7 & 61.8 / 39.3 & 63.7 / 46.3 \\ + \bf XLM-R & en & 100 & \bf 80.6 / 67.8 & \bf 74.1 / 56.0 & \bf 68.5 / 53.6 & \bf 63.1 / 43.5 & \bf 69.2 / 51.6 & \bf 71.3 / 50.9 & \bf 68.0 / 45.4 & \bf 70.7 / 52.7 \\ + \bottomrule + \end{tabular} + } + \caption{\textbf{Results on MLQA question answering} We report the F1 and EM (exact match) scores for zero-shot classification where models are fine-tuned on the English Squad dataset and evaluated on the 7 languages of MLQA. Results with $\dagger$ are taken from the original MLQA paper \citet{lewis2019mlqa}. + \label{tab:mlqa}} + \end{center} +\end{table*} +} + +\newcommand{\insertNER}{ +\begin{table}[t] + \begin{center} + % \scriptsize + \resizebox{1\linewidth}{!}{ + \begin{tabular}[b]{l cc cccc c} + \toprule + {\bf Model} & {\bf train} & {\bf \#M} & {\bf en} & {\bf nl} & {\bf es} & {\bf de} & {\bf Avg}\\ + \midrule + \citet{lample-etal-2016-neural} & each & N & 90.74 & 81.74 & 85.75 & 78.76 & 84.25 \\ + \citet{akbik2018coling} & each & N & \bf 93.18 & 90.44 & - & \bf 88.27 & - \\ + \midrule + \multirow{2}{*}{mBERT$^{\dagger}$} & each & N & 91.97 & 90.94 & 87.38 & 82.82 & 88.28\\ + & en & 1 & 91.97 & 77.57 & 74.96 & 69.56 & 78.52\\ + \midrule + \multirow{3}{*}{XLM-R\textsubscript{Base}} & each & N & 92.25 & 90.39 & 87.99 & 84.60 & 88.81\\ + & en & 1 & 92.25 & 78.08 & 76.53 & 69.60 & 79.11\\ + & all & 1 & 91.08 & 89.09 & 87.28 & 83.17 & 87.66 \\ + \midrule + \multirow{3}{*}{\bf XLM-R} & each & N & 92.92 & \bf 92.53 & \bf 89.72 & 85.81 & 90.24\\ + & en & 1 & 92.92 & 80.80 & 78.64 & 71.40 & 80.94\\ + & all & 1 & 92.00 & 91.60 & 89.52 & 84.60 & 89.43 \\ + \bottomrule + \end{tabular} + } + \caption{\textbf{Results on named entity recognition} on CoNLL-2002 and CoNLL-2003 (F1 score). Results with $\dagger$ are from \citet{wu2019beto}. Note that mBERT and \xlmr do not use a linear-chain CRF, as opposed to \citet{akbik2018coling} and \citet{lample-etal-2016-neural}. + \label{tab:ner}} + \end{center} + \vspace{-0.6cm} + \end{table} +} + + +\newcommand{\insertAblationone}{ +\begin{table*}[h!] + \begin{minipage}[t]{0.3\linewidth} + \begin{center} + %\includegraphics[width=\linewidth]{content/xlmroberta_transfer_dilution.pdf} + \includegraphics{content/dilution} + \captionof{figure}{The transfer-interference trade-off: Low-resource languages benefit from scaling to more languages, until dilution (interference) kicks in and degrades overall performance.} + \label{fig:transfer_dilution} + \vspace{-0.2cm} + \end{center} + \end{minipage} + \hfill + \begin{minipage}[t]{0.3\linewidth} + \begin{center} + %\includegraphics[width=\linewidth]{content/xlmroberta_evolution.pdf} + \includegraphics{content/wikicc} + \captionof{figure}{Wikipedia versus CommonCrawl: An XLM-7 obtains significantly better performance when trained on CC, in particular on low-resource languages.} + \label{fig:curse} + \end{center} + \end{minipage} + \hfill + \begin{minipage}[t]{0.3\linewidth} + \begin{center} + % \includegraphics[width=\linewidth]{content/xlmroberta_evolution.pdf} + \includegraphics{content/capacity} + \captionof{figure}{Adding more capacity to the model alleviates the curse of multilinguality, but remains an issue for models of moderate size.} + \label{fig:capacity} + \end{center} + \end{minipage} + \vspace{-0.2cm} +\end{table*} +} + + +\newcommand{\insertAblationtwo}{ +\begin{table*}[h!] + \begin{minipage}[t]{0.3\linewidth} + \begin{center} + %\includegraphics[width=\columnwidth]{content/xlmroberta_alpha_tradeoff.pdf} + \includegraphics{content/langsampling} + \captionof{figure}{On the high-resource versus low-resource trade-off: impact of batch language sampling for XLM-100. + \label{fig:alpha}} + \end{center} + \end{minipage} + \hfill + \begin{minipage}[t]{0.3\linewidth} + \begin{center} + %\includegraphics[width=\columnwidth]{content/xlmroberta_vocab.pdf} + \includegraphics{content/vocabsize.pdf} + \captionof{figure}{On the impact of vocabulary size at fixed capacity and with increasing capacity for XLM-100. + \label{fig:vocab}} + \end{center} + \end{minipage} + \hfill + \begin{minipage}[t]{0.3\linewidth} + \begin{center} + %\includegraphics[width=\columnwidth]{content/xlmroberta_batch_and_tok.pdf} + \includegraphics{content/batchsize.pdf} + \captionof{figure}{On the impact of large-scale training, and preprocessing simplification from BPE with tokenization to SPM on raw text data. + \label{fig:batch}} + \end{center} + \end{minipage} + \vspace{-0.2cm} +\end{table*} +} + + +% Multilingual vs monolingual +\newcommand{\insertMultiMono}{ + \begin{table}[h!] + \begin{center} + % \scriptsize + \resizebox{1\linewidth}{!}{ + \begin{tabular}[b]{l cc ccccccc c} + \toprule + {\bf Model} & {\bf D } & {\bf \#vocab} & {\bf en} & {\bf fr} & {\bf de} & {\bf ru} & {\bf zh} & {\bf sw} & {\bf ur} & {\bf Avg}\\ + \midrule + \multicolumn{11}{l}{\it Monolingual baselines}\\ + \midrule + \multirow{2}{*}{BERT} & Wiki & 40k & 84.5 & 78.6 & 80.0 & 75.5 & 77.7 & 60.1 & 57.3 & 73.4 \\ + & CC & 40k & 86.7 & 81.2 & 81.2 & 78.2 & 79.5 & 70.8 & 65.1 & 77.5 \\ + \midrule + \multicolumn{11}{l}{\it Multilingual models (cross-lingual transfer)}\\ + \midrule + \multirow{2}{*}{XLM-7} & Wiki & 150k & 82.3 & 76.8 & 74.7 & 72.5 & 73.1 & 60.8 & 62.3 & 71.8 \\ + & CC & 150k & 85.7 & 78.6 & 79.5 & 76.4 & 74.8 & 71.2 & 66.9 & 76.2 \\ + \midrule + \multicolumn{11}{l}{\it Multilingual models (translate-train-all)}\\ + \midrule + \multirow{2}{*}{XLM-7} & Wiki & 150k & 84.6 & 80.1 & 80.2 & 75.7 & 78 & 68.7 & 66.7 & 76.3 \\ + & CC & 150k & \bf 87.2 & \bf 82.5 & \bf 82.9 & \bf 79.7 & \bf 80.4 & \bf 75.7 & \bf 71.5 & \bf 80.0 \\ + % \midrule + % XLM (sw,ar) & CC & 60k & N & 2-3 & - & - & - & - & - & 00.0 & - & 00.0 \\ + % XLM (ur,hi,ar) & CC & 60k & N & 2-3 & - & - & - & - & - & - & 00.0 & 00.0 \\ + \bottomrule + \end{tabular} + } + \caption{\textbf{Multilingual versus monolingual models (BERT-BASE).} We compare the performance of monolingual models (BERT) versus multilingual models (XLM) on seven languages, using a BERT-BASE architecture. We choose a vocabulary size of 40k and 150k for monolingual and multilingual models. + \label{tab:multimono}} + \end{center} + \vspace{-0.4cm} + \end{table} +} + +% GLUE benchmark results +\newcommand{\insertGlue}{ + \begin{table}[h!] + \begin{center} + % \scriptsize + \resizebox{1\linewidth}{!}{ + \begin{tabular}[b]{l|c|cccccc|c} + \toprule + {\bf Model} & {\bf \#lgs} & {\bf MNLI-m/mm} & {\bf QNLI} & {\bf QQP} & {\bf SST} & {\bf MRPC} & {\bf STS-B} & {\bf Avg}\\ + \midrule + BERT\textsubscript{Large}$^{\dagger}$ & 1 & 86.6/- & 92.3 & 91.3 & 93.2 & 88.0 & 90.0 & 90.2 \\ + XLNet\textsubscript{Large}$^{\dagger}$ & 1 & 89.8/- & 93.9 & 91.8 & 95.6 & 89.2 & 91.8 & 92.0 \\ + RoBERTa$^{\dagger}$ & 1 & 90.2/90.2 & 94.7 & 92.2 & 96.4 & 90.9 & 92.4 & 92.8 \\ + XLM-R & 100 & 88.9/89.0 & 93.8 & 92.3 & 95.0 & 89.5 & 91.2 & 91.8 \\ + \bottomrule + \end{tabular} + } + \caption{\textbf{GLUE dev results.} Results with $^{\dagger}$ are from \citet{roberta2019}. We compare the performance of \xlmr to BERT\textsubscript{Large}, XLNet and RoBERTa on the English GLUE benchmark. + \label{tab:glue}} + \end{center} + \vspace{-0.4cm} + \end{table} +} + + +% Wiki vs CommonCrawl statistics +\newcommand{\insertWikivsCC}{ + \begin{table*}[h] + \begin{center} + %\includegraphics[width=\linewidth]{content/wiki_vs_cc.pdf} + \includegraphics{content/datasize.pdf} + \captionof{figure}{Amount of data in GiB (log-scale) for the 88 languages that appear in both the Wiki-100 corpus used for mBERT and XLM-100, and the CC-100 used for XLM-R. CC-100 increases the amount of data by several orders of magnitude, in particular for low-resource languages. + \label{fig:wikivscc}} + \end{center} +% \vspace{-0.4cm} + \end{table*} +} + +% Corpus statistics for CC-100 +\newcommand{\insertDataStatistics}{ +%\resizebox{1\linewidth}{!}{ +\begin{table}[h!] +\begin{center} +\small +\begin{tabular}[b]{clrrclrr} +\toprule +\textbf{ISO code} & \textbf{Language} & \textbf{Tokens} (M) & \textbf{Size} (GiB) & \textbf{ISO code} & \textbf{Language} & \textbf{Tokens} (M) & \textbf{Size} (GiB)\\ +\cmidrule(r){1-4}\cmidrule(l){5-8} +{\bf af }& Afrikaans & 242 & 1.3 &{\bf lo }& Lao & 17 & 0.6 \\ +{\bf am }& Amharic & 68 & 0.8 &{\bf lt }& Lithuanian & 1835 & 13.7 \\ +{\bf ar }& Arabic & 2869 & 28.0 &{\bf lv }& Latvian & 1198 & 8.8 \\ +{\bf as }& Assamese & 5 & 0.1 &{\bf mg }& Malagasy & 25 & 0.2 \\ +{\bf az }& Azerbaijani & 783 & 6.5 &{\bf mk }& Macedonian & 449 & 4.8 \\ +{\bf be }& Belarusian & 362 & 4.3 &{\bf ml }& Malayalam & 313 & 7.6 \\ +{\bf bg }& Bulgarian & 5487 & 57.5 &{\bf mn }& Mongolian & 248 & 3.0 \\ +{\bf bn }& Bengali & 525 & 8.4 &{\bf mr }& Marathi & 175 & 2.8 \\ +{\bf - }& Bengali Romanized & 77 & 0.5 &{\bf ms }& Malay & 1318 & 8.5 \\ +{\bf br }& Breton & 16 & 0.1 &{\bf my }& Burmese & 15 & 0.4 \\ +{\bf bs }& Bosnian & 14 & 0.1 &{\bf my }& Burmese & 56 & 1.6 \\ +{\bf ca }& Catalan & 1752 & 10.1 &{\bf ne }& Nepali & 237 & 3.8 \\ +{\bf cs }& Czech & 2498 & 16.3 &{\bf nl }& Dutch & 5025 & 29.3 \\ +{\bf cy }& Welsh & 141 & 0.8 &{\bf no }& Norwegian & 8494 & 49.0 \\ +{\bf da }& Danish & 7823 & 45.6 &{\bf om }& Oromo & 8 & 0.1 \\ +{\bf de }& German & 10297 & 66.6 &{\bf or }& Oriya & 36 & 0.6 \\ +{\bf el }& Greek & 4285 & 46.9 &{\bf pa }& Punjabi & 68 & 0.8 \\ +{\bf en }& English & 55608 & 300.8 &{\bf pl }& Polish & 6490 & 44.6 \\ +{\bf eo }& Esperanto & 157 & 0.9 &{\bf ps }& Pashto & 96 & 0.7 \\ +{\bf es }& Spanish & 9374 & 53.3 &{\bf pt }& Portuguese & 8405 & 49.1 \\ +{\bf et }& Estonian & 843 & 6.1 &{\bf ro }& Romanian & 10354 & 61.4 \\ +{\bf eu }& Basque & 270 & 2.0 &{\bf ru }& Russian & 23408 & 278.0 \\ +{\bf fa }& Persian & 13259 & 111.6 &{\bf sa }& Sanskrit & 17 & 0.3 \\ +{\bf fi }& Finnish & 6730 & 54.3 &{\bf sd }& Sindhi & 50 & 0.4 \\ +{\bf fr }& French & 9780 & 56.8 &{\bf si }& Sinhala & 243 & 3.6 \\ +{\bf fy }& Western Frisian & 29 & 0.2 &{\bf sk }& Slovak & 3525 & 23.2 \\ +{\bf ga }& Irish & 86 & 0.5 &{\bf sl }& Slovenian & 1669 & 10.3 \\ +{\bf gd }& Scottish Gaelic & 21 & 0.1 &{\bf so }& Somali & 62 & 0.4 \\ +{\bf gl }& Galician & 495 & 2.9 &{\bf sq }& Albanian & 918 & 5.4 \\ +{\bf gu }& Gujarati & 140 & 1.9 &{\bf sr }& Serbian & 843 & 9.1 \\ +{\bf ha }& Hausa & 56 & 0.3 &{\bf su }& Sundanese & 10 & 0.1 \\ +{\bf he }& Hebrew & 3399 & 31.6 &{\bf sv }& Swedish & 77.8 & 12.1 \\ +{\bf hi }& Hindi & 1715 & 20.2 &{\bf sw }& Swahili & 275 & 1.6 \\ +{\bf - }& Hindi Romanized & 88 & 0.5 &{\bf ta }& Tamil & 595 & 12.2 \\ +{\bf hr }& Croatian & 3297 & 20.5 &{\bf - }& Tamil Romanized & 36 & 0.3 \\ +{\bf hu }& Hungarian & 7807 & 58.4 &{\bf te }& Telugu & 249 & 4.7 \\ +{\bf hy }& Armenian & 421 & 5.5 &{\bf - }& Telugu Romanized & 39 & 0.3 \\ +{\bf id }& Indonesian & 22704 & 148.3 &{\bf th }& Thai & 1834 & 71.7 \\ +{\bf is }& Icelandic & 505 & 3.2 &{\bf tl }& Filipino & 556 & 3.1 \\ +{\bf it }& Italian & 4983 & 30.2 &{\bf tr }& Turkish & 2736 & 20.9 \\ +{\bf ja }& Japanese & 530 & 69.3 &{\bf ug }& Uyghur & 27 & 0.4 \\ +{\bf jv }& Javanese & 24 & 0.2 &{\bf uk }& Ukrainian & 6.5 & 84.6 \\ +{\bf ka }& Georgian & 469 & 9.1 &{\bf ur }& Urdu & 730 & 5.7 \\ +{\bf kk }& Kazakh & 476 & 6.4 &{\bf - }& Urdu Romanized & 85 & 0.5 \\ +{\bf km }& Khmer & 36 & 1.5 &{\bf uz }& Uzbek & 91 & 0.7 \\ +{\bf kn }& Kannada & 169 & 3.3 &{\bf vi }& Vietnamese & 24757 & 137.3 \\ +{\bf ko }& Korean & 5644 & 54.2 &{\bf xh }& Xhosa & 13 & 0.1 \\ +{\bf ku }& Kurdish (Kurmanji) & 66 & 0.4 &{\bf yi }& Yiddish & 34 & 0.3 \\ +{\bf ky }& Kyrgyz & 94 & 1.2 &{\bf zh }& Chinese (Simplified) & 259 & 46.9 \\ +{\bf la }& Latin & 390 & 2.5 &{\bf zh }& Chinese (Traditional) & 176 & 16.6 \\ + +\bottomrule +\end{tabular} +\caption{\textbf{Languages and statistics of the CC-100 corpus.} We report the list of 100 languages and include the number of tokens (Millions) and the size of the data (in GiB) for each language. Note that we also include romanized variants of some non latin languages such as Bengali, Hindi, Tamil, Telugu and Urdu.\label{tab:datastats}} +\end{center} +\end{table} +%} +} + + +% Comparison of parameters for different models +\newcommand{\insertParameters}{ + \begin{table*}[h!] + \begin{center} + % \scriptsize + %\resizebox{1\linewidth}{!}{ + \begin{tabular}[b]{lrcrrrrrc} + \toprule + \textbf{Model} & \textbf{\#lgs} & \textbf{tokenization} & \textbf{L} & \textbf{$H_{m}$} & \textbf{$H_{ff}$} & \textbf{A} & \textbf{V} & \textbf{\#params}\\ + \cmidrule(r){1-1} + \cmidrule(lr){2-3} + \cmidrule(lr){4-8} + \cmidrule(l){9-9} + % TODO: rank by number of parameters + BERT\textsubscript{Base} & 1 & WordPiece & 12 & 768 & 3072 & 12 & 30k & 110M \\ + BERT\textsubscript{Large} & 1 & WordPiece & 24 & 1024 & 4096 & 16 & 30k & 335M \\ + mBERT & 104 & WordPiece & 12 & 768 & 3072 & 12 & 110k & 172M \\ + RoBERTa\textsubscript{Base} & 1 & bBPE & 12 & 768 & 3072 & 8 & 50k & 125M \\ + RoBERTa & 1 & bBPE & 24 & 1024 & 4096 & 16 & 50k & 355M \\ + XLM-15 & 15 & BPE & 12 & 1024 & 4096 & 8 & 95k & 250M \\ + XLM-17 & 17 & BPE & 16 & 1280 & 5120 & 16 & 200k & 570M \\ + XLM-100 & 100 & BPE & 16 & 1280 & 5120 & 16 & 200k & 570M \\ + Unicoder & 15 & BPE & 12 & 1024 & 4096 & 8 & 95k & 250M \\ + \xlmr\textsubscript{Base} & 100 & SPM & 12 & 768 & 3072 & 12 & 250k & 270M \\ + \xlmr & 100 & SPM & 24 & 1024 & 4096 & 16 & 250k & 550M \\ + GPT2 & 1 & bBPE & 48 & 1600 & 6400 & 32 & 50k & 1.5B \\ + wide-mmNMT & 103 & SPM & 12 & 2048 & 16384 & 32 & 64k & 3B \\ + deep-mmNMT & 103 & SPM & 24 & 1024 & 16384 & 32 & 64k & 3B \\ + T5-3B & 1 & WordPiece & 24 & 1024 & 16384 & 32 & 32k & 3B \\ + T5-11B & 1 & WordPiece & 24 & 1024 & 65536 & 32 & 32k & 11B \\ + % XLNet\textsubscript{Large}$^{\dagger}$ & 1 & 89.8/- & 93.9 & 91.8 & 95.6 & 89.2 & 91.8 & 92.0 \\ + % RoBERTa$^{\dagger}$ & 1 & 90.2/90.2 & 94.7 & 92.2 & 96.4 & 90.9 & 92.4 & 92.8 \\ + % XLM-R & 100 & 88.4/88.5 & 93.1 & 92.2 & 95.1 & 89.7 & 90.4 & 91.5 \\ + \bottomrule + \end{tabular} + %} + \caption{\textbf{Details on model sizes.} + We show the tokenization used by each Transformer model, the number of layers L, the number of hidden states of the model $H_{m}$, the dimension of the feed-forward layer $H_{ff}$, the number of attention heads A, the size of the vocabulary V and the total number of parameters \#params. + For Transformer encoders, the number of parameters can be approximated by $4LH_m^2 + 2LH_m H_{ff} + VH_m$. + GPT2 numbers are from \citet{radford2019language}, mm-NMT models are from the work of \citet{arivazhagan2019massively} on massively multilingual neural machine translation (mmNMT), and T5 numbers are from \citet{raffel2019exploring}. While \xlmr is among the largest models partly due to its large embedding layer, it has a similar number of parameters than XLM-100, and remains significantly smaller that recently introduced Transformer models for multilingual MT and transfer learning. While this table gives more hindsight on the difference of capacity of each model, note it does not highlight other critical differences between the models. + \label{tab:parameters}} + \end{center} + \vspace{-0.4cm} + \end{table*} +} \ No newline at end of file diff --git a/references/2019.arxiv.conneau/source/content/vocabsize.pdf b/references/2019.arxiv.conneau/source/content/vocabsize.pdf new file mode 100644 index 0000000000000000000000000000000000000000..b6aaa736ba18593c6809ba7f7d403920f348ef4d --- /dev/null +++ b/references/2019.arxiv.conneau/source/content/vocabsize.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e45090856dc149265ada0062c8c2456c3057902dfaaade60aa80905785563506 +size 15677 diff --git a/references/2019.arxiv.conneau/source/content/wikicc.pdf b/references/2019.arxiv.conneau/source/content/wikicc.pdf new file mode 100644 index 0000000000000000000000000000000000000000..b0271d1a2d035ec2e76fe25e62eca0ff48038ffa --- /dev/null +++ b/references/2019.arxiv.conneau/source/content/wikicc.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f0d7e959db8240f283922c3ca7c6de6f5ad3750681f27f4fcf35d161506a7a21 +size 16304 diff --git a/references/2019.arxiv.conneau/source/texput.log b/references/2019.arxiv.conneau/source/texput.log new file mode 100644 index 0000000000000000000000000000000000000000..3b64d04a3048c2da93d41ce759b4dca2fa85dbe2 --- /dev/null +++ b/references/2019.arxiv.conneau/source/texput.log @@ -0,0 +1,21 @@ +This is pdfTeX, Version 3.14159265-2.6-1.40.20 (TeX Live 2019) (preloaded format=pdflatex 2019.5.8) 7 APR 2020 17:41 +entering extended mode + restricted \write18 enabled. + %&-line parsing enabled. +**acl2020.tex + +! Emergency stop. +<*> acl2020.tex + +*** (job aborted, file error in nonstop mode) + + +Here is how much of TeX's memory you used: + 3 strings out of 492616 + 102 string characters out of 6129482 + 57117 words of memory out of 5000000 + 4025 multiletter control sequences out of 15000+600000 + 3640 words of font info for 14 fonts, out of 8000000 for 9000 + 1141 hyphenation exceptions out of 8191 + 0i,0n,0p,1b,6s stack positions out of 5000i,500n,10000p,200000b,80000s +! ==> Fatal error occurred, no output PDF file produced! diff --git a/references/2019.arxiv.conneau/source/xlmr.bbl b/references/2019.arxiv.conneau/source/xlmr.bbl new file mode 100644 index 0000000000000000000000000000000000000000..264cff4aa65ba39bd36e75804c0b6cf22c037fba --- /dev/null +++ b/references/2019.arxiv.conneau/source/xlmr.bbl @@ -0,0 +1,285 @@ +\begin{thebibliography}{40} +\expandafter\ifx\csname natexlab\endcsname\relax\def\natexlab#1{#1}\fi + +\bibitem[{Akbik et~al.(2018)Akbik, Blythe, and Vollgraf}]{akbik2018coling} +Alan Akbik, Duncan Blythe, and Roland Vollgraf. 2018. +\newblock Contextual string embeddings for sequence labeling. +\newblock In \emph{COLING}, pages 1638--1649. + +\bibitem[{Arivazhagan et~al.(2019)Arivazhagan, Bapna, Firat, Lepikhin, Johnson, + Krikun, Chen, Cao, Foster, Cherry et~al.}]{arivazhagan2019massively} +Naveen Arivazhagan, Ankur Bapna, Orhan Firat, Dmitry Lepikhin, Melvin Johnson, + Maxim Krikun, Mia~Xu Chen, Yuan Cao, George Foster, Colin Cherry, et~al. + 2019. +\newblock Massively multilingual neural machine translation in the wild: + Findings and challenges. +\newblock \emph{arXiv preprint arXiv:1907.05019}. + +\bibitem[{Bowman et~al.(2015)Bowman, Angeli, Potts, and + Manning}]{bowman2015large} +Samuel~R. 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Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. +\newblock Attention is all you need. +\newblock In \emph{Advances in Neural Information Processing Systems}, pages + 6000--6010. + +\bibitem[{Wang et~al.(2018)Wang, Singh, Michael, Hill, Levy, and + Bowman}]{wang2018glue} +Alex Wang, Amapreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel~R + Bowman. 2018. +\newblock Glue: A multi-task benchmark and analysis platform for natural + language understanding. +\newblock \emph{arXiv preprint arXiv:1804.07461}. + +\bibitem[{Wenzek et~al.(2019)Wenzek, Lachaux, Conneau, Chaudhary, Guzman, + Joulin, and Grave}]{wenzek2019ccnet} +Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, Vishrav Chaudhary, + Francisco Guzman, Armand Joulin, and Edouard Grave. 2019. +\newblock Ccnet: Extracting high quality monolingual datasets from web crawl + data. +\newblock \emph{arXiv preprint arXiv:1911.00359}. + +\bibitem[{Williams et~al.(2017)Williams, Nangia, and + Bowman}]{williams2017broad} +Adina Williams, Nikita Nangia, and Samuel~R Bowman. 2017. +\newblock A broad-coverage challenge corpus for sentence understanding through + inference. +\newblock \emph{Proceedings of the 2nd Workshop on Evaluating Vector-Space + Representations for NLP}. + +\bibitem[{Wu et~al.(2019)Wu, Conneau, Li, Zettlemoyer, and + Stoyanov}]{wu2019emerging} +Shijie Wu, Alexis Conneau, Haoran Li, Luke Zettlemoyer, and Veselin Stoyanov. + 2019. +\newblock Emerging cross-lingual structure in pretrained language models. +\newblock \emph{ACL}. + +\bibitem[{Wu and Dredze(2019)}]{wu2019beto} +Shijie Wu and Mark Dredze. 2019. +\newblock Beto, bentz, becas: The surprising cross-lingual effectiveness of + bert. +\newblock \emph{EMNLP}. + +\bibitem[{Xie et~al.(2019)Xie, Dai, Hovy, Luong, and Le}]{xie2019unsupervised} +Qizhe Xie, Zihang Dai, Eduard Hovy, Minh-Thang Luong, and Quoc~V Le. 2019. +\newblock Unsupervised data augmentation for consistency training. +\newblock \emph{arXiv preprint arXiv:1904.12848}. + +\end{thebibliography} diff --git a/references/2019.arxiv.conneau/source/xlmr.synctex b/references/2019.arxiv.conneau/source/xlmr.synctex new file mode 100644 index 0000000000000000000000000000000000000000..e57dba8a4fb9924a435f9b3571768bc31358ad84 --- /dev/null +++ b/references/2019.arxiv.conneau/source/xlmr.synctex @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:420af1ab9f337834c49b93240fd9062be0a9f1bd9135878e6c96a6d128aa6856 +size 865236 diff --git a/references/2019.arxiv.conneau/source/xlmr.tex b/references/2019.arxiv.conneau/source/xlmr.tex new file mode 100644 index 0000000000000000000000000000000000000000..405b9d0bd794c141763acf6872fb4114066f3f9c --- /dev/null +++ b/references/2019.arxiv.conneau/source/xlmr.tex @@ -0,0 +1,307 @@ + +% +% File acl2020.tex +% +%% Based on the style files for ACL 2020, which were +%% Based on the style files for ACL 2018, NAACL 2018/19, which were +%% Based on the style files for ACL-2015, with some improvements +%% taken from the NAACL-2016 style +%% Based on the style files for ACL-2014, which were, in turn, +%% based on ACL-2013, ACL-2012, ACL-2011, ACL-2010, ACL-IJCNLP-2009, +%% EACL-2009, IJCNLP-2008... +%% Based on the style files for EACL 2006 by +%%e.agirre@ehu.es or Sergi.Balari@uab.es +%% and that of ACL 08 by Joakim Nivre and Noah Smith + +\documentclass[11pt,a4paper]{article} +\usepackage[hyperref]{acl2020} +\usepackage{times} +\usepackage{latexsym} +\renewcommand{\UrlFont}{\ttfamily\small} + +% This is not strictly necessary, and may be commented out, +% but it will improve the layout of the manuscript, +% and will typically save some space. +\usepackage{microtype} +\usepackage{graphicx} +\usepackage{subfigure} +\usepackage{booktabs} % for professional tables +\usepackage{url} +\usepackage{times} +\usepackage{latexsym} +\usepackage{array} +\usepackage{adjustbox} +\usepackage{multirow} +% \usepackage{subcaption} +\usepackage{hyperref} +\usepackage{longtable} + +\input{content/tables} + + +\aclfinalcopy % Uncomment this line for the final submission +\def\aclpaperid{479} % Enter the acl Paper ID here + +%\setlength\titlebox{5cm} +% You can expand the titlebox if you need extra space +% to show all the authors. Please do not make the titlebox +% smaller than 5cm (the original size); we will check this +% in the camera-ready version and ask you to change it back. + +\newcommand\BibTeX{B\textsc{ib}\TeX} +\usepackage{xspace} +\newcommand{\xlmr}{\textit{XLM-R}\xspace} +\newcommand{\mbert}{mBERT\xspace} +\newcommand{\XX}{\textcolor{red}{XX}\xspace} + +\newcommand{\note}[3]{{\color{#2}[#1: #3]}} +\newcommand{\ves}[1]{\note{ves}{red}{#1}} +\newcommand{\luke}[1]{\note{luke}{green}{#1}} +\newcommand{\myle}[1]{\note{myle}{cyan}{#1}} +\newcommand{\paco}[1]{\note{paco}{blue}{#1}} +\newcommand{\eg}[1]{\note{edouard}{orange}{#1}} +\newcommand{\kk}[1]{\note{kartikay}{pink}{#1}} + +\renewcommand{\UrlFont}{\scriptsize} +\title{Unsupervised Cross-lingual Representation Learning at Scale} + +\author{Alexis Conneau\thanks{\ \ Equal contribution.} \space\space\space + Kartikay Khandelwal\footnotemark[1] \space\space\space \AND + \bf Naman Goyal \space\space\space + Vishrav Chaudhary \space\space\space + Guillaume Wenzek \space\space\space + Francisco Guzm\'an \space\space\space \AND + \bf Edouard Grave \space\space\space + Myle Ott \space\space\space + Luke Zettlemoyer \space\space\space + Veselin Stoyanov \space\space\space \\ \\ \\ + \bf Facebook AI + } + +\date{} + +\begin{document} +\maketitle +\begin{abstract} +This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed \xlmr, significantly outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +14.6\% average accuracy on XNLI, +13\% average F1 score on MLQA, and +2.4\% F1 score on NER. \xlmr performs particularly well on low-resource languages, improving 15.7\% in XNLI accuracy for Swahili and 11.4\% for Urdu over previous XLM models. We also present a detailed empirical analysis of the key factors that are required to achieve these gains, including the trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing per-language performance; \xlmr is very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We will make our code, data and models publicly available.{\let\thefootnote\relax\footnotetext{\scriptsize Correspondence to {\tt \{aconneau,kartikayk\}@fb.com}}}\footnote{\url{https://github.com/facebookresearch/(fairseq-py,pytext,xlm)}} +\end{abstract} + + +\section{Introduction} + +The goal of this paper is to improve cross-lingual language understanding (XLU), by carefully studying the effects of training unsupervised cross-lingual representations at a very large scale. +We present \xlmr a transformer-based multilingual masked language model pre-trained on text in 100 languages, which obtains state-of-the-art performance on cross-lingual classification, sequence labeling and question answering. + +Multilingual masked language models (MLM) like \mbert ~\cite{devlin2018bert} and XLM \cite{lample2019cross} have pushed the state-of-the-art on cross-lingual understanding tasks by jointly pretraining large Transformer models~\cite{transformer17} on many languages. These models allow for effective cross-lingual transfer, as seen in a number of benchmarks including cross-lingual natural language inference ~\cite{bowman2015large,williams2017broad,conneau2018xnli}, question answering~\cite{rajpurkar-etal-2016-squad,lewis2019mlqa}, and named entity recognition~\cite{Pires2019HowMI,wu2019beto}. +However, all of these studies pre-train on Wikipedia, which provides a relatively limited scale especially for lower resource languages. + + +In this paper, we first present a comprehensive analysis of the trade-offs and limitations of multilingual language models at scale, inspired by recent monolingual scaling efforts~\cite{roberta2019}. +We measure the trade-off between high-resource and low-resource languages and the impact of language sampling and vocabulary size. +%By training models with an increasing number of languages, +The experiments expose a trade-off as we scale the number of languages for a fixed model capacity: more languages leads to better cross-lingual performance on low-resource languages up until a point, after which the overall performance on monolingual and cross-lingual benchmarks degrades. We refer to this tradeoff as the \emph{curse of multilinguality}, and show that it can be alleviated by simply increasing model capacity. +We argue, however, that this remains an important limitation for future XLU systems which may aim to improve performance with more modest computational budgets. + +Our best model XLM-RoBERTa (\xlmr) outperforms \mbert on cross-lingual classification by up to 23\% accuracy on low-resource languages. +%like Swahili and Urdu. +It outperforms the previous state of the art by 5.1\% average accuracy on XNLI, 2.42\% average F1-score on Named Entity Recognition, and 9.1\% average F1-score on cross-lingual Question Answering. We also evaluate monolingual fine tuning on the GLUE and XNLI benchmarks, where \xlmr obtains results competitive with state-of-the-art monolingual models, including RoBERTa \cite{roberta2019}. +These results demonstrate, for the first time, that it is possible to have a single large model for all languages, without sacrificing per-language performance. +We will make our code, models and data publicly available, with the hope that this will help research in multilingual NLP and low-resource language understanding. + +\section{Related Work} +From pretrained word embeddings~\citep{mikolov2013distributed, pennington2014glove} to pretrained contextualized representations~\citep{peters2018deep,schuster2019cross} and transformer based language models~\citep{radford2018improving,devlin2018bert}, unsupervised representation learning has significantly improved the state of the art in natural language understanding. Parallel work on cross-lingual understanding~\citep{mikolov2013exploiting,schuster2019cross,lample2019cross} extends these systems to more languages and to the cross-lingual setting in which a model is learned in one language and applied in other languages. + +Most recently, \citet{devlin2018bert} and \citet{lample2019cross} introduced \mbert and XLM - masked language models trained on multiple languages, without any cross-lingual supervision. +\citet{lample2019cross} propose translation language modeling (TLM) as a way to leverage parallel data and obtain a new state of the art on the cross-lingual natural language inference (XNLI) benchmark~\cite{conneau2018xnli}. +They further show strong improvements on unsupervised machine translation and pretraining for sequence generation. \citet{wu2019emerging} shows that monolingual BERT representations are similar across languages, explaining in part the natural emergence of multilinguality in bottleneck architectures. Separately, \citet{Pires2019HowMI} demonstrated the effectiveness of multilingual models like \mbert on sequence labeling tasks. \citet{huang2019unicoder} showed gains over XLM using cross-lingual multi-task learning, and \citet{singh2019xlda} demonstrated the efficiency of cross-lingual data augmentation for cross-lingual NLI. However, all of this work was at a relatively modest scale, in terms of the amount of training data, as compared to our approach. + +\insertWikivsCC + +The benefits of scaling language model pretraining by increasing the size of the model as well as the training data has been extensively studied in the literature. For the monolingual case, \citet{jozefowicz2016exploring} show how large-scale LSTM models can obtain much stronger performance on language modeling benchmarks when trained on billions of tokens. +%[Kartikay: TODO; CHange the reference to GPT2] +GPT~\cite{radford2018improving} also highlights the importance of scaling the amount of data and RoBERTa \cite{roberta2019} shows that training BERT longer on more data leads to significant boost in performance. Inspired by RoBERTa, we show that mBERT and XLM are undertuned, and that simple improvements in the learning procedure of unsupervised MLM leads to much better performance. We train on cleaned CommonCrawls~\cite{wenzek2019ccnet}, which increase the amount of data for low-resource languages by two orders of magnitude on average. Similar data has also been shown to be effective for learning high quality word embeddings in multiple languages~\cite{grave2018learning}. + + +Several efforts have trained massively multilingual machine translation models from large parallel corpora. They uncover the high and low resource trade-off and the problem of capacity dilution~\citep{johnson2017google,tan2019multilingual}. The work most similar to ours is \citet{arivazhagan2019massively}, which trains a single model in 103 languages on over 25 billion parallel sentences. +\citet{siddhant2019evaluating} further analyze the representations obtained by the encoder of a massively multilingual machine translation system and show that it obtains similar results to mBERT on cross-lingual NLI. +%, which performs much wors that the XLM models we study. +Our work, in contrast, focuses on the unsupervised learning of cross-lingual representations and their transfer to discriminative tasks. + + +\section{Model and Data} +\label{sec:model+data} + +In this section, we present the training objective, languages, and data we use. We follow the XLM approach~\cite{lample2019cross} as closely as possible, only introducing changes that improve performance at scale. + +\paragraph{Masked Language Models.} +We use a Transformer model~\cite{transformer17} trained with the multilingual MLM objective~\cite{devlin2018bert,lample2019cross} using only monolingual data. We sample streams of text from each language and train the model to predict the masked tokens in the input. +We apply subword tokenization directly on raw text data using Sentence Piece~\cite{kudo2018sentencepiece} with a unigram language model~\cite{kudo2018subword}. We sample batches from different languages using the same sampling distribution as \citet{lample2019cross}, but with $\alpha=0.3$. Unlike \citet{lample2019cross}, we do not use language embeddings, which allows our model to better deal with code-switching. We use a large vocabulary size of 250K with a full softmax and train two different models: \xlmr\textsubscript{Base} (L = 12, H = 768, A = 12, 270M params) and \xlmr (L = 24, H = 1024, A = 16, 550M params). For all of our ablation studies, we use a BERT\textsubscript{Base} architecture with a vocabulary of 150K tokens. Appendix~\ref{sec:appendix_B} goes into more details about the architecture of the different models referenced in this paper. + +\paragraph{Scaling to a hundred languages.} +\xlmr is trained on 100 languages; +we provide a full list of languages and associated statistics in Appendix~\ref{sec:appendix_A}. Figure~\ref{fig:wikivscc} specifies the iso codes of 88 languages that are shared across \xlmr and XLM-100, the model from~\citet{lample2019cross} trained on Wikipedia text in 100 languages. + +Compared to previous work, we replace some languages with more commonly used ones such as romanized Hindi and traditional Chinese. In our ablation studies, we always include the 7 languages for which we have classification and sequence labeling evaluation benchmarks: English, French, German, Russian, Chinese, Swahili and Urdu. We chose this set as it covers a suitable range of language families and includes low-resource languages such as Swahili and Urdu. +We also consider larger sets of 15, 30, 60 and all 100 languages. When reporting results on high-resource and low-resource, we refer to the average of English and French results, and the average of Swahili and Urdu results respectively. + +\paragraph{Scaling the Amount of Training Data.} +Following~\citet{wenzek2019ccnet}~\footnote{\url{https://github.com/facebookresearch/cc_net}}, we build a clean CommonCrawl Corpus in 100 languages. We use an internal language identification model in combination with the one from fastText~\cite{joulin2017bag}. We train language models in each language and use it to filter documents as described in \citet{wenzek2019ccnet}. We consider one CommonCrawl dump for English and twelve dumps for all other languages, which significantly increases dataset sizes, especially for low-resource languages like Burmese and Swahili. + +Figure~\ref{fig:wikivscc} shows the difference in size between the Wikipedia Corpus used by mBERT and XLM-100, and the CommonCrawl Corpus we use. As we show in Section~\ref{sec:multimono}, monolingual Wikipedia corpora are too small to enable unsupervised representation learning. Based on our experiments, we found that a few hundred MiB of text data is usually a minimal size for learning a BERT model. + +\section{Evaluation} +We consider four evaluation benchmarks. +For cross-lingual understanding, we use cross-lingual natural language inference, named entity recognition, and question answering. We use the GLUE benchmark to evaluate the English performance of \xlmr and compare it to other state-of-the-art models. + +\paragraph{Cross-lingual Natural Language Inference (XNLI).} +The XNLI dataset comes with ground-truth dev and test sets in 15 languages, and a ground-truth English training set. The training set has been machine-translated to the remaining 14 languages, providing synthetic training data for these languages as well. We evaluate our model on cross-lingual transfer from English to other languages. We also consider three machine translation baselines: (i) \textit{translate-test}: dev and test sets are machine-translated to English and a single English model is used (ii) \textit{translate-train} (per-language): the English training set is machine-translated to each language and we fine-tune a multiligual model on each training set (iii) \textit{translate-train-all} (multi-language): we fine-tune a multilingual model on the concatenation of all training sets from translate-train. For the translations, we use the official data provided by the XNLI project. +% In case we want to add more details about the CC-100 corpora : We train language models in each language and use it to filter documents as described in Wenzek et al. (2019). We additionally apply a filter based on type-token ratio score of 0.6. We consider one CommonCrawl snapshot (December, 2018) for English and twelve snapshots from all months of 2018 for all other languages, which significantly increases dataset sizes, especially for low-resource languages like Burmese and Swahili. + +\paragraph{Named Entity Recognition.} +% WikiAnn http://nlp.cs.rpi.edu/wikiann/ +For NER, we consider the CoNLL-2002~\cite{sang2002introduction} and CoNLL-2003~\cite{tjong2003introduction} datasets in English, Dutch, Spanish and German. We fine-tune multilingual models either (1) on the English set to evaluate cross-lingual transfer, (2) on each set to evaluate per-language performance, or (3) on all sets to evaluate multilingual learning. We report the F1 score, and compare to baselines from \citet{lample-etal-2016-neural} and \citet{akbik2018coling}. + +\paragraph{Cross-lingual Question Answering.} +We use the MLQA benchmark from \citet{lewis2019mlqa}, which extends the English SQuAD benchmark to Spanish, German, Arabic, Hindi, Vietnamese and Chinese. We report the F1 score as well as the exact match (EM) score for cross-lingual transfer from English. + +\paragraph{GLUE Benchmark.} +Finally, we evaluate the English performance of our model on the GLUE benchmark~\cite{wang2018glue} which gathers multiple classification tasks, such as MNLI~\cite{williams2017broad}, SST-2~\cite{socher2013recursive}, or QNLI~\cite{rajpurkar2018know}. We use BERT\textsubscript{Large} and RoBERTa as baselines. + +\section{Analysis and Results} +\label{sec:analysis} + +In this section, we perform a comprehensive analysis of multilingual masked language models. We conduct most of the analysis on XNLI, which we found to be representative of our findings on other tasks. We then present the results of \xlmr on cross-lingual understanding and GLUE. Finally, we compare multilingual and monolingual models, and present results on low-resource languages. + +\subsection{Improving and Understanding Multilingual Masked Language Models} +% prior analysis necessary to build \xlmr +\insertAblationone +\insertAblationtwo + +Much of the work done on understanding the cross-lingual effectiveness of \mbert or XLM~\cite{Pires2019HowMI,wu2019beto,lewis2019mlqa} has focused on analyzing the performance of fixed pretrained models on downstream tasks. In this section, we present a comprehensive study of different factors that are important to \textit{pretraining} large scale multilingual models. We highlight the trade-offs and limitations of these models as we scale to one hundred languages. + +\paragraph{Transfer-dilution Trade-off and Curse of Multilinguality.} +Model capacity (i.e. the number of parameters in the model) is constrained due to practical considerations such as memory and speed during training and inference. For a fixed sized model, the per-language capacity decreases as we increase the number of languages. While low-resource language performance can be improved by adding similar higher-resource languages during pretraining, the overall downstream performance suffers from this capacity dilution~\cite{arivazhagan2019massively}. Positive transfer and capacity dilution have to be traded off against each other. + +We illustrate this trade-off in Figure~\ref{fig:transfer_dilution}, which shows XNLI performance vs the number of languages the model is pretrained on. Initially, as we go from 7 to 15 languages, the model is able to take advantage of positive transfer which improves performance, especially on low resource languages. Beyond this point the {\em curse of multilinguality} +kicks in and degrades performance across all languages. Specifically, the overall XNLI accuracy decreases from 71.8\% to 67.7\% as we go from XLM-7 to XLM-100. The same trend can be observed for models trained on the larger CommonCrawl Corpus. + +The issue is even more prominent when the capacity of the model is small. To show this, we pretrain models on Wikipedia Data in 7, 30 and 100 languages. As we add more languages, we make the Transformer wider by increasing the hidden size from 768 to 960 to 1152. In Figure~\ref{fig:capacity}, we show that the added capacity allows XLM-30 to be on par with XLM-7, thus overcoming the curse of multilinguality. The added capacity for XLM-100, however, is not enough +and it still lags behind due to higher vocabulary dilution (recall from Section~\ref{sec:model+data} that we used a fixed vocabulary size of 150K for all models). + +\paragraph{High-resource vs Low-resource Trade-off.} +The allocation of the model capacity across languages is controlled by several parameters: the training set size, the size of the shared subword vocabulary, and the rate at which we sample training examples from each language. We study the effect of sampling on the performance of high-resource (English and French) and low-resource (Swahili and Urdu) languages for an XLM-100 model trained on Wikipedia (we observe a similar trend for the construction of the subword vocab). Specifically, we investigate the impact of varying the $\alpha$ parameter which controls the exponential smoothing of the language sampling rate. Similar to~\citet{lample2019cross}, we use a sampling rate proportional to the number of sentences in each corpus. Models trained with higher values of $\alpha$ see batches of high-resource languages more often. +Figure~\ref{fig:alpha} shows that the higher the value of $\alpha$, the better the performance on high-resource languages, and vice-versa. When considering overall performance, we found $0.3$ to be an optimal value for $\alpha$, and use this for \xlmr. + +\paragraph{Importance of Capacity and Vocabulary.} +In previous sections and in Figure~\ref{fig:capacity}, we showed the importance of scaling the model size as we increase the number of languages. Similar to the overall model size, we argue that scaling the size of the shared vocabulary (the vocabulary capacity) can improve the performance of multilingual models on downstream tasks. To illustrate this effect, we train XLM-100 models on Wikipedia data with different vocabulary sizes. We keep the overall number of parameters constant by adjusting the width of the transformer. Figure~\ref{fig:vocab} shows that even with a fixed capacity, we observe a 2.8\% increase in XNLI average accuracy as we increase the vocabulary size from 32K to 256K. This suggests that multilingual models can benefit from allocating a higher proportion of the total number of parameters to the embedding layer even though this reduces the size of the Transformer. +%With bigger models, we believe that using a vocabulary of up to 2 million tokens with an adaptive softmax~\cite{grave2017efficient,baevski2018adaptive} should improve performance even further, but we leave this exploration to future work. +For simplicity and given the softmax computational constraints, we use a vocabulary of 250k for \xlmr. + +We further illustrate the importance of this parameter, by training three models with the same transformer architecture (BERT\textsubscript{Base}) but with different vocabulary sizes: 128K, 256K and 512K. We observe more than 3\% gains in overall accuracy on XNLI by simply increasing the vocab size from 128k to 512k. + +\paragraph{Larger-scale Datasets and Training.} +As shown in Figure~\ref{fig:wikivscc}, the CommonCrawl Corpus that we collected has significantly more monolingual data than the previously used Wikipedia corpora. Figure~\ref{fig:curse} shows that for the same BERT\textsubscript{Base} architecture, all models trained on CommonCrawl obtain significantly better performance. + +Apart from scaling the training data, \citet{roberta2019} also showed the benefits of training MLMs longer. In our experiments, we observed similar effects of large-scale training, such as increasing batch size (see Figure~\ref{fig:batch}) and training time, on model performance. Specifically, we found that using validation perplexity as a stopping criterion for pretraining caused the multilingual MLM in \citet{lample2019cross} to be under-tuned. In our experience, performance on downstream tasks continues to improve even after validation perplexity has plateaued. Combining this observation with our implementation of the unsupervised XLM-MLM objective, we were able to improve the performance of \citet{lample2019cross} from 71.3\% to more than 75\% average accuracy on XNLI, which was on par with their supervised translation language modeling (TLM) objective. Based on these results, and given our focus on unsupervised learning, we decided to not use the supervised TLM objective for training our models. + + +\paragraph{Simplifying Multilingual Tokenization with Sentence Piece.} +The different language-specific tokenization tools +used by mBERT and XLM-100 make these models more difficult to use on raw text. Instead, we train a Sentence Piece model (SPM) and apply it directly on raw text data for all languages. We did not observe any loss in performance for models trained with SPM when compared to models trained with language-specific preprocessing and byte-pair encoding (see Figure~\ref{fig:batch}) and hence use SPM for \xlmr. + +\subsection{Cross-lingual Understanding Results} +Based on these results, we adapt the setting of \citet{lample2019cross} and use a large Transformer model with 24 layers and 1024 hidden states, with a 250k vocabulary. We use the multilingual MLM loss and train our \xlmr model for 1.5 Million updates on five-hundred 32GB Nvidia V100 GPUs with a batch size of 8192. We leverage the SPM-preprocessed text data from CommonCrawl in 100 languages and sample languages with $\alpha=0.3$. In this section, we show that it outperforms all previous techniques on cross-lingual benchmarks while getting performance on par with RoBERTa on the GLUE benchmark. + + +\insertXNLItable + +\paragraph{XNLI.} +Table~\ref{tab:xnli} shows XNLI results and adds some additional details: (i) the number of models the approach induces (\#M), (ii) the data on which the model was trained (D), and (iii) the number of languages the model was pretrained on (\#lg). As we show in our results, these parameters significantly impact performance. Column \#M specifies whether model selection was done separately on the dev set of each language ($N$ models), or on the joint dev set of all the languages (single model). We observe a 0.6 decrease in overall accuracy when we go from $N$ models to a single model - going from 71.3 to 70.7. We encourage the community to adopt this setting. For cross-lingual transfer, while this approach is not fully zero-shot transfer, we argue that in real applications, a small amount of supervised data is often available for validation in each language. + +\xlmr sets a new state of the art on XNLI. +On cross-lingual transfer, \xlmr obtains 80.9\% accuracy, outperforming the XLM-100 and \mbert open-source models by 10.2\% and 14.6\% average accuracy. On the Swahili and Urdu low-resource languages, \xlmr outperforms XLM-100 by 15.7\% and 11.4\%, and \mbert by 23.5\% and 15.8\%. While \xlmr handles 100 languages, we also show that it outperforms the former state of the art Unicoder~\citep{huang2019unicoder} and XLM (MLM+TLM), which handle only 15 languages, by 5.5\% and 5.8\% average accuracy respectively. Using the multilingual training of translate-train-all, \xlmr further improves performance and reaches 83.6\% accuracy, a new overall state of the art for XNLI, outperforming Unicoder by 5.1\%. Multilingual training is similar to practical applications where training sets are available in various languages for the same task. In the case of XNLI, datasets have been translated, and translate-train-all can be seen as some form of cross-lingual data augmentation~\cite{singh2019xlda}, similar to back-translation~\cite{xie2019unsupervised}. + +\insertNER +\paragraph{Named Entity Recognition.} +In Table~\ref{tab:ner}, we report results of \xlmr and \mbert on CoNLL-2002 and CoNLL-2003. We consider the LSTM + CRF approach from \citet{lample-etal-2016-neural} and the Flair model from \citet{akbik2018coling} as baselines. We evaluate the performance of the model on each of the target languages in three different settings: (i) train on English data only (en) (ii) train on data in target language (each) (iii) train on data in all languages (all). Results of \mbert are reported from \citet{wu2019beto}. Note that we do not use a linear-chain CRF on top of \xlmr and \mbert representations, which gives an advantage to \citet{akbik2018coling}. Without the CRF, our \xlmr model still performs on par with the state of the art, outperforming \citet{akbik2018coling} on Dutch by $2.09$ points. On this task, \xlmr also outperforms \mbert by 2.42 F1 on average for cross-lingual transfer, and 1.86 F1 when trained on each language. Training on all languages leads to an average F1 score of 89.43\%, outperforming cross-lingual transfer approach by 8.49\%. + +\paragraph{Question Answering.} +We also obtain new state of the art results on the MLQA cross-lingual question answering benchmark, introduced by \citet{lewis2019mlqa}. We follow their procedure by training on the English training data and evaluating on the 7 languages of the dataset. +We report results in Table~\ref{tab:mlqa}. +\xlmr obtains F1 and accuracy scores of 70.7\% and 52.7\% while the previous state of the art was 61.6\% and 43.5\%. \xlmr also outperforms \mbert by 13.0\% F1-score and 11.1\% accuracy. It even outperforms BERT-Large on English, confirming its strong monolingual performance. + +\insertMLQA + +\subsection{Multilingual versus Monolingual} +\label{sec:multimono} +In this section, we present results of multilingual XLM models against monolingual BERT models. + +\paragraph{GLUE: \xlmr versus RoBERTa.} +Our goal is to obtain a multilingual model with strong performance on both, cross-lingual understanding tasks as well as natural language understanding tasks for each language. To that end, we evaluate \xlmr on the GLUE benchmark. We show in Table~\ref{tab:glue}, that \xlmr obtains better average dev performance than BERT\textsubscript{Large} by 1.6\% and reaches performance on par with XLNet\textsubscript{Large}. The RoBERTa model outperforms \xlmr by only 1.0\% on average. We believe future work can reduce this gap even further by alleviating the curse of multilinguality and vocabulary dilution. These results demonstrate the possibility of learning one model for many languages while maintaining strong performance on per-language downstream tasks. + +\insertGlue + +\paragraph{XNLI: XLM versus BERT.} +A recurrent criticism against multilingual models is that they obtain worse performance than their monolingual counterparts. In addition to the comparison of \xlmr and RoBERTa, we provide the first comprehensive study to assess this claim on the XNLI benchmark. We extend our comparison between multilingual XLM models and monolingual BERT models on 7 languages and compare performance in Table~\ref{tab:multimono}. We train 14 monolingual BERT models on Wikipedia and CommonCrawl (capped at 60 GiB), +%\footnote{For simplicity, we use a reduced version of our corpus by capping the size of each monolingual dataset to 60 GiB.} +and two XLM-7 models. We increase the vocabulary size of the multilingual model for a better comparison. +% To our surprise - and backed by further study on internal benchmarks - +We found that \textit{multilingual models can outperform their monolingual BERT counterparts}. Specifically, in Table~\ref{tab:multimono}, we show that for cross-lingual transfer, monolingual baselines outperform XLM-7 for both Wikipedia and CC by 1.6\% and 1.3\% average accuracy. However, by making use of multilingual training (translate-train-all) and leveraging training sets coming from multiple languages, XLM-7 can outperform the BERT models: our XLM-7 trained on CC obtains 80.0\% average accuracy on the 7 languages, while the average performance of BERT models trained on CC is 77.5\%. This is a surprising result that shows that the capacity of multilingual models to leverage training data coming from multiple languages for a particular task can overcome the capacity dilution problem to obtain better overall performance. + + +\insertMultiMono + +\subsection{Representation Learning for Low-resource Languages} +We observed in Table~\ref{tab:multimono} that pretraining on Wikipedia for Swahili and Urdu performed similarly to a randomly initialized model; most likely due to the small size of the data for these languages. On the other hand, pretraining on CC improved performance by up to 10 points. This confirms our assumption that mBERT and XLM-100 rely heavily on cross-lingual transfer but do not model the low-resource languages as well as \xlmr. Specifically, in the translate-train-all setting, we observe that the biggest gains for XLM models trained on CC, compared to their Wikipedia counterparts, are on low-resource languages; 7\% and 4.8\% improvement on Swahili and Urdu respectively. + +\section{Conclusion} +In this work, we introduced \xlmr, our new state of the art multilingual masked language model trained on 2.5 TB of newly created clean CommonCrawl data in 100 languages. We show that it provides strong gains over previous multilingual models like \mbert and XLM on classification, sequence labeling and question answering. We exposed the limitations of multilingual MLMs, in particular by uncovering the high-resource versus low-resource trade-off, the curse of multilinguality and the importance of key hyperparameters. We also expose the surprising effectiveness of multilingual models over monolingual models, and show strong improvements on low-resource languages. +% \section*{Acknowledgements} + + +\bibliography{acl2020} +\bibliographystyle{acl_natbib} + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% DELETE THIS PART. DO NOT PLACE CONTENT AFTER THE REFERENCES! +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + \newpage + \clearpage + \appendix + \onecolumn + \section*{Appendix} + \section{Languages and statistics for CC-100 used by \xlmr} + In this section we present the list of languages in the CC-100 corpus we created for training \xlmr. We also report statistics such as the number of tokens and the size of each monolingual corpus. + \label{sec:appendix_A} + \insertDataStatistics + +% \newpage + \section{Model Architectures and Sizes} + As we showed in section~\ref{sec:analysis}, capacity is an important parameter for learning strong cross-lingual representations. In the table below, we list multiple monolingual and multilingual models used by the research community and summarize their architectures and total number of parameters. + \label{sec:appendix_B} + +\insertParameters + +% \section{Do \emph{not} have an appendix here} + +% \textbf{\emph{Do not put content after the references.}} +% % +% Put anything that you might normally include after the references in a separate +% supplementary file. + +% We recommend that you build supplementary material in a separate document. +% If you must create one PDF and cut it up, please be careful to use a tool that +% doesn't alter the margins, and that doesn't aggressively rewrite the PDF file. +% pdftk usually works fine. + +% \textbf{Please do not use Apple's preview to cut off supplementary material.} In +% previous years it has altered margins, and created headaches at the camera-ready +% stage. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + + +\end{document} \ No newline at end of file diff --git a/references/2019.arxiv.ho/paper.md b/references/2019.arxiv.ho/paper.md new file mode 100644 index 0000000000000000000000000000000000000000..5be48529fe747c9e6751f1bb00b1a4b4e3fec89c --- /dev/null +++ b/references/2019.arxiv.ho/paper.md @@ -0,0 +1,220 @@ +--- +title: "Emotion Recognition for Vietnamese Social Media Text" +authors: + - "Vong Anh Ho" + - "Duong Huynh-Cong Nguyen" + - "Danh Hoang Nguyen" + - "Linh Thi-Van Pham" + - "Duc-Vu Nguyen" + - "Kiet Van Nguyen" + - "Ngan Luu-Thuy Nguyen" +year: 2019 +venue: "arXiv" +url: "https://arxiv.org/abs/1911.09339" +arxiv: "1911.09339" +--- + +\title{Emotion Recognition\\for Vietnamese Social Media Text} + +\titlerunning{Emotion Recognition for Vietnamese Social Media Text} + +\author{Vong Anh Ho\inst{1,4}\textsuperscript{(\Letter)} \and +Duong Huynh-Cong Nguyen\inst{1,4} \and +Danh Hoang Nguyen\inst{1,4} \and +\\Linh Thi-Van Pham\inst{2,4} \and +Duc-Vu Nguyen\inst{3,4} \and +\\Kiet Van Nguyen\inst{1,4} \and +Ngan Luu-Thuy Nguyen\inst{1,4}} + +\authorrunning{Vong Anh Ho et al.} + +\institute{University of Information Technology, VNU-HCM, Vietnam\\ +\email{\{15521025,15520148,15520090\}@gm.uit.edu.vn, \{kietnv,ngannlt\}@uit.edu.vn}\\ +\and +University of Social Sciences and Humanities, VNU-HCM, Vietnam\\ +\email{vanlinhpham888@gmail.com}\\ +\and +Multimedia Communications Laboratory, University of Information Technology, VNU-HCM, Vietnam\\ +\email{vund@uit.edu.vn}\\ +\and +Vietnam National University, Ho Chi Minh City, Vietnam} +\maketitle + +\begin{abstract} + +Emotion recognition or emotion prediction is a higher approach or a special case of sentiment analysis. In this task, the result is not produced in terms of either polarity: positive or negative or in the form of rating (from 1 to 5) but of a more detailed level of analysis in which the results are depicted in more expressions like sadness, enjoyment, anger, disgust, fear and surprise. Emotion recognition plays a critical role in measuring brand value of a product by recognizing specific emotions of customers' comments. In this study, we have achieved two targets. First and foremost, we built a standard **V**ietnamese **S**ocial **M**edia **E**motion **C**orpus (UIT-VSMEC) with exactly 6,927 emotion-annotated sentences, contributing to emotion recognition research in Vietnamese which is a low-resource language in natural language processing (NLP). Secondly, we assessed and measured machine learning and deep neural network models on our UIT-VSMEC corpus. As a result, the CNN model achieved the highest performance with the weighted F1-score of 59.74\ + +\keywords{Emotion Recognition \and Emotion Prediction \and Vietnamese \and Machine Learning \and Deep Learning \and CNN \and LSTM \and SVM.} +\end{abstract} + +\input{sections/1-introduction.tex} +\input{sections/2-relatedwork.tex} +\input{sections/3-corpus.tex} +\input{sections/4-method.tex} +\input{sections/5-experiments.tex} +\input{sections/6-conclusion.tex} + +# Acknowledgment +We would like to give our thanks to the NLP@UIT research group and the Citynow-UIT Laboratory of the University of Information Technology - Vietnam National University Ho Chi Minh City for their supports with pragmatic and inspiring advice. + +\bibliographystyle{splncs04} + +\begin{thebibliography}{10} +\providecommand{\url}[1]{`#1`} +\providecommand{\urlprefix}{URL } +\providecommand{\doi}[1]{https://doi.org/#1} + +\bibitem{PlabanKumarBhowmick} +{Bhowmick}, P.K., {Basu}, A., {Mitra}, P.: {An Agreement Measure for + Determining Inter-Annotator Reliability of Human Judgements on Affective + Tex}. In: {Proceedings of the workshop on Human Judgements in Computational + Linguistics}. pp. 58--65. COLING 2008, Manchester, United Kingdom (2008) + +\bibitem{Jointstockcompany} +company, J.S.: {The habit of using social networks of Vietnamese people 2018}. + brands vietnam, Ho Chi Minh City, Vietnam (2018) + +\bibitem{Ekman1993} +{Ekman}, P.: In: {Facial expression and emotion}. vol.~48, pp. 384--392. + {American Psychologist} (1993) + +\bibitem{Ekman} +{Ekman}, P.: In: {Emotions revealed: Recognizing faces and feelings to improve + communication and emotional life}. p.~2007. {Macmillan} (2012) + +\bibitem{PaulEkman} +{Ekman}, P., {Ekman}, E., {Lama}, D.: In: {The Ekmans' Atlas of Emotion} (2018) + +\bibitem{Kim} +{Kim}, Y.: {Convolutional Neural Networks for Sentence Classifications}. In: + {Proceedings of the 2014 Conference on Empirical Methods in Natural Language + Processing (EMNLP)}. pp. 1746--1751. { Association for Computational + Linguistics}, Doha, Qatar (2014) + +\bibitem{Kiritchenko} +{Kiritchenko}, S., {Mohammad}, S.: {Using Hashtags to Capture Fine Emotion + Categories from Tweets}. In: {Computational Intelligence}. pp. 301--326 + (2015) + +\bibitem{RomanKlinger} +{Klinger}, R., {Clerc}, O.D., {Mohammad}, S.M., {Balahur}, A.: {IEST:WASSA-2018 + Implicit Emotions Shared Task}. pp. 31--42. 2017 AFNLP, Brussels, Belgium + (2018) + +\bibitem{BernhardKratzwald} +{Kratzwald}, B., {Ilic}, S., {Kraus}, M., S.~{Feuerriegel}, H.P.: {Decision + support with text-based emotion recognition: Deep learning for affective + computing}. pp. 24 -- 35. {Decision Support Systems} (2018) + +\bibitem{SaifMohammad2017} +{Mohammad}, S., {Bravo-Marquez}, F.: {Emotion Intensities in Tweets}. 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Information + Processing and Management: an International Journal (2015) + +\bibitem{Nguyen} +Nguyen: {Vietnam has the 7th largest number of Facebook users in the world}. + Dan Tri newspaper (2018) + +\bibitem{VLSPX} +{Nguyen}, H.T.M., {Nguyen}, H.V., {Ngo}, Q.T., {Vu}, L.X., {Tran}, V.M., {Ngo}, + B.X., {Le}, C.A.: {VLSP Shared Task: Sentiment Analysis}. In: {Journal of + Computer Science and Cybernetics}. pp. 295--310 (2018) + + +\bibitem{KietVanNguyen} +{Nguyen}, K.V., {Nguyen}, V.D., {Nguyen}, P., {Truong}, T., {Nguyen}, N.L.T.: + {UIT-VSFC: Vietnamese Students’ Feedback Corpus for Sentiment Analysis}. + In: {2018 10th International Conference on Knowledge and Systems Engineering + (KSE)}. pp. 19--24. {IEEE}, Ho Chi Minh City, Vietnam (2018) + +\bibitem{PhuNguyen} +{Nguyen}, P.X.V., {Truong}, T.T.H., {Nguyen}, K.V., {Nguyen}, N.L.T.: {Deep + Learning versus Traditional Classifiers on Vietnamese Students' Feedback + Corpus}. In: {2018 5th NAFOSTED Conference on Information and Computer + Science (NICS)}. pp. 75--80. Ho Chi Minh City, Vietnam (2018) + +\bibitem{VuDucNguyen} +{Nguyen}, V.D., {Nguyen}, K.V., {Nguyen}, N.L.T.: {Variants of Long Short-Term + Memory for Sentiment Analysis on Vietnamese Students’ Feedback Corpus}. In: + {2018 10th International Conference on Knowledge and Systems Engineering + (KSE)}. pp. 306--311. IEEE, Ho Chi Minh City, Vietnam (2018) + +\bibitem{AurelienGeron} +Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, + O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., + Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: + Scikit-learn: Machine learning in python. Journal of Machine Learning + Research **12**, 2825--2830 (2011) + +\bibitem{CarloStrapparava} +{Strapparava}, C., {Mihalcea}, R.: {SemEval-2007 Task 14: Affective Text}. In: + {Proceedings of the 4th International Workshop on Semantic Evaluations + (SemEval-2007)}. pp. 70--74. { Association for Computational Linguistics}, + Prague (2007) + +\bibitem{TingweiWang} +{T. {Wang} and X. {Yang} and C. {Ouyang}}: {A Multi-emotion Classification + Method Based on BLSTM-MC in Code-Switching Text: 7th CCF International + Conference, NLPCC 2018, Hohhot, China, August 26–30, 2018, Proceedings, + Part II.} pp. 190--199. Natural Language Processing and Chinese Computing + (2018) + +\bibitem{ZhongqingWang} +{Wang}, Z., {Li}, S.: {Overview of NLPCC 2018 Shared Task 1: Emotion Detection + in Code-Switching Text: 7th CCF International Conference, NLPCC 2018, Hohhot, + China, August 26–30, 2018, Proceedings, Part II}. pp. 429--433. Natural + Language Processing and Chinese Computing (2018) + +\bibitem{Facial2007} +{Zhang}, S., {Wu}, Z., {Meng}, H., {Cai}, L.: Facial expression synthesis using + pad emotional parameters for a chinese expressive avatar. vol.~4738, pp. + 24--35 (09 2007) + +\bibitem{YingjieZhang} +{Zhang}, Y., {Wallace}, B.C.: {A Sensitivity Analysis of (and Practitioners’ + Guide to Convolutional}. pp. 253--263. 2017 AFNLP, Taipei, Taiwan (2017) + +\bibitem +{Nguyen}, K.V., {Nguyen}, N.L.T., 2016, October. {Vietnamese transition-based dependency parsing with supertag features}. In 2016 Eighth International Conference on Knowledge and Systems Engineering (KSE) (pp. 175-180). IEEE. + +\bibitem{nguyen2014treebank} +Nguyen, D.Q., Pham, S.B., Nguyen, P.T., Le Nguyen, M., et al.: From treebankconversion to automatic dependency parsing for vietnamese. In: International Con-ference on Applications of Natural Language to Data Bases/Information Systems.pp. 196–207. Springer (2014) + +\bibitem{nguyen2016vietnamese} +Nguyen, K.V., Nguyen, N.L.T.: Vietnamese transition-based dependency parsingwith supertag features. In: 2016 Eighth International Conference on Knowledgeand Systems Engineering (KSE). pp. 175–180. IEEE (2016) + +\bibitem{bach2018empirical} +Bach, N.X., Linh, N.D., Phuong, T.M.: An empirical study on pos tagging forvietnamese social media text. Computer Speech \& Language50, 1–15 (2018) + +\bibitem{nguyen2017word} +Nguyen, D.Q., Vu, T., Nguyen, D.Q., Dras, M., Johnson, M.: From word segmen-tation to pos tagging for vietnamese. arXiv preprint arXiv:1711.04951 (2017) + +\bibitem{thao2007named} +Thao, P.T.X., Tri, T.Q., Dien, D., Collier, N.: Named entity recognition in viet-namese using classifier voting. ACM Transactions on Asian Language InformationProcessing (TALIP)6(4), 1–18 (2007) + +\bibitem{nguyen2016approach} +Nguyen, L.H., Dinh, D., Tran, P.: An approach to construct a named entity anno-tated english-vietnamese bilingual corpus. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP)16(2), 1–17 (2016) + +\bibitem{Nguyen_2009} +Nguyen, D.Q., Nguyen, D.Q., Pham, S.B.: A vietnamese question answeringsystem. 2009 International Conference on Knowledge and Systems Engineering(Oct 2009). https://doi.org/10.1109/kse.2009.42,http://dx.doi.org/10.1109/KSE.2009.42 + +\bibitem{le2018factoid} +Le-Hong, P., Bui, D.T.: A factoid question answering system for vietnamese. In:Companion Proceedings of the The Web Conference 2018. pp. 1049–1055 (2018) + +\end{thebibliography} \ No newline at end of file diff --git a/references/2019.arxiv.ho/paper.pdf b/references/2019.arxiv.ho/paper.pdf new file mode 100644 index 0000000000000000000000000000000000000000..a6071daacadc228f8b6f25d8c349eddf5d38a8c9 --- /dev/null +++ b/references/2019.arxiv.ho/paper.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:583f61e2334e8547aba92a311850d5fb7b6dbac7301b0d9af9186c3ffb7aed60 +size 365205 diff --git a/references/2019.arxiv.ho/paper.tex b/references/2019.arxiv.ho/paper.tex new file mode 100644 index 0000000000000000000000000000000000000000..f393fe627e3a9af70c45cb094351f81eb9488b7b --- /dev/null +++ b/references/2019.arxiv.ho/paper.tex @@ -0,0 +1,239 @@ +\documentclass[runningheads]{llncs} +\usepackage{graphicx} +\usepackage{marvosym} +\usepackage{amsmath,amssymb,amsfonts} +\usepackage{algorithmic} +\usepackage{graphicx} +\usepackage{textcomp} +\usepackage[T5]{fontenc} +\usepackage[utf8]{inputenc} +\usepackage[vietnamese,english]{babel} +\usepackage{pifont} +\usepackage{float} +\usepackage{caption} +\usepackage{placeins} +\usepackage{array} +\newcolumntype{P}[1]{>{\centering\arraybackslash}p{#1}} +\newcolumntype{M}[1]{>{\centering\arraybackslash}m{#1}} +\usepackage{multirow} +\usepackage{hyperref} +\usepackage{amsmath} +\hypersetup{colorlinks, citecolor=blue, linkcolor=blue, urlcolor=blue} + +\begin{document} +% \title{Emotion Recognition for Vietnamese Social Media Text} +\title{Emotion Recognition\\for Vietnamese Social Media Text} + +\titlerunning{Emotion Recognition for Vietnamese Social Media Text} + +\author{Vong Anh Ho\inst{1,4}\textsuperscript{(\Letter)} \and +Duong Huynh-Cong Nguyen\inst{1,4} \and +Danh Hoang Nguyen\inst{1,4} \and +\\Linh Thi-Van Pham\inst{2,4} \and +Duc-Vu Nguyen\inst{3,4} \and +\\Kiet Van Nguyen\inst{1,4} \and +Ngan Luu-Thuy Nguyen\inst{1,4}} + +\authorrunning{Vong Anh Ho et al.} + +\institute{University of Information Technology, VNU-HCM, Vietnam\\ +\email{\{15521025,15520148,15520090\}@gm.uit.edu.vn, \{kietnv,ngannlt\}@uit.edu.vn}\\ +\and +University of Social Sciences and Humanities, VNU-HCM, Vietnam\\ +\email{vanlinhpham888@gmail.com}\\ +\and +Multimedia Communications Laboratory, University of Information Technology, VNU-HCM, Vietnam\\ +\email{vund@uit.edu.vn}\\ +\and +Vietnam National University, Ho Chi Minh City, Vietnam} +\maketitle + +\begin{abstract} + +Emotion recognition or emotion prediction is a higher approach or a special case of sentiment analysis. In this task, the result is not produced in terms of either polarity: positive or negative or in the form of rating (from 1 to 5) but of a more detailed level of analysis in which the results are depicted in more expressions like sadness, enjoyment, anger, disgust, fear and surprise. Emotion recognition plays a critical role in measuring brand value of a product by recognizing specific emotions of customers' comments. In this study, we have achieved two targets. First and foremost, we built a standard \textbf{V}ietnamese \textbf{S}ocial \textbf{M}edia \textbf{E}motion \textbf{C}orpus (UIT-VSMEC) with exactly 6,927 emotion-annotated sentences, contributing to emotion recognition research in Vietnamese which is a low-resource language in natural language processing (NLP). Secondly, we assessed and measured machine learning and deep neural network models on our UIT-VSMEC corpus. As a result, the CNN model achieved the highest performance with the weighted F1-score of 59.74\%. Our corpus is available at our research website \footnote[1]{\url{ https://sites.google.com/uit.edu.vn/uit-nlp/corpora-projects}}. + +\keywords{Emotion Recognition \and Emotion Prediction \and Vietnamese \and Machine Learning \and Deep Learning \and CNN \and LSTM \and SVM.} +\end{abstract} + +\input{sections/1-introduction.tex} +\input{sections/2-relatedwork.tex} +\input{sections/3-corpus.tex} +\input{sections/4-method.tex} +\input{sections/5-experiments.tex} +\input{sections/6-conclusion.tex} + +\section*{Acknowledgment} +We would like to give our thanks to the NLP@UIT research group and the Citynow-UIT Laboratory of the University of Information Technology - Vietnam National University Ho Chi Minh City for their supports with pragmatic and inspiring advice. + +\bibliographystyle{splncs04} +% \bibliography{bibliography} +\begin{thebibliography}{10} +\providecommand{\url}[1]{\texttt{#1}} +\providecommand{\urlprefix}{URL } +\providecommand{\doi}[1]{https://doi.org/#1} + +\bibitem{PlabanKumarBhowmick} +{Bhowmick}, P.K., {Basu}, A., {Mitra}, P.: {An Agreement Measure for + Determining Inter-Annotator Reliability of Human Judgements on Affective + Tex}. In: {Proceedings of the workshop on Human Judgements in Computational + Linguistics}. pp. 58--65. COLING 2008, Manchester, United Kingdom (2008) + +\bibitem{Jointstockcompany} +company, J.S.: {The habit of using social networks of Vietnamese people 2018}. + brands vietnam, Ho Chi Minh City, Vietnam (2018) + +\bibitem{Ekman1993} +{Ekman}, P.: In: {Facial expression and emotion}. vol.~48, pp. 384--392. + {American Psychologist} (1993) + +\bibitem{Ekman} +{Ekman}, P.: In: {Emotions revealed: Recognizing faces and feelings to improve + communication and emotional life}. p.~2007. {Macmillan} (2012) + +\bibitem{PaulEkman} +{Ekman}, P., {Ekman}, E., {Lama}, D.: In: {The Ekmans' Atlas of Emotion} (2018) + +\bibitem{Kim} +{Kim}, Y.: {Convolutional Neural Networks for Sentence Classifications}. In: + {Proceedings of the 2014 Conference on Empirical Methods in Natural Language + Processing (EMNLP)}. pp. 1746--1751. { Association for Computational + Linguistics}, Doha, Qatar (2014) + +\bibitem{Kiritchenko} +{Kiritchenko}, S., {Mohammad}, S.: {Using Hashtags to Capture Fine Emotion + Categories from Tweets}. In: {Computational Intelligence}. pp. 301--326 + (2015) + +\bibitem{RomanKlinger} +{Klinger}, R., {Clerc}, O.D., {Mohammad}, S.M., {Balahur}, A.: {IEST:WASSA-2018 + Implicit Emotions Shared Task}. pp. 31--42. 2017 AFNLP, Brussels, Belgium + (2018) + +\bibitem{BernhardKratzwald} +{Kratzwald}, B., {Ilic}, S., {Kraus}, M., S.~{Feuerriegel}, H.P.: {Decision + support with text-based emotion recognition: Deep learning for affective + computing}. pp. 24 -- 35. {Decision Support Systems} (2018) + +\bibitem{SaifMohammad2017} +{Mohammad}, S., {Bravo-Marquez}, F.: {Emotion Intensities in Tweets}. 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In:Companion Proceedings of the The Web Conference 2018. pp. 1049–1055 (2018) + + + + + + + +\end{thebibliography} + + +\end{document} diff --git a/references/2019.arxiv.ho/source/bibliography.bib b/references/2019.arxiv.ho/source/bibliography.bib new file mode 100644 index 0000000000000000000000000000000000000000..57f0928d2ba909571ea17cd284f647f5be145726 --- /dev/null +++ b/references/2019.arxiv.ho/source/bibliography.bib @@ -0,0 +1,289 @@ +@InProceedings{Kiritchenko, + title = "{Using Hashtags to Capture Fine Emotion Categories from Tweets}", + author = {S. {Kiritchenko} and S. {Mohammad}}, + booktitle = "{Computational Intelligence}", + year = {2015}, + pages = {301-326}, +} + +@InProceedings{BernhardKratzwald, + title = "{Decision support with text-based emotion recognition: Deep learning for affective computing}", + author = { B. {Kratzwald} and S. {Ilic} and M. {Kraus} and S. {Feuerriegel}, H. {Prendinger}}, + year = {2018}, + publisher = "{Decision Support Systems}", + pages = {24 - 35}, +} + +@InProceedings{ApurbaPaul, + title = "{Identification and Classification of Emotional Key Phrases from Psychological texts.}", + author = "{A. {Paul} and D. {Das}}", + booktitle = "{Proceedings of the ACL 2015 Workshop on Novel Computational Approaches to Keyphrase Extraction}", + year = {2015}, + publisher = "{Association for Computational Linguistics}", + address = {Beijing, China}, + pages = {32 - 38}, +} + +@InProceedings{CarloStrapparava, + title = "{SemEval-2007 Task 14: Affective Text}", + author = {C. {Strapparava} and R. {Mihalcea}}, + booktitle = "{Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval-2007)}", + year = {2007}, + publisher = "{ Association for Computational Linguistics}", + address = {Prague}, + pages = {70-74}, +} +@InProceedings{tran2009, + author = {O. T. {Tran} and C. A. {Le} and T. Q. {Ha} and Q. H. {Le}}, + booktitle = "{2009 International Conference on Asian Language Processing}", + title = "{An Experimental Study on Vietnamese POS Tagging}", + year = {2009}, + pages = {23-27} +} + +@InProceedings{Facial2007, + author = {S. {Zhang} and Z. {Wu} and H. {Meng} and L. {Cai}}, + year = {2007}, + month = {09}, + pages = {24-35}, + title = {Facial Expression Synthesis Using PAD Emotional Parameters for a Chinese Expressive Avatar}, + volume = {4738} +} + +@InProceedings{vncorenlp, + title = "{{V}n{C}ore{NLP}: A {V}ietnamese Natural Language Processing Toolkit}", + author = {T. {Vu} and Q. D. {Nguyen} and Q. D. {Nguyen} and M. {Dras} and M. {Johnson}}, + booktitle = "{Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations}", + year = {2018}, + address = "{New Orleans, Louisiana}", + publisher = "{Association for Computational Linguistics}", + pages = {56-60} +} + +@InProceedings{Ekman, + booktitle = "{Emotions revealed: Recognizing faces and feelings to improve communication and emotional life}", + author = {P. {Ekman}}, + year = {2012}, + publisher = "{Macmillan}", + pages = {2007}, +} + +@InProceedings{Ekman1993, + booktitle = "{Facial expression and emotion}", + author = {P. {Ekman}}, + year = {1993}, + publisher = "{American Psychologist}", + pages = {384-392}, + volume = {48} +} + +@InProceedings{VLSPX, + title = "{VLSP Shared Task: Sentiment Analysis}", + author = {H. T. M. {Nguyen} and H. V. {Nguyen} and Q. T. {Ngo} and L. X. {Vu} and V. M. {Tran} and B. X. {Ngo} and C. A. {Le}}, + booktitle = "{Journal of Computer Science and Cybernetics}", + year = {2018}, + pages = {295-310}, +} + +@InProceedings{KietVanNguyen, + title = "{UIT-VSFC: Vietnamese Students’ Feedback Corpus for Sentiment Analysis}", + author = {K. V. {Nguyen} and V. D. {Nguyen} and P. {Nguyen} and T. {Truong} and N. L. T. {Nguyen}}, + booktitle = "{2018 10th International Conference on Knowledge and Systems Engineering (KSE)}", + year = {2018}, + publisher = "{IEEE}", + pages = {19-24}, + address = {Ho Chi Minh City, Vietnam}, +} + +@InProceedings{PhuNguyen, + title = "{Deep Learning versus Traditional Classifiers on Vietnamese Students' Feedback Corpus}", + author = { P. X. V. {Nguyen} and T. T. H. {Truong} and K. V. {Nguyen} and N. L. T. {Nguyen}}, + booktitle = "{2018 5th NAFOSTED Conference on Information and Computer Science (NICS)}", + year = {2018}, + pages = {75-80}, + address = {Ho Chi Minh City, Vietnam}, +} + +@InProceedings{Kim, + title = "{Convolutional Neural Networks for Sentence Classifications}", + author = {Y. {Kim}}, + booktitle = "{Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)}", + year = {2014}, + publisher = "{ Association for Computational Linguistics}", + pages = {1746-1751}, + address = {Doha, Qatar} +} + +@InProceedings{Mohammad, + title = "{\#Emotional Tweets}", + author = {S. M. {Mohammad}}, + booktitle = "{First Joint Conference on Lexical and Computational Semantics (*SEM)}", + year = {2012}, + publisher = "{Association for Computational Linguistics}", + pages = {246-255}, + address = {Montreal, Canada} +} + +@InProceedings{PaulEkman, + booktitle = "{The Ekmans' Atlas of Emotion}", + author = {P. {Ekman} and E. {Ekman} and D. {Lama}}, + year = {2018} +} + +@InProceedings{PlabanKumarBhowmick, + title = "{An Agreement Measure for Determining Inter-Annotator Reliability of Human Judgements on Affective Tex}", + author = {P. K. {Bhowmick} and A. {Basu} and P. {Mitra}}, + booktitle = "{Proceedings of the workshop on Human Judgements in Computational Linguistics}", + publisher = {COLING 2008}, + year = {2008}, + address = {Manchester, United Kingdom}, + pages = {58-65}, +} + +@InProceedings{Nguyen, + title = "{Vietnam has the 7th largest number of Facebook users in the world}", + author = {Nguyen}, + publisher = {Dan Tri newspaper}, + year = {2018} +} + +@InProceedings{SaifMohammad, + title = "{Sentiment, emotion, purpose, and style in electoral tweets}", + author = {S. M. {Mohammad} and Z. {Xiaodan} and S. {Kiritchenko} and J. {Martin}}, + publisher = {Information Processing and Management: an International Journal}, + year = {2015}, + pages = {480-499}, +} + +@InProceedings{Mohammad2018, + title = "{SemEval-2018 task 1: Affect in tweets}", + author = { S. M. {Mohammad} and F. {Bravo-Marquez} and M. {Salameh} and S. {Kiritchenko}}, + publisher = { Proceedings of International Workshop on Semantic Evaluation}, + year = {2018}, + pages = {1-17}, + address = {New Orleans, Louisiana}, +} + +@InProceedings{SaifMohammad2017, + title = "{Emotion Intensities in Tweets}", + author = {S. {Mohammad} and F. {Bravo-Marquez}}, + booktitle = "{Proceedings of the Sixth Joint Conference on Lexical and Computational Semantics (*SEM)}", + publisher = {Association for Computational Linguistics}, + year = {2017}, + pages = {65-77}, + address = {Vancouver, Canada}, +} + +@InProceedings{smd1, + title = "{Social media data}", + author = {Science and Information Technology}, + publisher = {Science and Information Technology - University of Information Technology}, + year = {2016} +} + +@InProceedings{TingweiWang, + title = "{A Multi-emotion Classification Method Based on BLSTM-MC in Code-Switching Text: 7th CCF International Conference, NLPCC 2018, Hohhot, China, August 26–30, 2018, Proceedings, Part II.}", + author = "{T. {Wang} and X. {Yang} and C. {Ouyang}}", + publisher = {Natural Language Processing and Chinese Computing}, + year = {2018}, + pages = {190-199}, +} + +@InProceedings{VenkateshDuppada, + title = "{SeerNet at SemEval-2018 Task 1: Domain Adaptation for Affect in Tweets}", + author = {V. {Duppada} and R. {Jain} and S. {Hiray}}, + booktitle = "{*SEMEVAL}", + publisher = {Association for Computational Linguistics}, + address = {New Orleans, Louisiana}, + year = {2018}, + pages = {18-23}, +} +@InProceedings{VoNgocPhu, + title = "{A Vietnamese adjective emotion dictionary based on exploitation of Vietnamese language characteristics}", + author = {P. N. {Vo} and C. T. {Vo} and T. T. {Vo} and D. D. {Nguyen}}, + publisher = {Artificial Intelligence Review}, + address = {New Orleans, Louisiana}, + year = {2018}, + pages = {93-159}, +} + +@InProceedings{VuDucNguyen, + title = "{Variants of Long Short-Term Memory for Sentiment Analysis on Vietnamese Students’ Feedback Corpus}", + author = {V. D. {Nguyen} and K. V. {Nguyen} and N. L. T. {Nguyen}}, + booktitle = "{2018 10th International Conference on Knowledge and Systems Engineering (KSE)}", + publisher = {IEEE}, + address = {Ho Chi Minh City, Vietnam}, + year = {2018}, + pages = {306-311}, +} +@InProceedings{Jointstockcompany, + title = "{The habit of using social networks of Vietnamese people 2018}", + author = {Joint Stock company}, + publisher = {brands vietnam}, + address = {Ho Chi Minh City, Vietnam}, + year = {2018} +} +@InProceedings{Yam, + title = "{Emotion Detection and Recognition from Text Using Deep Learning}", + author = {C. Y. {Yam}}, + year = {2018}, + publisher = {Developer blog} +} +@InProceedings{YingjieZhang, + title = "{A Sensitivity Analysis of (and Practitioners’ Guide to Convolutional}", + author = {Y. {Zhang} and B. C. {Wallace}}, + booktiltle= "{Proceedings of the The 8th International Joint Conference on Natural Language Processing}", + publisher = {2017 AFNLP}, + address = {Taipei, Taiwan}, + year = {2017}, + pages = {253-263}, +} +@InProceedings{ZhongqingWang, + title = "{Overview of NLPCC 2018 Shared Task 1: Emotion Detection in Code-Switching Text: 7th CCF International Conference, NLPCC 2018, Hohhot, China, August 26–30, 2018, Proceedings, Part II}", + author = {Z. {Wang} and S. {Li}}, + publisher = {Natural Language Processing and Chinese Computing}, + year = {2018}, + pages = {429-433}, +} +@InProceedings{RomanKlinger, + title = "{IEST:WASSA-2018 Implicit Emotions Shared Task}", + author = {R. {Klinger} and O. D. {Clerc} and S. M. {Mohammad} and A. {Balahur}}, + booktiltle= "{2018 Association for Computational Linguistics}", + publisher = {2017 AFNLP}, + address = {Brussels, Belgium}, + year = {2018}, + pages = {31-42}, +} +@InProceedings{joulin2016fasttext, + title="{FastText.zip: Compressing text classification models}", + author={Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Douze, Matthijs and J{\'e}gou, H{\'e}rve and Mikolov, Tomas}, + journal="{arXiv preprint arXiv:1612.03651}", + year={2016}, +} + +@Article{AurelienGeron, + author = {Pedregosa, Fabian and Varoquaux, Ga\"{e}l and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and Vanderplas, Jake and Passos, Alexandre and Cournapeau, David and Brucher, Matthieu and Perrot, Matthieu and Duchesnay, \'{E}douard}, + title = {Scikit-learn: Machine Learning in Python}, + journal = {Journal of Machine Learning Research}, + volume = {12}, + year = {2011}, + pages = {2825-2830} +} + +@InProceedings{KietVanNguyen1, + title = "{UIT-VSFC: Vietnamese Students’ Feedback Corpus for Sentiment Analysis}", + author = {K. V. {Nguyen} and V. D. {Nguyen} and P. {Nguyen} and T. {Truong} and N. L. T. {Nguyen}}, + booktitle = "{2018 10th International Conference on Knowledge and Systems Engineering (KSE)}", + year = {2018}, + pages = {19-24}, + address = {Ho Chi Minh City, Vietnam}, +} + +@Inproceedings{nguyen2016, + title={Vietnamese transition-based dependency parsing with supertag features}, + author={Nguyen, Kiet V and Nguyen, Ngan Luu-Thuy}, + booktitle={2016 Eighth International Conference on Knowledge and Systems Engineering (KSE)}, + pages={175--180}, + year={2016}, + organization={IEEE} +} \ No newline at end of file diff --git a/references/2019.arxiv.ho/source/images/DataProcessing.pdf b/references/2019.arxiv.ho/source/images/DataProcessing.pdf new file mode 100644 index 0000000000000000000000000000000000000000..4aad412c5984fc74f37cd7caeca457cebb5876a2 --- /dev/null +++ b/references/2019.arxiv.ho/source/images/DataProcessing.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0c2e2acb142678c169cb7523e8b6e151b726648fa3d048c149a8594e1791189d +size 10809 diff --git a/references/2019.arxiv.ho/source/images/cnnmodel.pdf b/references/2019.arxiv.ho/source/images/cnnmodel.pdf new file mode 100644 index 0000000000000000000000000000000000000000..f920f22d5aeff3da107316136a335c980a2a2d94 --- /dev/null +++ b/references/2019.arxiv.ho/source/images/cnnmodel.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e96bee03b1dec742297dbdf7c3a9f1fb99386c3b13372549c9e4dba07a59e571 +size 74015 diff --git a/references/2019.arxiv.ho/source/images/con_matrix.png b/references/2019.arxiv.ho/source/images/con_matrix.png new file mode 100644 index 0000000000000000000000000000000000000000..42dc769367526ef354ecb9ac36d63a2d55f28690 --- /dev/null +++ b/references/2019.arxiv.ho/source/images/con_matrix.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a153882bb69258af0fbe136853563cec63d231b2b3b4a36a4f4d7f643db4b2b0 +size 153888 diff --git a/references/2019.arxiv.ho/source/images/confusion_matrix.pdf b/references/2019.arxiv.ho/source/images/confusion_matrix.pdf new file mode 100644 index 0000000000000000000000000000000000000000..671dea8b9a7e9dd310c29f776f66d29d7378d540 --- /dev/null +++ b/references/2019.arxiv.ho/source/images/confusion_matrix.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:523a704f183b8ebe06afda81dee17f7570f4782fd9b0a1ebcdd0f0710923fcd7 +size 21538 diff --git a/references/2019.arxiv.ho/source/images/curve.pdf b/references/2019.arxiv.ho/source/images/curve.pdf new file mode 100644 index 0000000000000000000000000000000000000000..8e8b189f2e17af7df51087f5ba3ae95a2957dbf7 --- /dev/null +++ b/references/2019.arxiv.ho/source/images/curve.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:11e4a71b8373d46d6d5dcd8fadae85220a77bce6c882f4d62aa47c635f63c2db +size 14211 diff --git a/references/2019.arxiv.ho/source/images/dataset.JPG b/references/2019.arxiv.ho/source/images/dataset.JPG new file mode 100644 index 0000000000000000000000000000000000000000..9c83d049b7593152e5fee237f3c385057d3dbd55 Binary files /dev/null and b/references/2019.arxiv.ho/source/images/dataset.JPG differ diff --git a/references/2019.arxiv.ho/source/images/foo.pdf b/references/2019.arxiv.ho/source/images/foo.pdf new file mode 100644 index 0000000000000000000000000000000000000000..86009b6edb4ab5c8290d92af4a6db9a6b834047f --- /dev/null +++ b/references/2019.arxiv.ho/source/images/foo.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:590a97dea5eff0bbe040ffdb7fb1fbd6e0de58c718f9b73a2ddb938b30c77ec4 +size 15497 diff --git a/references/2019.arxiv.ho/source/images/gettingData.pdf b/references/2019.arxiv.ho/source/images/gettingData.pdf new file mode 100644 index 0000000000000000000000000000000000000000..ce3670770ab0eac544c1d08390b06a49ce20e1a2 --- /dev/null +++ b/references/2019.arxiv.ho/source/images/gettingData.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1d294219cef509f549ce731d659125b2902af9e4e8cfca790f599a754766e13b +size 11683 diff --git a/references/2019.arxiv.ho/source/llncs.cls b/references/2019.arxiv.ho/source/llncs.cls new file mode 100644 index 0000000000000000000000000000000000000000..886bf7264d8dd0e22e13e9bcf419c1fa6b1448cc --- /dev/null +++ b/references/2019.arxiv.ho/source/llncs.cls @@ -0,0 +1,1218 @@ +% LLNCS DOCUMENT CLASS -- version 2.20 (10-Mar-2018) +% Springer Verlag LaTeX2e support for Lecture Notes in Computer Science +% +%% +%% \CharacterTable +%% {Upper-case \A\B\C\D\E\F\G\H\I\J\K\L\M\N\O\P\Q\R\S\T\U\V\W\X\Y\Z +%% Lower-case \a\b\c\d\e\f\g\h\i\j\k\l\m\n\o\p\q\r\s\t\u\v\w\x\y\z +%% Digits \0\1\2\3\4\5\6\7\8\9 +%% Exclamation \! Double quote \" Hash (number) \# +%% Dollar \$ Percent \% Ampersand \& +%% Acute accent \' Left paren \( Right paren \) +%% Asterisk \* Plus \+ Comma \, +%% Minus \- Point \. Solidus \/ +%% Colon \: Semicolon \; Less than \< +%% Equals \= Greater than \> Question mark \? +%% Commercial at \@ Left bracket \[ Backslash \\ +%% Right bracket \] Circumflex \^ Underscore \_ +%% Grave accent \` Left brace \{ Vertical bar \| +%% Right brace \} Tilde \~} +%% +\NeedsTeXFormat{LaTeX2e}[1995/12/01] +\ProvidesClass{llncs}[2018/03/10 v2.20 +^^J LaTeX document class for Lecture Notes in Computer Science] +% Options +\let\if@envcntreset\iffalse +\DeclareOption{envcountreset}{\let\if@envcntreset\iftrue} +\DeclareOption{citeauthoryear}{\let\citeauthoryear=Y} +\DeclareOption{oribibl}{\let\oribibl=Y} +\let\if@custvec\iftrue +\DeclareOption{orivec}{\let\if@custvec\iffalse} +\let\if@envcntsame\iffalse +\DeclareOption{envcountsame}{\let\if@envcntsame\iftrue} +\let\if@envcntsect\iffalse +\DeclareOption{envcountsect}{\let\if@envcntsect\iftrue} +\let\if@runhead\iffalse +\DeclareOption{runningheads}{\let\if@runhead\iftrue} + +\let\if@openright\iftrue +\let\if@openbib\iffalse +\DeclareOption{openbib}{\let\if@openbib\iftrue} + +% languages +\let\switcht@@therlang\relax +\def\ds@deutsch{\def\switcht@@therlang{\switcht@deutsch}} +\def\ds@francais{\def\switcht@@therlang{\switcht@francais}} + +\DeclareOption*{\PassOptionsToClass{\CurrentOption}{article}} + +\ProcessOptions + +\LoadClass[twoside]{article} +\RequirePackage{multicol} % needed for the list of participants, index +\RequirePackage{aliascnt} + +\setlength{\textwidth}{12.2cm} +\setlength{\textheight}{19.3cm} +\renewcommand\@pnumwidth{2em} +\renewcommand\@tocrmarg{3.5em} +% +\def\@dottedtocline#1#2#3#4#5{% + \ifnum #1>\c@tocdepth \else + \vskip \z@ \@plus.2\p@ + {\leftskip #2\relax \rightskip \@tocrmarg \advance\rightskip by 0pt plus 2cm + \parfillskip -\rightskip \pretolerance=10000 + \parindent #2\relax\@afterindenttrue + \interlinepenalty\@M + \leavevmode + \@tempdima #3\relax + \advance\leftskip \@tempdima \null\nobreak\hskip -\leftskip + {#4}\nobreak + \leaders\hbox{$\m@th + \mkern \@dotsep mu\hbox{.}\mkern \@dotsep + mu$}\hfill + \nobreak + \hb@xt@\@pnumwidth{\hfil\normalfont \normalcolor #5}% + \par}% + \fi} +% +\def\switcht@albion{% +\def\abstractname{Abstract.} +\def\ackname{Acknowledgement.} +\def\andname{and} +\def\lastandname{\unskip, and} +\def\appendixname{Appendix} +\def\chaptername{Chapter} +\def\claimname{Claim} +\def\conjecturename{Conjecture} +\def\contentsname{Table of Contents} +\def\corollaryname{Corollary} +\def\definitionname{Definition} +\def\examplename{Example} +\def\exercisename{Exercise} +\def\figurename{Fig.} +\def\keywordname{{\bf Keywords:}} +\def\indexname{Index} +\def\lemmaname{Lemma} +\def\contriblistname{List of Contributors} +\def\listfigurename{List of Figures} +\def\listtablename{List of Tables} +\def\mailname{{\it Correspondence to\/}:} +\def\noteaddname{Note added in proof} +\def\notename{Note} +\def\partname{Part} +\def\problemname{Problem} +\def\proofname{Proof} +\def\propertyname{Property} +\def\propositionname{Proposition} +\def\questionname{Question} +\def\remarkname{Remark} +\def\seename{see} +\def\solutionname{Solution} +\def\subclassname{{\it Subject Classifications\/}:} +\def\tablename{Table} +\def\theoremname{Theorem}} +\switcht@albion +% Names of theorem like environments are already defined +% but must be translated if another language is chosen +% +% French section +\def\switcht@francais{%\typeout{On parle francais.}% + \def\abstractname{R\'esum\'e.}% + \def\ackname{Remerciements.}% + \def\andname{et}% + \def\lastandname{ et}% + \def\appendixname{Appendice} + \def\chaptername{Chapitre}% + \def\claimname{Pr\'etention}% + \def\conjecturename{Hypoth\`ese}% + \def\contentsname{Table des mati\`eres}% + \def\corollaryname{Corollaire}% + \def\definitionname{D\'efinition}% + \def\examplename{Exemple}% + \def\exercisename{Exercice}% + \def\figurename{Fig.}% + \def\keywordname{{\bf Mots-cl\'e:}} + \def\indexname{Index} + \def\lemmaname{Lemme}% + \def\contriblistname{Liste des contributeurs} + \def\listfigurename{Liste des figures}% + \def\listtablename{Liste des tables}% + \def\mailname{{\it Correspondence to\/}:} + \def\noteaddname{Note ajout\'ee \`a l'\'epreuve}% + \def\notename{Remarque}% + \def\partname{Partie}% + \def\problemname{Probl\`eme}% + \def\proofname{Preuve}% + \def\propertyname{Caract\'eristique}% +%\def\propositionname{Proposition}% + \def\questionname{Question}% + \def\remarkname{Remarque}% + \def\seename{voir} + \def\solutionname{Solution}% + \def\subclassname{{\it Subject Classifications\/}:} + \def\tablename{Tableau}% + \def\theoremname{Th\'eor\`eme}% +} +% +% German section +\def\switcht@deutsch{%\typeout{Man spricht deutsch.}% + \def\abstractname{Zusammenfassung.}% + \def\ackname{Danksagung.}% + \def\andname{und}% + \def\lastandname{ und}% + \def\appendixname{Anhang}% + \def\chaptername{Kapitel}% + \def\claimname{Behauptung}% + \def\conjecturename{Hypothese}% + \def\contentsname{Inhaltsverzeichnis}% + \def\corollaryname{Korollar}% +%\def\definitionname{Definition}% + \def\examplename{Beispiel}% + \def\exercisename{\"Ubung}% + \def\figurename{Abb.}% + \def\keywordname{{\bf Schl\"usselw\"orter:}} + \def\indexname{Index} +%\def\lemmaname{Lemma}% + \def\contriblistname{Mitarbeiter} + \def\listfigurename{Abbildungsverzeichnis}% + \def\listtablename{Tabellenverzeichnis}% + \def\mailname{{\it Correspondence to\/}:} + \def\noteaddname{Nachtrag}% + \def\notename{Anmerkung}% + \def\partname{Teil}% +%\def\problemname{Problem}% + \def\proofname{Beweis}% + \def\propertyname{Eigenschaft}% +%\def\propositionname{Proposition}% + \def\questionname{Frage}% + \def\remarkname{Anmerkung}% + \def\seename{siehe} + \def\solutionname{L\"osung}% + \def\subclassname{{\it Subject Classifications\/}:} + \def\tablename{Tabelle}% +%\def\theoremname{Theorem}% +} + +% Ragged bottom for the actual page +\def\thisbottomragged{\def\@textbottom{\vskip\z@ plus.0001fil +\global\let\@textbottom\relax}} + +\renewcommand\small{% + \@setfontsize\small\@ixpt{11}% + \abovedisplayskip 8.5\p@ \@plus3\p@ \@minus4\p@ + \abovedisplayshortskip \z@ \@plus2\p@ + \belowdisplayshortskip 4\p@ \@plus2\p@ \@minus2\p@ + \def\@listi{\leftmargin\leftmargini + \parsep 0\p@ \@plus1\p@ \@minus\p@ + \topsep 8\p@ \@plus2\p@ \@minus4\p@ + \itemsep0\p@}% + \belowdisplayskip \abovedisplayskip +} + +\frenchspacing +\widowpenalty=10000 +\clubpenalty=10000 + +\setlength\oddsidemargin {63\p@} +\setlength\evensidemargin {63\p@} +\setlength\marginparwidth {90\p@} + +\setlength\headsep {16\p@} + +\setlength\footnotesep{7.7\p@} +\setlength\textfloatsep{8mm\@plus 2\p@ \@minus 4\p@} +\setlength\intextsep {8mm\@plus 2\p@ \@minus 2\p@} + +\setcounter{secnumdepth}{2} + +\newcounter {chapter} +\renewcommand\thechapter {\@arabic\c@chapter} + +\newif\if@mainmatter \@mainmattertrue +\newcommand\frontmatter{\cleardoublepage + \@mainmatterfalse\pagenumbering{Roman}} +\newcommand\mainmatter{\cleardoublepage + \@mainmattertrue\pagenumbering{arabic}} +\newcommand\backmatter{\if@openright\cleardoublepage\else\clearpage\fi + \@mainmatterfalse} + +\renewcommand\part{\cleardoublepage + \thispagestyle{empty}% + \if@twocolumn + \onecolumn + \@tempswatrue + \else + \@tempswafalse + \fi + \null\vfil + \secdef\@part\@spart} + +\def\@part[#1]#2{% + \ifnum \c@secnumdepth >-2\relax + \refstepcounter{part}% + \addcontentsline{toc}{part}{\thepart\hspace{1em}#1}% + \else + \addcontentsline{toc}{part}{#1}% + \fi + \markboth{}{}% + {\centering + \interlinepenalty \@M + \normalfont + \ifnum \c@secnumdepth >-2\relax + \huge\bfseries \partname~\thepart + \par + \vskip 20\p@ + \fi + \Huge \bfseries #2\par}% + \@endpart} +\def\@spart#1{% + {\centering + \interlinepenalty \@M + \normalfont + \Huge \bfseries #1\par}% + \@endpart} +\def\@endpart{\vfil\newpage + \if@twoside + \null + \thispagestyle{empty}% + \newpage + \fi + \if@tempswa + \twocolumn + \fi} + +\newcommand\chapter{\clearpage + \thispagestyle{empty}% + \global\@topnum\z@ + \@afterindentfalse + \secdef\@chapter\@schapter} +\def\@chapter[#1]#2{\ifnum \c@secnumdepth >\m@ne + \if@mainmatter + \refstepcounter{chapter}% + \typeout{\@chapapp\space\thechapter.}% + \addcontentsline{toc}{chapter}% + {\protect\numberline{\thechapter}#1}% + \else + \addcontentsline{toc}{chapter}{#1}% + \fi + \else + \addcontentsline{toc}{chapter}{#1}% + \fi + \chaptermark{#1}% + \addtocontents{lof}{\protect\addvspace{10\p@}}% + \addtocontents{lot}{\protect\addvspace{10\p@}}% + \if@twocolumn + \@topnewpage[\@makechapterhead{#2}]% + \else + \@makechapterhead{#2}% + \@afterheading + \fi} +\def\@makechapterhead#1{% +% \vspace*{50\p@}% + {\centering + \ifnum \c@secnumdepth >\m@ne + \if@mainmatter + \large\bfseries \@chapapp{} \thechapter + \par\nobreak + \vskip 20\p@ + \fi + \fi + \interlinepenalty\@M + \Large \bfseries #1\par\nobreak + \vskip 40\p@ + }} +\def\@schapter#1{\if@twocolumn + \@topnewpage[\@makeschapterhead{#1}]% + \else + \@makeschapterhead{#1}% + \@afterheading + \fi} +\def\@makeschapterhead#1{% +% \vspace*{50\p@}% + {\centering + \normalfont + \interlinepenalty\@M + \Large \bfseries #1\par\nobreak + \vskip 40\p@ + }} + +\renewcommand\section{\@startsection{section}{1}{\z@}% + {-18\p@ \@plus -4\p@ \@minus -4\p@}% + {12\p@ \@plus 4\p@ \@minus 4\p@}% + {\normalfont\large\bfseries\boldmath + \rightskip=\z@ \@plus 8em\pretolerance=10000 }} +\renewcommand\subsection{\@startsection{subsection}{2}{\z@}% + {-18\p@ \@plus -4\p@ \@minus -4\p@}% + {8\p@ \@plus 4\p@ \@minus 4\p@}% + {\normalfont\normalsize\bfseries\boldmath + \rightskip=\z@ \@plus 8em\pretolerance=10000 }} +\renewcommand\subsubsection{\@startsection{subsubsection}{3}{\z@}% + {-18\p@ \@plus -4\p@ \@minus -4\p@}% + {-0.5em \@plus -0.22em \@minus -0.1em}% + {\normalfont\normalsize\bfseries\boldmath}} +\renewcommand\paragraph{\@startsection{paragraph}{4}{\z@}% + {-12\p@ \@plus -4\p@ \@minus -4\p@}% + {-0.5em \@plus -0.22em \@minus -0.1em}% + {\normalfont\normalsize\itshape}} +\renewcommand\subparagraph[1]{\typeout{LLNCS warning: You should not use + \string\subparagraph\space with this class}\vskip0.5cm +You should not use \verb|\subparagraph| with this class.\vskip0.5cm} + +\DeclareMathSymbol{\Gamma}{\mathalpha}{letters}{"00} +\DeclareMathSymbol{\Delta}{\mathalpha}{letters}{"01} +\DeclareMathSymbol{\Theta}{\mathalpha}{letters}{"02} +\DeclareMathSymbol{\Lambda}{\mathalpha}{letters}{"03} +\DeclareMathSymbol{\Xi}{\mathalpha}{letters}{"04} +\DeclareMathSymbol{\Pi}{\mathalpha}{letters}{"05} +\DeclareMathSymbol{\Sigma}{\mathalpha}{letters}{"06} +\DeclareMathSymbol{\Upsilon}{\mathalpha}{letters}{"07} +\DeclareMathSymbol{\Phi}{\mathalpha}{letters}{"08} +\DeclareMathSymbol{\Psi}{\mathalpha}{letters}{"09} +\DeclareMathSymbol{\Omega}{\mathalpha}{letters}{"0A} + +\let\footnotesize\small + +\if@custvec +\def\vec#1{\mathchoice{\mbox{\boldmath$\displaystyle#1$}} +{\mbox{\boldmath$\textstyle#1$}} +{\mbox{\boldmath$\scriptstyle#1$}} +{\mbox{\boldmath$\scriptscriptstyle#1$}}} +\fi + +\def\squareforqed{\hbox{\rlap{$\sqcap$}$\sqcup$}} +\def\qed{\ifmmode\squareforqed\else{\unskip\nobreak\hfil +\penalty50\hskip1em\null\nobreak\hfil\squareforqed +\parfillskip=0pt\finalhyphendemerits=0\endgraf}\fi} + +\def\getsto{\mathrel{\mathchoice {\vcenter{\offinterlineskip +\halign{\hfil +$\displaystyle##$\hfil\cr\gets\cr\to\cr}}} +{\vcenter{\offinterlineskip\halign{\hfil$\textstyle##$\hfil\cr\gets +\cr\to\cr}}} +{\vcenter{\offinterlineskip\halign{\hfil$\scriptstyle##$\hfil\cr\gets +\cr\to\cr}}} +{\vcenter{\offinterlineskip\halign{\hfil$\scriptscriptstyle##$\hfil\cr +\gets\cr\to\cr}}}}} +\def\lid{\mathrel{\mathchoice {\vcenter{\offinterlineskip\halign{\hfil +$\displaystyle##$\hfil\cr<\cr\noalign{\vskip1.2pt}=\cr}}} +{\vcenter{\offinterlineskip\halign{\hfil$\textstyle##$\hfil\cr<\cr +\noalign{\vskip1.2pt}=\cr}}} +{\vcenter{\offinterlineskip\halign{\hfil$\scriptstyle##$\hfil\cr<\cr +\noalign{\vskip1pt}=\cr}}} +{\vcenter{\offinterlineskip\halign{\hfil$\scriptscriptstyle##$\hfil\cr +<\cr +\noalign{\vskip0.9pt}=\cr}}}}} +\def\gid{\mathrel{\mathchoice {\vcenter{\offinterlineskip\halign{\hfil +$\displaystyle##$\hfil\cr>\cr\noalign{\vskip1.2pt}=\cr}}} +{\vcenter{\offinterlineskip\halign{\hfil$\textstyle##$\hfil\cr>\cr +\noalign{\vskip1.2pt}=\cr}}} +{\vcenter{\offinterlineskip\halign{\hfil$\scriptstyle##$\hfil\cr>\cr +\noalign{\vskip1pt}=\cr}}} +{\vcenter{\offinterlineskip\halign{\hfil$\scriptscriptstyle##$\hfil\cr +>\cr +\noalign{\vskip0.9pt}=\cr}}}}} +\def\grole{\mathrel{\mathchoice {\vcenter{\offinterlineskip +\halign{\hfil +$\displaystyle##$\hfil\cr>\cr\noalign{\vskip-1pt}<\cr}}} +{\vcenter{\offinterlineskip\halign{\hfil$\textstyle##$\hfil\cr +>\cr\noalign{\vskip-1pt}<\cr}}} +{\vcenter{\offinterlineskip\halign{\hfil$\scriptstyle##$\hfil\cr +>\cr\noalign{\vskip-0.8pt}<\cr}}} +{\vcenter{\offinterlineskip\halign{\hfil$\scriptscriptstyle##$\hfil\cr +>\cr\noalign{\vskip-0.3pt}<\cr}}}}} +\def\bbbr{{\rm I\!R}} %reelle Zahlen +\def\bbbm{{\rm I\!M}} +\def\bbbn{{\rm I\!N}} %natuerliche Zahlen +\def\bbbf{{\rm I\!F}} +\def\bbbh{{\rm I\!H}} +\def\bbbk{{\rm I\!K}} +\def\bbbp{{\rm I\!P}} +\def\bbbone{{\mathchoice {\rm 1\mskip-4mu l} {\rm 1\mskip-4mu l} +{\rm 1\mskip-4.5mu l} {\rm 1\mskip-5mu l}}} +\def\bbbc{{\mathchoice {\setbox0=\hbox{$\displaystyle\rm C$}\hbox{\hbox +to0pt{\kern0.4\wd0\vrule height0.9\ht0\hss}\box0}} +{\setbox0=\hbox{$\textstyle\rm C$}\hbox{\hbox +to0pt{\kern0.4\wd0\vrule height0.9\ht0\hss}\box0}} +{\setbox0=\hbox{$\scriptstyle\rm C$}\hbox{\hbox +to0pt{\kern0.4\wd0\vrule height0.9\ht0\hss}\box0}} +{\setbox0=\hbox{$\scriptscriptstyle\rm C$}\hbox{\hbox +to0pt{\kern0.4\wd0\vrule height0.9\ht0\hss}\box0}}}} +\def\bbbq{{\mathchoice {\setbox0=\hbox{$\displaystyle\rm +Q$}\hbox{\raise +0.15\ht0\hbox to0pt{\kern0.4\wd0\vrule height0.8\ht0\hss}\box0}} +{\setbox0=\hbox{$\textstyle\rm Q$}\hbox{\raise +0.15\ht0\hbox to0pt{\kern0.4\wd0\vrule height0.8\ht0\hss}\box0}} +{\setbox0=\hbox{$\scriptstyle\rm Q$}\hbox{\raise +0.15\ht0\hbox to0pt{\kern0.4\wd0\vrule height0.7\ht0\hss}\box0}} +{\setbox0=\hbox{$\scriptscriptstyle\rm Q$}\hbox{\raise +0.15\ht0\hbox to0pt{\kern0.4\wd0\vrule height0.7\ht0\hss}\box0}}}} +\def\bbbt{{\mathchoice {\setbox0=\hbox{$\displaystyle\rm +T$}\hbox{\hbox to0pt{\kern0.3\wd0\vrule height0.9\ht0\hss}\box0}} +{\setbox0=\hbox{$\textstyle\rm T$}\hbox{\hbox +to0pt{\kern0.3\wd0\vrule height0.9\ht0\hss}\box0}} +{\setbox0=\hbox{$\scriptstyle\rm T$}\hbox{\hbox +to0pt{\kern0.3\wd0\vrule height0.9\ht0\hss}\box0}} +{\setbox0=\hbox{$\scriptscriptstyle\rm T$}\hbox{\hbox +to0pt{\kern0.3\wd0\vrule height0.9\ht0\hss}\box0}}}} +\def\bbbs{{\mathchoice +{\setbox0=\hbox{$\displaystyle \rm S$}\hbox{\raise0.5\ht0\hbox +to0pt{\kern0.35\wd0\vrule height0.45\ht0\hss}\hbox +to0pt{\kern0.55\wd0\vrule height0.5\ht0\hss}\box0}} +{\setbox0=\hbox{$\textstyle \rm S$}\hbox{\raise0.5\ht0\hbox +to0pt{\kern0.35\wd0\vrule height0.45\ht0\hss}\hbox +to0pt{\kern0.55\wd0\vrule height0.5\ht0\hss}\box0}} +{\setbox0=\hbox{$\scriptstyle \rm S$}\hbox{\raise0.5\ht0\hbox +to0pt{\kern0.35\wd0\vrule height0.45\ht0\hss}\raise0.05\ht0\hbox +to0pt{\kern0.5\wd0\vrule height0.45\ht0\hss}\box0}} +{\setbox0=\hbox{$\scriptscriptstyle\rm S$}\hbox{\raise0.5\ht0\hbox +to0pt{\kern0.4\wd0\vrule height0.45\ht0\hss}\raise0.05\ht0\hbox +to0pt{\kern0.55\wd0\vrule height0.45\ht0\hss}\box0}}}} +\def\bbbz{{\mathchoice {\hbox{$\mathsf\textstyle Z\kern-0.4em Z$}} +{\hbox{$\mathsf\textstyle Z\kern-0.4em Z$}} +{\hbox{$\mathsf\scriptstyle Z\kern-0.3em Z$}} +{\hbox{$\mathsf\scriptscriptstyle Z\kern-0.2em Z$}}}} + +\let\ts\, + +\setlength\leftmargini {17\p@} +\setlength\leftmargin {\leftmargini} +\setlength\leftmarginii {\leftmargini} +\setlength\leftmarginiii {\leftmargini} +\setlength\leftmarginiv {\leftmargini} +\setlength \labelsep {.5em} +\setlength \labelwidth{\leftmargini} +\addtolength\labelwidth{-\labelsep} + +\def\@listI{\leftmargin\leftmargini + \parsep 0\p@ \@plus1\p@ \@minus\p@ + \topsep 8\p@ \@plus2\p@ \@minus4\p@ + \itemsep0\p@} +\let\@listi\@listI +\@listi +\def\@listii {\leftmargin\leftmarginii + \labelwidth\leftmarginii + \advance\labelwidth-\labelsep + \topsep 0\p@ \@plus2\p@ \@minus\p@} +\def\@listiii{\leftmargin\leftmarginiii + \labelwidth\leftmarginiii + \advance\labelwidth-\labelsep + \topsep 0\p@ \@plus\p@\@minus\p@ + \parsep \z@ + \partopsep \p@ \@plus\z@ \@minus\p@} + +\renewcommand\labelitemi{\normalfont\bfseries --} +\renewcommand\labelitemii{$\m@th\bullet$} + +\setlength\arraycolsep{1.4\p@} +\setlength\tabcolsep{1.4\p@} + +\def\tableofcontents{\chapter*{\contentsname\@mkboth{{\contentsname}}% + {{\contentsname}}} + \def\authcount##1{\setcounter{auco}{##1}\setcounter{@auth}{1}} + \def\lastand{\ifnum\value{auco}=2\relax + \unskip{} \andname\ + \else + \unskip \lastandname\ + \fi}% + \def\and{\stepcounter{@auth}\relax + \ifnum\value{@auth}=\value{auco}% + \lastand + \else + \unskip, + \fi}% + \@starttoc{toc}\if@restonecol\twocolumn\fi} + +\def\l@part#1#2{\addpenalty{\@secpenalty}% + \addvspace{2em plus\p@}% % space above part line + \begingroup + \parindent \z@ + \rightskip \z@ plus 5em + \hrule\vskip5pt + \large % same size as for a contribution heading + \bfseries\boldmath % set line in boldface + \leavevmode % TeX command to enter horizontal mode. + #1\par + \vskip5pt + \hrule + \vskip1pt + \nobreak % Never break after part entry + \endgroup} + +\def\@dotsep{2} + +\let\phantomsection=\relax + +\def\hyperhrefextend{\ifx\hyper@anchor\@undefined\else +{}\fi} + +\def\addnumcontentsmark#1#2#3{% +\addtocontents{#1}{\protect\contentsline{#2}{\protect\numberline + {\thechapter}#3}{\thepage}\hyperhrefextend}}% +\def\addcontentsmark#1#2#3{% +\addtocontents{#1}{\protect\contentsline{#2}{#3}{\thepage}\hyperhrefextend}}% +\def\addcontentsmarkwop#1#2#3{% +\addtocontents{#1}{\protect\contentsline{#2}{#3}{0}\hyperhrefextend}}% + +\def\@adcmk[#1]{\ifcase #1 \or +\def\@gtempa{\addnumcontentsmark}% + \or \def\@gtempa{\addcontentsmark}% + \or \def\@gtempa{\addcontentsmarkwop}% + \fi\@gtempa{toc}{chapter}% +} +\def\addtocmark{% +\phantomsection +\@ifnextchar[{\@adcmk}{\@adcmk[3]}% +} + +\def\l@chapter#1#2{\addpenalty{-\@highpenalty} + \vskip 1.0em plus 1pt \@tempdima 1.5em \begingroup + \parindent \z@ \rightskip \@tocrmarg + \advance\rightskip by 0pt plus 2cm + \parfillskip -\rightskip \pretolerance=10000 + \leavevmode \advance\leftskip\@tempdima \hskip -\leftskip + {\large\bfseries\boldmath#1}\ifx0#2\hfil\null + \else + \nobreak + \leaders\hbox{$\m@th \mkern \@dotsep mu.\mkern + \@dotsep mu$}\hfill + \nobreak\hbox to\@pnumwidth{\hss #2}% + \fi\par + \penalty\@highpenalty \endgroup} + +\def\l@title#1#2{\addpenalty{-\@highpenalty} + \addvspace{8pt plus 1pt} + \@tempdima \z@ + \begingroup + \parindent \z@ \rightskip \@tocrmarg + \advance\rightskip by 0pt plus 2cm + \parfillskip -\rightskip \pretolerance=10000 + \leavevmode \advance\leftskip\@tempdima \hskip -\leftskip + #1\nobreak + \leaders\hbox{$\m@th \mkern \@dotsep mu.\mkern + \@dotsep mu$}\hfill + \nobreak\hbox to\@pnumwidth{\hss #2}\par + \penalty\@highpenalty \endgroup} + +\def\l@author#1#2{\addpenalty{\@highpenalty} + \@tempdima=15\p@ %\z@ + \begingroup + \parindent \z@ \rightskip \@tocrmarg + \advance\rightskip by 0pt plus 2cm + \pretolerance=10000 + \leavevmode \advance\leftskip\@tempdima %\hskip -\leftskip + \textit{#1}\par + \penalty\@highpenalty \endgroup} + +\setcounter{tocdepth}{0} +\newdimen\tocchpnum +\newdimen\tocsecnum +\newdimen\tocsectotal +\newdimen\tocsubsecnum +\newdimen\tocsubsectotal +\newdimen\tocsubsubsecnum +\newdimen\tocsubsubsectotal +\newdimen\tocparanum +\newdimen\tocparatotal +\newdimen\tocsubparanum +\tocchpnum=\z@ % no chapter numbers +\tocsecnum=15\p@ % section 88. plus 2.222pt +\tocsubsecnum=23\p@ % subsection 88.8 plus 2.222pt +\tocsubsubsecnum=27\p@ % subsubsection 88.8.8 plus 1.444pt +\tocparanum=35\p@ % paragraph 88.8.8.8 plus 1.666pt +\tocsubparanum=43\p@ % subparagraph 88.8.8.8.8 plus 1.888pt +\def\calctocindent{% +\tocsectotal=\tocchpnum +\advance\tocsectotal by\tocsecnum +\tocsubsectotal=\tocsectotal +\advance\tocsubsectotal by\tocsubsecnum +\tocsubsubsectotal=\tocsubsectotal +\advance\tocsubsubsectotal by\tocsubsubsecnum +\tocparatotal=\tocsubsubsectotal +\advance\tocparatotal by\tocparanum} +\calctocindent + +\def\l@section{\@dottedtocline{1}{\tocchpnum}{\tocsecnum}} +\def\l@subsection{\@dottedtocline{2}{\tocsectotal}{\tocsubsecnum}} +\def\l@subsubsection{\@dottedtocline{3}{\tocsubsectotal}{\tocsubsubsecnum}} +\def\l@paragraph{\@dottedtocline{4}{\tocsubsubsectotal}{\tocparanum}} +\def\l@subparagraph{\@dottedtocline{5}{\tocparatotal}{\tocsubparanum}} + +\def\listoffigures{\@restonecolfalse\if@twocolumn\@restonecoltrue\onecolumn + \fi\section*{\listfigurename\@mkboth{{\listfigurename}}{{\listfigurename}}} + \@starttoc{lof}\if@restonecol\twocolumn\fi} +\def\l@figure{\@dottedtocline{1}{0em}{1.5em}} + +\def\listoftables{\@restonecolfalse\if@twocolumn\@restonecoltrue\onecolumn + \fi\section*{\listtablename\@mkboth{{\listtablename}}{{\listtablename}}} + \@starttoc{lot}\if@restonecol\twocolumn\fi} +\let\l@table\l@figure + +\renewcommand\listoffigures{% + \section*{\listfigurename + \@mkboth{\listfigurename}{\listfigurename}}% + \@starttoc{lof}% + } + +\renewcommand\listoftables{% + \section*{\listtablename + \@mkboth{\listtablename}{\listtablename}}% + \@starttoc{lot}% + } + +\ifx\oribibl\undefined +\ifx\citeauthoryear\undefined +\renewenvironment{thebibliography}[1] + {\section*{\refname} + \def\@biblabel##1{##1.} + \small + \list{\@biblabel{\@arabic\c@enumiv}}% + {\settowidth\labelwidth{\@biblabel{#1}}% + \leftmargin\labelwidth + \advance\leftmargin\labelsep + \if@openbib + \advance\leftmargin\bibindent + \itemindent -\bibindent + \listparindent \itemindent + \parsep \z@ + \fi + \usecounter{enumiv}% + \let\p@enumiv\@empty + \renewcommand\theenumiv{\@arabic\c@enumiv}}% + \if@openbib + \renewcommand\newblock{\par}% + \else + \renewcommand\newblock{\hskip .11em \@plus.33em \@minus.07em}% + \fi + \sloppy\clubpenalty4000\widowpenalty4000% + \sfcode`\.=\@m} + {\def\@noitemerr + {\@latex@warning{Empty `thebibliography' environment}}% + \endlist} +\def\@lbibitem[#1]#2{\item[{[#1]}\hfill]\if@filesw + {\let\protect\noexpand\immediate + \write\@auxout{\string\bibcite{#2}{#1}}}\fi\ignorespaces} +\newcount\@tempcntc +\def\@citex[#1]#2{\if@filesw\immediate\write\@auxout{\string\citation{#2}}\fi + \@tempcnta\z@\@tempcntb\m@ne\def\@citea{}\@cite{\@for\@citeb:=#2\do + {\@ifundefined + {b@\@citeb}{\@citeo\@tempcntb\m@ne\@citea\def\@citea{,}{\bfseries + ?}\@warning + {Citation `\@citeb' on page \thepage \space undefined}}% + {\setbox\z@\hbox{\global\@tempcntc0\csname b@\@citeb\endcsname\relax}% + \ifnum\@tempcntc=\z@ \@citeo\@tempcntb\m@ne + \@citea\def\@citea{,}\hbox{\csname b@\@citeb\endcsname}% + \else + \advance\@tempcntb\@ne + \ifnum\@tempcntb=\@tempcntc + \else\advance\@tempcntb\m@ne\@citeo + \@tempcnta\@tempcntc\@tempcntb\@tempcntc\fi\fi}}\@citeo}{#1}} +\def\@citeo{\ifnum\@tempcnta>\@tempcntb\else + \@citea\def\@citea{,\,\hskip\z@skip}% + \ifnum\@tempcnta=\@tempcntb\the\@tempcnta\else + {\advance\@tempcnta\@ne\ifnum\@tempcnta=\@tempcntb \else + \def\@citea{--}\fi + \advance\@tempcnta\m@ne\the\@tempcnta\@citea\the\@tempcntb}\fi\fi} +\else +\renewenvironment{thebibliography}[1] + {\section*{\refname} + \small + \list{}% + {\settowidth\labelwidth{}% + \leftmargin\parindent + \itemindent=-\parindent + \labelsep=\z@ + \if@openbib + \advance\leftmargin\bibindent + \itemindent -\bibindent + \listparindent \itemindent + \parsep \z@ + \fi + \usecounter{enumiv}% + \let\p@enumiv\@empty + \renewcommand\theenumiv{}}% + \if@openbib + \renewcommand\newblock{\par}% + \else + \renewcommand\newblock{\hskip .11em \@plus.33em \@minus.07em}% + \fi + \sloppy\clubpenalty4000\widowpenalty4000% + \sfcode`\.=\@m} + {\def\@noitemerr + {\@latex@warning{Empty `thebibliography' environment}}% + \endlist} + \def\@cite#1{#1}% + \def\@lbibitem[#1]#2{\item[]\if@filesw + {\def\protect##1{\string ##1\space}\immediate + \write\@auxout{\string\bibcite{#2}{#1}}}\fi\ignorespaces} + \fi +\else +\@cons\@openbib@code{\noexpand\small} +\fi + +\def\idxquad{\hskip 10\p@}% space that divides entry from number + +\def\@idxitem{\par\hangindent 10\p@} + +\def\subitem{\par\setbox0=\hbox{--\enspace}% second order + \noindent\hangindent\wd0\box0}% index entry + +\def\subsubitem{\par\setbox0=\hbox{--\,--\enspace}% third + \noindent\hangindent\wd0\box0}% order index entry + +\def\indexspace{\par \vskip 10\p@ plus5\p@ minus3\p@\relax} + +\renewenvironment{theindex} + {\@mkboth{\indexname}{\indexname}% + \thispagestyle{empty}\parindent\z@ + \parskip\z@ \@plus .3\p@\relax + \let\item\par + \def\,{\relax\ifmmode\mskip\thinmuskip + \else\hskip0.2em\ignorespaces\fi}% + \normalfont\small + \begin{multicols}{2}[\@makeschapterhead{\indexname}]% + } + {\end{multicols}} + +\renewcommand\footnoterule{% + \kern-3\p@ + \hrule\@width 2truecm + \kern2.6\p@} + \newdimen\fnindent + \fnindent1em +\long\def\@makefntext#1{% + \parindent \fnindent% + \leftskip \fnindent% + \noindent + \llap{\hb@xt@1em{\hss\@makefnmark\ }}\ignorespaces#1} + +\long\def\@makecaption#1#2{% + \small + \vskip\abovecaptionskip + \sbox\@tempboxa{{\bfseries #1.} #2}% + \ifdim \wd\@tempboxa >\hsize + {\bfseries #1.} #2\par + \else + \global \@minipagefalse + \hb@xt@\hsize{\hfil\box\@tempboxa\hfil}% + \fi + \vskip\belowcaptionskip} + +\def\fps@figure{htbp} +\def\fnum@figure{\figurename\thinspace\thefigure} +\def \@floatboxreset {% + \reset@font + \small + \@setnobreak + \@setminipage +} +\def\fps@table{htbp} +\def\fnum@table{\tablename~\thetable} +\renewenvironment{table} + {\setlength\abovecaptionskip{0\p@}% + \setlength\belowcaptionskip{10\p@}% + \@float{table}} + {\end@float} +\renewenvironment{table*} + {\setlength\abovecaptionskip{0\p@}% + \setlength\belowcaptionskip{10\p@}% + \@dblfloat{table}} + {\end@dblfloat} + +\long\def\@caption#1[#2]#3{\par\addcontentsline{\csname + ext@#1\endcsname}{#1}{\protect\numberline{\csname + the#1\endcsname}{\ignorespaces #2}}\begingroup + \@parboxrestore + \@makecaption{\csname fnum@#1\endcsname}{\ignorespaces #3}\par + \endgroup} + +% LaTeX does not provide a command to enter the authors institute +% addresses. The \institute command is defined here. + +\newcounter{@inst} +\newcounter{@auth} +\newcounter{auco} +\newdimen\instindent +\newbox\authrun +\newtoks\authorrunning +\newtoks\tocauthor +\newbox\titrun +\newtoks\titlerunning +\newtoks\toctitle + +\def\clearheadinfo{\gdef\@author{No Author Given}% + \gdef\@title{No Title Given}% + \gdef\@subtitle{}% + \gdef\@institute{No Institute Given}% + \gdef\@thanks{}% + \global\titlerunning={}\global\authorrunning={}% + \global\toctitle={}\global\tocauthor={}} + +\def\institute#1{\gdef\@institute{#1}} + +\def\institutename{\par + \begingroup + \parskip=\z@ + \parindent=\z@ + \setcounter{@inst}{1}% + \def\and{\par\stepcounter{@inst}% + \noindent$^{\the@inst}$\enspace\ignorespaces}% + \setbox0=\vbox{\def\thanks##1{}\@institute}% + \ifnum\c@@inst=1\relax + \gdef\fnnstart{0}% + \else + \xdef\fnnstart{\c@@inst}% + \setcounter{@inst}{1}% + \noindent$^{\the@inst}$\enspace + \fi + \ignorespaces + \@institute\par + \endgroup} + +\def\@fnsymbol#1{\ensuremath{\ifcase#1\or\star\or{\star\star}\or + {\star\star\star}\or \dagger\or \ddagger\or + \mathchar "278\or \mathchar "27B\or \|\or **\or \dagger\dagger + \or \ddagger\ddagger \else\@ctrerr\fi}} + +\def\inst#1{\unskip$^{#1}$} +\def\orcidID#1{\unskip$^{[#1]}$} % added MR 2018-03-10 +\def\fnmsep{\unskip$^,$} +\def\email#1{{\tt#1}} + +\AtBeginDocument{\@ifundefined{url}{\def\url#1{#1}}{}% +\@ifpackageloaded{babel}{% +\@ifundefined{extrasenglish}{}{\addto\extrasenglish{\switcht@albion}}% +\@ifundefined{extrasfrenchb}{}{\addto\extrasfrenchb{\switcht@francais}}% +\@ifundefined{extrasgerman}{}{\addto\extrasgerman{\switcht@deutsch}}% +\@ifundefined{extrasngerman}{}{\addto\extrasngerman{\switcht@deutsch}}% +}{\switcht@@therlang}% +\providecommand{\keywords}[1]{\def\and{{\textperiodcentered} }% +\par\addvspace\baselineskip +\noindent\keywordname\enspace\ignorespaces#1}% +\@ifpackageloaded{hyperref}{% +\def\doi#1{\href{https://doi.org/#1}{https://doi.org/#1}}}{ +\def\doi#1{https://doi.org/#1}} +} +\def\homedir{\~{ }} + +\def\subtitle#1{\gdef\@subtitle{#1}} +\clearheadinfo +% +%%% to avoid hyperref warnings +\providecommand*{\toclevel@author}{999} +%%% to make title-entry parent of section-entries +\providecommand*{\toclevel@title}{0} +% +\renewcommand\maketitle{\newpage +\phantomsection + \refstepcounter{chapter}% + \stepcounter{section}% + \setcounter{section}{0}% + \setcounter{subsection}{0}% + \setcounter{figure}{0} + \setcounter{table}{0} + \setcounter{equation}{0} + \setcounter{footnote}{0}% + \begingroup + \parindent=\z@ + \renewcommand\thefootnote{\@fnsymbol\c@footnote}% + \if@twocolumn + \ifnum \col@number=\@ne + \@maketitle + \else + \twocolumn[\@maketitle]% + \fi + \else + \newpage + \global\@topnum\z@ % Prevents figures from going at top of page. + \@maketitle + \fi + \thispagestyle{empty}\@thanks +% + \def\\{\unskip\ \ignorespaces}\def\inst##1{\unskip{}}% + \def\thanks##1{\unskip{}}\def\fnmsep{\unskip}% + \instindent=\hsize + \advance\instindent by-\headlineindent + \if!\the\toctitle!\addcontentsline{toc}{title}{\@title}\else + \addcontentsline{toc}{title}{\the\toctitle}\fi + \if@runhead + \if!\the\titlerunning!\else + \edef\@title{\the\titlerunning}% + \fi + \global\setbox\titrun=\hbox{\small\rm\unboldmath\ignorespaces\@title}% + \ifdim\wd\titrun>\instindent + \typeout{Title too long for running head. Please supply}% + \typeout{a shorter form with \string\titlerunning\space prior to + \string\maketitle}% + \global\setbox\titrun=\hbox{\small\rm + Title Suppressed Due to Excessive Length}% + \fi + \xdef\@title{\copy\titrun}% + \fi +% + \if!\the\tocauthor!\relax + {\def\and{\noexpand\protect\noexpand\and}% + \def\inst##1{}% added MR 2017-09-20 to remove inst numbers from the TOC + \def\orcidID##1{}% added MR 2017-09-20 to remove ORCID ids from the TOC + \protected@xdef\toc@uthor{\@author}}% + \else + \def\\{\noexpand\protect\noexpand\newline}% + \protected@xdef\scratch{\the\tocauthor}% + \protected@xdef\toc@uthor{\scratch}% + \fi + \addtocontents{toc}{\noexpand\protect\noexpand\authcount{\the\c@auco}}% + \addcontentsline{toc}{author}{\toc@uthor}% + \if@runhead + \if!\the\authorrunning! + \value{@inst}=\value{@auth}% + \setcounter{@auth}{1}% + \else + \edef\@author{\the\authorrunning}% + \fi + \global\setbox\authrun=\hbox{\def\inst##1{}% added MR 2017-09-20 to remove inst numbers from the runninghead + \def\orcidID##1{}% added MR 2017-09-20 to remove ORCID ids from the runninghead + \small\unboldmath\@author\unskip}% + \ifdim\wd\authrun>\instindent + \typeout{Names of authors too long for running head. Please supply}% + \typeout{a shorter form with \string\authorrunning\space prior to + \string\maketitle}% + \global\setbox\authrun=\hbox{\small\rm + Authors Suppressed Due to Excessive Length}% + \fi + \xdef\@author{\copy\authrun}% + \markboth{\@author}{\@title}% + \fi + \endgroup + \setcounter{footnote}{\fnnstart}% + \clearheadinfo} +% +\def\@maketitle{\newpage + \markboth{}{}% + \def\lastand{\ifnum\value{@inst}=2\relax + \unskip{} \andname\ + \else + \unskip \lastandname\ + \fi}% + \def\and{\stepcounter{@auth}\relax + \ifnum\value{@auth}=\value{@inst}% + \lastand + \else + \unskip, + \fi}% + \begin{center}% + \let\newline\\ + {\Large \bfseries\boldmath + \pretolerance=10000 + \@title \par}\vskip .8cm +\if!\@subtitle!\else {\large \bfseries\boldmath + \vskip -.65cm + \pretolerance=10000 + \@subtitle \par}\vskip .8cm\fi + \setbox0=\vbox{\setcounter{@auth}{1}\def\and{\stepcounter{@auth}}% + \def\thanks##1{}\@author}% + \global\value{@inst}=\value{@auth}% + \global\value{auco}=\value{@auth}% + \setcounter{@auth}{1}% +{\lineskip .5em +\noindent\ignorespaces +\@author\vskip.35cm} + {\small\institutename} + \end{center}% + } + +% definition of the "\spnewtheorem" command. +% +% Usage: +% +% \spnewtheorem{env_nam}{caption}[within]{cap_font}{body_font} +% or \spnewtheorem{env_nam}[numbered_like]{caption}{cap_font}{body_font} +% or \spnewtheorem*{env_nam}{caption}{cap_font}{body_font} +% +% New is "cap_font" and "body_font". It stands for +% fontdefinition of the caption and the text itself. +% +% "\spnewtheorem*" gives a theorem without number. +% +% A defined spnewthoerem environment is used as described +% by Lamport. +% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +\def\@thmcountersep{} +\def\@thmcounterend{.} + +\def\spnewtheorem{\@ifstar{\@sthm}{\@Sthm}} + +% definition of \spnewtheorem with number + +\def\@spnthm#1#2{% + \@ifnextchar[{\@spxnthm{#1}{#2}}{\@spynthm{#1}{#2}}} +\def\@Sthm#1{\@ifnextchar[{\@spothm{#1}}{\@spnthm{#1}}} + +\def\@spxnthm#1#2[#3]#4#5{\expandafter\@ifdefinable\csname #1\endcsname + {\@definecounter{#1}\@addtoreset{#1}{#3}% + \expandafter\xdef\csname the#1\endcsname{\expandafter\noexpand + \csname the#3\endcsname \noexpand\@thmcountersep \@thmcounter{#1}}% + \expandafter\xdef\csname #1name\endcsname{#2}% + \global\@namedef{#1}{\@spthm{#1}{\csname #1name\endcsname}{#4}{#5}}% + \global\@namedef{end#1}{\@endtheorem}}} + +\def\@spynthm#1#2#3#4{\expandafter\@ifdefinable\csname #1\endcsname + {\@definecounter{#1}% + \expandafter\xdef\csname the#1\endcsname{\@thmcounter{#1}}% + \expandafter\xdef\csname #1name\endcsname{#2}% + \global\@namedef{#1}{\@spthm{#1}{\csname #1name\endcsname}{#3}{#4}}% + \global\@namedef{end#1}{\@endtheorem}}} + +\def\@spothm#1[#2]#3#4#5{% + \@ifundefined{c@#2}{\@latexerr{No theorem environment `#2' defined}\@eha}% + {\expandafter\@ifdefinable\csname #1\endcsname + {\newaliascnt{#1}{#2}% + \expandafter\xdef\csname #1name\endcsname{#3}% + \global\@namedef{#1}{\@spthm{#1}{\csname #1name\endcsname}{#4}{#5}}% + \global\@namedef{end#1}{\@endtheorem}}}} + +\def\@spthm#1#2#3#4{\topsep 7\p@ \@plus2\p@ \@minus4\p@ +\refstepcounter{#1}% +\@ifnextchar[{\@spythm{#1}{#2}{#3}{#4}}{\@spxthm{#1}{#2}{#3}{#4}}} + +\def\@spxthm#1#2#3#4{\@spbegintheorem{#2}{\csname the#1\endcsname}{#3}{#4}% + \ignorespaces} + +\def\@spythm#1#2#3#4[#5]{\@spopargbegintheorem{#2}{\csname + the#1\endcsname}{#5}{#3}{#4}\ignorespaces} + +\def\@spbegintheorem#1#2#3#4{\trivlist + \item[\hskip\labelsep{#3#1\ #2\@thmcounterend}]#4} + +\def\@spopargbegintheorem#1#2#3#4#5{\trivlist + \item[\hskip\labelsep{#4#1\ #2}]{#4(#3)\@thmcounterend\ }#5} + +% definition of \spnewtheorem* without number + +\def\@sthm#1#2{\@Ynthm{#1}{#2}} + +\def\@Ynthm#1#2#3#4{\expandafter\@ifdefinable\csname #1\endcsname + {\global\@namedef{#1}{\@Thm{\csname #1name\endcsname}{#3}{#4}}% + \expandafter\xdef\csname #1name\endcsname{#2}% + \global\@namedef{end#1}{\@endtheorem}}} + +\def\@Thm#1#2#3{\topsep 7\p@ \@plus2\p@ \@minus4\p@ +\@ifnextchar[{\@Ythm{#1}{#2}{#3}}{\@Xthm{#1}{#2}{#3}}} + +\def\@Xthm#1#2#3{\@Begintheorem{#1}{#2}{#3}\ignorespaces} + +\def\@Ythm#1#2#3[#4]{\@Opargbegintheorem{#1} + {#4}{#2}{#3}\ignorespaces} + +\def\@Begintheorem#1#2#3{#3\trivlist + \item[\hskip\labelsep{#2#1\@thmcounterend}]} + +\def\@Opargbegintheorem#1#2#3#4{#4\trivlist + \item[\hskip\labelsep{#3#1}]{#3(#2)\@thmcounterend\ }} + +\if@envcntsect + \def\@thmcountersep{.} + \spnewtheorem{theorem}{Theorem}[section]{\bfseries}{\itshape} +\else + \spnewtheorem{theorem}{Theorem}{\bfseries}{\itshape} + \if@envcntreset + \@addtoreset{theorem}{section} + \else + \@addtoreset{theorem}{chapter} + \fi +\fi + +%definition of divers theorem environments +\spnewtheorem*{claim}{Claim}{\itshape}{\rmfamily} +\spnewtheorem*{proof}{Proof}{\itshape}{\rmfamily} +\if@envcntsame % alle Umgebungen wie Theorem. + \def\spn@wtheorem#1#2#3#4{\@spothm{#1}[theorem]{#2}{#3}{#4}} +\else % alle Umgebungen mit eigenem Zaehler + \if@envcntsect % mit section numeriert + \def\spn@wtheorem#1#2#3#4{\@spxnthm{#1}{#2}[section]{#3}{#4}} + \else % nicht mit section numeriert + \if@envcntreset + \def\spn@wtheorem#1#2#3#4{\@spynthm{#1}{#2}{#3}{#4} + \@addtoreset{#1}{section}} + \else + \def\spn@wtheorem#1#2#3#4{\@spynthm{#1}{#2}{#3}{#4} + \@addtoreset{#1}{chapter}}% + \fi + \fi +\fi +\spn@wtheorem{case}{Case}{\itshape}{\rmfamily} +\spn@wtheorem{conjecture}{Conjecture}{\itshape}{\rmfamily} +\spn@wtheorem{corollary}{Corollary}{\bfseries}{\itshape} +\spn@wtheorem{definition}{Definition}{\bfseries}{\itshape} +\spn@wtheorem{example}{Example}{\itshape}{\rmfamily} +\spn@wtheorem{exercise}{Exercise}{\itshape}{\rmfamily} +\spn@wtheorem{lemma}{Lemma}{\bfseries}{\itshape} +\spn@wtheorem{note}{Note}{\itshape}{\rmfamily} +\spn@wtheorem{problem}{Problem}{\itshape}{\rmfamily} +\spn@wtheorem{property}{Property}{\itshape}{\rmfamily} +\spn@wtheorem{proposition}{Proposition}{\bfseries}{\itshape} +\spn@wtheorem{question}{Question}{\itshape}{\rmfamily} +\spn@wtheorem{solution}{Solution}{\itshape}{\rmfamily} +\spn@wtheorem{remark}{Remark}{\itshape}{\rmfamily} + +\def\@takefromreset#1#2{% + \def\@tempa{#1}% + \let\@tempd\@elt + \def\@elt##1{% + \def\@tempb{##1}% + \ifx\@tempa\@tempb\else + \@addtoreset{##1}{#2}% + \fi}% + \expandafter\expandafter\let\expandafter\@tempc\csname cl@#2\endcsname + \expandafter\def\csname cl@#2\endcsname{}% + \@tempc + \let\@elt\@tempd} + +\def\theopargself{\def\@spopargbegintheorem##1##2##3##4##5{\trivlist + \item[\hskip\labelsep{##4##1\ ##2}]{##4##3\@thmcounterend\ }##5} + \def\@Opargbegintheorem##1##2##3##4{##4\trivlist + \item[\hskip\labelsep{##3##1}]{##3##2\@thmcounterend\ }} + } + +\renewenvironment{abstract}{% + \list{}{\advance\topsep by0.35cm\relax\small + \leftmargin=1cm + \labelwidth=\z@ + \listparindent=\z@ + \itemindent\listparindent + \rightmargin\leftmargin}\item[\hskip\labelsep + \bfseries\abstractname]} + {\endlist} + +\newdimen\headlineindent % dimension for space between +\headlineindent=1.166cm % number and text of headings. + +\def\ps@headings{\let\@mkboth\@gobbletwo + \let\@oddfoot\@empty\let\@evenfoot\@empty + \def\@evenhead{\normalfont\small\rlap{\thepage}\hspace{\headlineindent}% + \leftmark\hfil} + \def\@oddhead{\normalfont\small\hfil\rightmark\hspace{\headlineindent}% + \llap{\thepage}} + \def\chaptermark##1{}% + \def\sectionmark##1{}% + \def\subsectionmark##1{}} + +\def\ps@titlepage{\let\@mkboth\@gobbletwo + \let\@oddfoot\@empty\let\@evenfoot\@empty + \def\@evenhead{\normalfont\small\rlap{\thepage}\hspace{\headlineindent}% + \hfil} + \def\@oddhead{\normalfont\small\hfil\hspace{\headlineindent}% + \llap{\thepage}} + \def\chaptermark##1{}% + \def\sectionmark##1{}% + \def\subsectionmark##1{}} + +\if@runhead\ps@headings\else +\ps@empty\fi + +\setlength\arraycolsep{1.4\p@} +\setlength\tabcolsep{1.4\p@} + +\endinput +%end of file llncs.cls diff --git a/references/2019.arxiv.ho/source/ms.bbl b/references/2019.arxiv.ho/source/ms.bbl new file mode 100644 index 0000000000000000000000000000000000000000..e22b7d1085f33e79b11241561157e92192965ba0 --- /dev/null +++ b/references/2019.arxiv.ho/source/ms.bbl @@ -0,0 +1,131 @@ +\begin{thebibliography}{10} +\providecommand{\url}[1]{\texttt{#1}} +\providecommand{\urlprefix}{URL } +\providecommand{\doi}[1]{https://doi.org/#1} + +\bibitem{PlabanKumarBhowmick} +{Bhowmick}, P.K., {Basu}, A., {Mitra}, P.: {An Agreement Measure for + Determining Inter-Annotator Reliability of Human Judgements on Affective + Tex}. In: {Proceedings of the workshop on Human Judgements in Computational + Linguistics}. pp. 58--65. COLING 2008, Manchester, United Kingdom (2008) + +\bibitem{Jointstockcompany} +company, J.S.: {The habit of using social networks of Vietnamese people 2018}. + brands vietnam, Ho Chi Minh City, Vietnam (2018) + +\bibitem{Ekman1993} +{Ekman}, P.: In: {Facial expression and emotion}. vol.~48, pp. 384--392. + {American Psychologist} (1993) + +\bibitem{Ekman} +{Ekman}, P.: In: {Emotions revealed: Recognizing faces and feelings to improve + communication and emotional life}. p.~2007. {Macmillan} (2012) + +\bibitem{PaulEkman} +{Ekman}, P., {Ekman}, E., {Lama}, D.: In: {The Ekmans' Atlas of Emotion} (2018) + +\bibitem{Kim} +{Kim}, Y.: {Convolutional Neural Networks for Sentence Classifications}. In: + {Proceedings of the 2014 Conference on Empirical Methods in Natural Language + Processing (EMNLP)}. pp. 1746--1751. { Association for Computational + Linguistics}, Doha, Qatar (2014) + +\bibitem{Kiritchenko} +{Kiritchenko}, S., {Mohammad}, S.: {Using Hashtags to Capture Fine Emotion + Categories from Tweets}. In: {Computational Intelligence}. pp. 301--326 + (2015) + +\bibitem{RomanKlinger} +{Klinger}, R., {Clerc}, O.D., {Mohammad}, S.M., {Balahur}, A.: {IEST:WASSA-2018 + Implicit Emotions Shared Task}. pp. 31--42. 2017 AFNLP, Brussels, Belgium + (2018) + +\bibitem{BernhardKratzwald} +{Kratzwald}, B., {Ilic}, S., {Kraus}, M., S.~{Feuerriegel}, H.P.: {Decision + support with text-based emotion recognition: Deep learning for affective + computing}. pp. 24 -- 35. {Decision Support Systems} (2018) + +\bibitem{SaifMohammad2017} +{Mohammad}, S., {Bravo-Marquez}, F.: {Emotion Intensities in Tweets}. In: + {Proceedings of the Sixth Joint Conference on Lexical and Computational + Semantics (*SEM)}. pp. 65--77. Association for Computational Linguistics, + Vancouver, Canada (2017) + +\bibitem{Mohammad} +{Mohammad}, S.M.: {\#Emotional Tweets}. In: {First Joint Conference on Lexical + and Computational Semantics (*SEM)}. pp. 246--255. {Association for + Computational Linguistics}, Montreal, Canada (2012) + +\bibitem{Mohammad2018} +{Mohammad}, S.M., {Bravo-Marquez}, F., {Salameh}, M., {Kiritchenko}, S.: + {SemEval-2018 task 1: Affect in tweets}. pp. 1--17. Proceedings of + International Workshop on Semantic Evaluation, New Orleans, Louisiana (2018) + +\bibitem{SaifMohammad} +{Mohammad}, S.M., {Xiaodan}, Z., {Kiritchenko}, S., {Martin}, J.: {Sentiment, + emotion, purpose, and style in electoral tweets}. pp. 480--499. Information + Processing and Management: an International Journal (2015) + +\bibitem{Nguyen} +Nguyen: {Vietnam has the 7th largest number of Facebook users in the world}. + Dan Tri newspaper (2018) + +\bibitem{VLSPX} +{Nguyen}, H.T.M., {Nguyen}, H.V., {Ngo}, Q.T., {Vu}, L.X., {Tran}, V.M., {Ngo}, + B.X., {Le}, C.A.: {VLSP Shared Task: Sentiment Analysis}. In: {Journal of + Computer Science and Cybernetics}. pp. 295--310 (2018) + +\bibitem{KietVanNguyen} +{Nguyen}, K.V., {Nguyen}, V.D., {Nguyen}, P., {Truong}, T., {Nguyen}, N.L.T.: + {UIT-VSFC: Vietnamese Students’ Feedback Corpus for Sentiment Analysis}. + In: {2018 10th International Conference on Knowledge and Systems Engineering + (KSE)}. pp. 19--24. {IEEE}, Ho Chi Minh City, Vietnam (2018) + +\bibitem{PhuNguyen} +{Nguyen}, P.X.V., {Truong}, T.T.H., {Nguyen}, K.V., {Nguyen}, N.L.T.: {Deep + Learning versus Traditional Classifiers on Vietnamese Students' Feedback + Corpus}. In: {2018 5th NAFOSTED Conference on Information and Computer + Science (NICS)}. pp. 75--80. Ho Chi Minh City, Vietnam (2018) + +\bibitem{VuDucNguyen} +{Nguyen}, V.D., {Nguyen}, K.V., {Nguyen}, N.L.T.: {Variants of Long Short-Term + Memory for Sentiment Analysis on Vietnamese Students’ Feedback Corpus}. In: + {2018 10th International Conference on Knowledge and Systems Engineering + (KSE)}. pp. 306--311. IEEE, Ho Chi Minh City, Vietnam (2018) + +\bibitem{AurelienGeron} +Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, + O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., + Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: + Scikit-learn: Machine learning in python. Journal of Machine Learning + Research \textbf{12}, 2825--2830 (2011) + +\bibitem{CarloStrapparava} +{Strapparava}, C., {Mihalcea}, R.: {SemEval-2007 Task 14: Affective Text}. In: + {Proceedings of the 4th International Workshop on Semantic Evaluations + (SemEval-2007)}. pp. 70--74. { Association for Computational Linguistics}, + Prague (2007) + +\bibitem{TingweiWang} +{T. {Wang} and X. {Yang} and C. {Ouyang}}: {A Multi-emotion Classification + Method Based on BLSTM-MC in Code-Switching Text: 7th CCF International + Conference, NLPCC 2018, Hohhot, China, August 26–30, 2018, Proceedings, + Part II.} pp. 190--199. Natural Language Processing and Chinese Computing + (2018) + +\bibitem{ZhongqingWang} +{Wang}, Z., {Li}, S.: {Overview of NLPCC 2018 Shared Task 1: Emotion Detection + in Code-Switching Text: 7th CCF International Conference, NLPCC 2018, Hohhot, + China, August 26–30, 2018, Proceedings, Part II}. pp. 429--433. Natural + Language Processing and Chinese Computing (2018) + +\bibitem{Facial2007} +{Zhang}, S., {Wu}, Z., {Meng}, H., {Cai}, L.: Facial expression synthesis using + pad emotional parameters for a chinese expressive avatar. vol.~4738, pp. + 24--35 (09 2007) + +\bibitem{YingjieZhang} +{Zhang}, Y., {Wallace}, B.C.: {A Sensitivity Analysis of (and Practitioners’ + Guide to Convolutional}. pp. 253--263. 2017 AFNLP, Taipei, Taiwan (2017) + +\end{thebibliography} diff --git a/references/2019.arxiv.ho/source/ms.tex b/references/2019.arxiv.ho/source/ms.tex new file mode 100644 index 0000000000000000000000000000000000000000..f393fe627e3a9af70c45cb094351f81eb9488b7b --- /dev/null +++ b/references/2019.arxiv.ho/source/ms.tex @@ -0,0 +1,239 @@ +\documentclass[runningheads]{llncs} +\usepackage{graphicx} +\usepackage{marvosym} +\usepackage{amsmath,amssymb,amsfonts} +\usepackage{algorithmic} +\usepackage{graphicx} +\usepackage{textcomp} +\usepackage[T5]{fontenc} +\usepackage[utf8]{inputenc} +\usepackage[vietnamese,english]{babel} +\usepackage{pifont} +\usepackage{float} +\usepackage{caption} +\usepackage{placeins} +\usepackage{array} +\newcolumntype{P}[1]{>{\centering\arraybackslash}p{#1}} +\newcolumntype{M}[1]{>{\centering\arraybackslash}m{#1}} +\usepackage{multirow} +\usepackage{hyperref} +\usepackage{amsmath} +\hypersetup{colorlinks, citecolor=blue, linkcolor=blue, urlcolor=blue} + +\begin{document} +% \title{Emotion Recognition for Vietnamese Social Media Text} +\title{Emotion Recognition\\for Vietnamese Social Media Text} + +\titlerunning{Emotion Recognition for Vietnamese Social Media Text} + +\author{Vong Anh Ho\inst{1,4}\textsuperscript{(\Letter)} \and +Duong Huynh-Cong Nguyen\inst{1,4} \and +Danh Hoang Nguyen\inst{1,4} \and +\\Linh Thi-Van Pham\inst{2,4} \and +Duc-Vu Nguyen\inst{3,4} \and +\\Kiet Van Nguyen\inst{1,4} \and +Ngan Luu-Thuy Nguyen\inst{1,4}} + +\authorrunning{Vong Anh Ho et al.} + +\institute{University of Information Technology, VNU-HCM, Vietnam\\ +\email{\{15521025,15520148,15520090\}@gm.uit.edu.vn, \{kietnv,ngannlt\}@uit.edu.vn}\\ +\and +University of Social Sciences and Humanities, VNU-HCM, Vietnam\\ +\email{vanlinhpham888@gmail.com}\\ +\and +Multimedia Communications Laboratory, University of Information Technology, VNU-HCM, Vietnam\\ +\email{vund@uit.edu.vn}\\ +\and +Vietnam National University, Ho Chi Minh City, Vietnam} +\maketitle + +\begin{abstract} + +Emotion recognition or emotion prediction is a higher approach or a special case of sentiment analysis. In this task, the result is not produced in terms of either polarity: positive or negative or in the form of rating (from 1 to 5) but of a more detailed level of analysis in which the results are depicted in more expressions like sadness, enjoyment, anger, disgust, fear and surprise. Emotion recognition plays a critical role in measuring brand value of a product by recognizing specific emotions of customers' comments. In this study, we have achieved two targets. First and foremost, we built a standard \textbf{V}ietnamese \textbf{S}ocial \textbf{M}edia \textbf{E}motion \textbf{C}orpus (UIT-VSMEC) with exactly 6,927 emotion-annotated sentences, contributing to emotion recognition research in Vietnamese which is a low-resource language in natural language processing (NLP). Secondly, we assessed and measured machine learning and deep neural network models on our UIT-VSMEC corpus. As a result, the CNN model achieved the highest performance with the weighted F1-score of 59.74\%. Our corpus is available at our research website \footnote[1]{\url{ https://sites.google.com/uit.edu.vn/uit-nlp/corpora-projects}}. + +\keywords{Emotion Recognition \and Emotion Prediction \and Vietnamese \and Machine Learning \and Deep Learning \and CNN \and LSTM \and SVM.} +\end{abstract} + +\input{sections/1-introduction.tex} +\input{sections/2-relatedwork.tex} +\input{sections/3-corpus.tex} +\input{sections/4-method.tex} +\input{sections/5-experiments.tex} +\input{sections/6-conclusion.tex} + +\section*{Acknowledgment} +We would like to give our thanks to the NLP@UIT research group and the Citynow-UIT Laboratory of the University of Information Technology - Vietnam National University Ho Chi Minh City for their supports with pragmatic and inspiring advice. + +\bibliographystyle{splncs04} +% \bibliography{bibliography} +\begin{thebibliography}{10} +\providecommand{\url}[1]{\texttt{#1}} +\providecommand{\urlprefix}{URL } +\providecommand{\doi}[1]{https://doi.org/#1} + +\bibitem{PlabanKumarBhowmick} +{Bhowmick}, P.K., {Basu}, A., {Mitra}, P.: {An Agreement Measure for + Determining Inter-Annotator Reliability of Human Judgements on Affective + Tex}. In: {Proceedings of the workshop on Human Judgements in Computational + Linguistics}. pp. 58--65. COLING 2008, Manchester, United Kingdom (2008) + +\bibitem{Jointstockcompany} +company, J.S.: {The habit of using social networks of Vietnamese people 2018}. + brands vietnam, Ho Chi Minh City, Vietnam (2018) + +\bibitem{Ekman1993} +{Ekman}, P.: In: {Facial expression and emotion}. vol.~48, pp. 384--392. + {American Psychologist} (1993) + +\bibitem{Ekman} +{Ekman}, P.: In: {Emotions revealed: Recognizing faces and feelings to improve + communication and emotional life}. p.~2007. {Macmillan} (2012) + +\bibitem{PaulEkman} +{Ekman}, P., {Ekman}, E., {Lama}, D.: In: {The Ekmans' Atlas of Emotion} (2018) + +\bibitem{Kim} +{Kim}, Y.: {Convolutional Neural Networks for Sentence Classifications}. In: + {Proceedings of the 2014 Conference on Empirical Methods in Natural Language + Processing (EMNLP)}. pp. 1746--1751. { Association for Computational + Linguistics}, Doha, Qatar (2014) + +\bibitem{Kiritchenko} +{Kiritchenko}, S., {Mohammad}, S.: {Using Hashtags to Capture Fine Emotion + Categories from Tweets}. In: {Computational Intelligence}. pp. 301--326 + (2015) + +\bibitem{RomanKlinger} +{Klinger}, R., {Clerc}, O.D., {Mohammad}, S.M., {Balahur}, A.: {IEST:WASSA-2018 + Implicit Emotions Shared Task}. pp. 31--42. 2017 AFNLP, Brussels, Belgium + (2018) + +\bibitem{BernhardKratzwald} +{Kratzwald}, B., {Ilic}, S., {Kraus}, M., S.~{Feuerriegel}, H.P.: {Decision + support with text-based emotion recognition: Deep learning for affective + computing}. pp. 24 -- 35. {Decision Support Systems} (2018) + +\bibitem{SaifMohammad2017} +{Mohammad}, S., {Bravo-Marquez}, F.: {Emotion Intensities in Tweets}. In: + {Proceedings of the Sixth Joint Conference on Lexical and Computational + Semantics (*SEM)}. pp. 65--77. Association for Computational Linguistics, + Vancouver, Canada (2017) + +\bibitem{Mohammad} +{Mohammad}, S.M.: {\#Emotional Tweets}. In: {First Joint Conference on Lexical + and Computational Semantics (*SEM)}. pp. 246--255. {Association for + Computational Linguistics}, Montreal, Canada (2012) + +\bibitem{Mohammad2018} +{Mohammad}, S.M., {Bravo-Marquez}, F., {Salameh}, M., {Kiritchenko}, S.: + {SemEval-2018 task 1: Affect in tweets}. pp. 1--17. Proceedings of + International Workshop on Semantic Evaluation, New Orleans, Louisiana (2018) + +\bibitem{SaifMohammad} +{Mohammad}, S.M., {Xiaodan}, Z., {Kiritchenko}, S., {Martin}, J.: {Sentiment, + emotion, purpose, and style in electoral tweets}. pp. 480--499. Information + Processing and Management: an International Journal (2015) + +\bibitem{Nguyen} +Nguyen: {Vietnam has the 7th largest number of Facebook users in the world}. + Dan Tri newspaper (2018) + +\bibitem{VLSPX} +{Nguyen}, H.T.M., {Nguyen}, H.V., {Ngo}, Q.T., {Vu}, L.X., {Tran}, V.M., {Ngo}, + B.X., {Le}, C.A.: {VLSP Shared Task: Sentiment Analysis}. In: {Journal of + Computer Science and Cybernetics}. pp. 295--310 (2018) + + +\bibitem{KietVanNguyen} +{Nguyen}, K.V., {Nguyen}, V.D., {Nguyen}, P., {Truong}, T., {Nguyen}, N.L.T.: + {UIT-VSFC: Vietnamese Students’ Feedback Corpus for Sentiment Analysis}. + In: {2018 10th International Conference on Knowledge and Systems Engineering + (KSE)}. pp. 19--24. {IEEE}, Ho Chi Minh City, Vietnam (2018) + +\bibitem{PhuNguyen} +{Nguyen}, P.X.V., {Truong}, T.T.H., {Nguyen}, K.V., {Nguyen}, N.L.T.: {Deep + Learning versus Traditional Classifiers on Vietnamese Students' Feedback + Corpus}. In: {2018 5th NAFOSTED Conference on Information and Computer + Science (NICS)}. pp. 75--80. Ho Chi Minh City, Vietnam (2018) + +\bibitem{VuDucNguyen} +{Nguyen}, V.D., {Nguyen}, K.V., {Nguyen}, N.L.T.: {Variants of Long Short-Term + Memory for Sentiment Analysis on Vietnamese Students’ Feedback Corpus}. In: + {2018 10th International Conference on Knowledge and Systems Engineering + (KSE)}. pp. 306--311. IEEE, Ho Chi Minh City, Vietnam (2018) + +\bibitem{AurelienGeron} +Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, + O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., + Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: + Scikit-learn: Machine learning in python. Journal of Machine Learning + Research \textbf{12}, 2825--2830 (2011) + +\bibitem{CarloStrapparava} +{Strapparava}, C., {Mihalcea}, R.: {SemEval-2007 Task 14: Affective Text}. In: + {Proceedings of the 4th International Workshop on Semantic Evaluations + (SemEval-2007)}. pp. 70--74. { Association for Computational Linguistics}, + Prague (2007) + +\bibitem{TingweiWang} +{T. {Wang} and X. {Yang} and C. {Ouyang}}: {A Multi-emotion Classification + Method Based on BLSTM-MC in Code-Switching Text: 7th CCF International + Conference, NLPCC 2018, Hohhot, China, August 26–30, 2018, Proceedings, + Part II.} pp. 190--199. Natural Language Processing and Chinese Computing + (2018) + +\bibitem{ZhongqingWang} +{Wang}, Z., {Li}, S.: {Overview of NLPCC 2018 Shared Task 1: Emotion Detection + in Code-Switching Text: 7th CCF International Conference, NLPCC 2018, Hohhot, + China, August 26–30, 2018, Proceedings, Part II}. pp. 429--433. Natural + Language Processing and Chinese Computing (2018) + +\bibitem{Facial2007} +{Zhang}, S., {Wu}, Z., {Meng}, H., {Cai}, L.: Facial expression synthesis using + pad emotional parameters for a chinese expressive avatar. vol.~4738, pp. + 24--35 (09 2007) + +\bibitem{YingjieZhang} +{Zhang}, Y., {Wallace}, B.C.: {A Sensitivity Analysis of (and Practitioners’ + Guide to Convolutional}. pp. 253--263. 2017 AFNLP, Taipei, Taiwan (2017) + +\bibitem +{Nguyen}, K.V., {Nguyen}, N.L.T., 2016, October. {Vietnamese transition-based dependency parsing with supertag features}. In 2016 Eighth International Conference on Knowledge and Systems Engineering (KSE) (pp. 175-180). IEEE. + +\bibitem{nguyen2014treebank} +Nguyen, D.Q., Pham, S.B., Nguyen, P.T., Le Nguyen, M., et al.: From treebankconversion to automatic dependency parsing for vietnamese. In: International Con-ference on Applications of Natural Language to Data Bases/Information Systems.pp. 196–207. Springer (2014) + +\bibitem{nguyen2016vietnamese} +Nguyen, K.V., Nguyen, N.L.T.: Vietnamese transition-based dependency parsingwith supertag features. In: 2016 Eighth International Conference on Knowledgeand Systems Engineering (KSE). pp. 175–180. IEEE (2016) + + +\bibitem{bach2018empirical} +Bach, N.X., Linh, N.D., Phuong, T.M.: An empirical study on pos tagging forvietnamese social media text. Computer Speech \& Language50, 1–15 (2018) + +\bibitem{nguyen2017word} +Nguyen, D.Q., Vu, T., Nguyen, D.Q., Dras, M., Johnson, M.: From word segmen-tation to pos tagging for vietnamese. arXiv preprint arXiv:1711.04951 (2017) + +\bibitem{thao2007named} +Thao, P.T.X., Tri, T.Q., Dien, D., Collier, N.: Named entity recognition in viet-namese using classifier voting. ACM Transactions on Asian Language InformationProcessing (TALIP)6(4), 1–18 (2007) + +\bibitem{nguyen2016approach} +Nguyen, L.H., Dinh, D., Tran, P.: An approach to construct a named entity anno-tated english-vietnamese bilingual corpus. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP)16(2), 1–17 (2016) + + +\bibitem{Nguyen_2009} +Nguyen, D.Q., Nguyen, D.Q., Pham, S.B.: A vietnamese question answeringsystem. 2009 International Conference on Knowledge and Systems Engineering(Oct 2009). https://doi.org/10.1109/kse.2009.42,http://dx.doi.org/10.1109/KSE.2009.42 + +\bibitem{le2018factoid} +Le-Hong, P., Bui, D.T.: A factoid question answering system for vietnamese. In:Companion Proceedings of the The Web Conference 2018. pp. 1049–1055 (2018) + + + + + + + +\end{thebibliography} + + +\end{document} diff --git a/references/2019.arxiv.ho/source/sections/1-introduction.tex b/references/2019.arxiv.ho/source/sections/1-introduction.tex new file mode 100644 index 0000000000000000000000000000000000000000..c537c0eb7ca340ef911b8cd206e2eb40afb90927 --- /dev/null +++ b/references/2019.arxiv.ho/source/sections/1-introduction.tex @@ -0,0 +1,31 @@ +\section{Introduction} \label{introduction} +Expressing emotion is a fundamental need of human and that we use language not just to convey facts, but also our emotions \cite{Kiritchenko}. Emotions determine the quality of our lives, and we organize our lives to maximize the experience of positive emotions and minimize the experience of negative emotions \cite{Ekman}. Thus, Paul Ekman \cite{Ekman1993} proposed six basic emotions of human including enjoyment, sadness, anger, surprise, fear, and disgust through facial expression. Nonetheless, apart from facial expression, many different sources of information can be used to analyze emotions since emotion recognition has emerged as an important research area. And in recent years, emotion recognition in text has become more popular due to its vast potential applications in marketing, security, psychology, human-computer interaction, artificial intelligence, and so on \cite{Mohammad}. + +In this study, we focus on the problem of recognizing emotions for Vietnamese comments on social network. To be more specific, the input of the problem is a Vietnamese comment from social network, and the output is a predicted emotion of that comment labeled with one of these: enjoyment, sadness, anger, surprise, fear, disgust and other. Several examples are shown in Table~\ref{tab:1}. + +\begin{table}[!htbp] +\caption{Examples of emotion-labeled sentences.} +\label{tab:1} +%\resizebox{\columnwidth}{!}{ +\vspace{10pt} +\begin{tabular}{|c|p{5cm}|p{4.5cm}|c|} +\hline +\textbf{No.} & \multicolumn{1}{c|}{\textbf{Vietnamese sentences}} & \multicolumn{1}{c|}{\textbf{English translation}} & \textbf{Emotion} \\ \hline +1 & Ảnh đẹp quá! & The picture is so beautiful! & Enjoyment \\ \hline +2 & Tao khóc..huhu.. Tao rớt rồi & I'm crying..huhu.. I failed the exam. & Sadness \\ \hline +3 & Khuôn mặt của tên đó vẫn còn ám ảnh tao. & The face of that man still haunts me. & Fear \\ \hline +4 & Cái gì cơ? Bắt bỏ tù lũ khốn đó hết! & What the fuck? Arrest all those goddamn bastards! & Anger \\ \hline +5 & Thật không thể tin nổi, tại sao lại nhanh đến thế?? & It's unbelievable, why can be that fast?? & Surprise \\ \hline +6 & Những điều nó nói làm tao buồn nôn. & What he said makes me puke. & Disgust \\ \hline +\end{tabular} +%} +\end{table} + +In this paper, our two key contributions are summarized as follows. +\begin{itemize} +\item One of the most primary contributions is to obtain the UIT-VSMEC corpus, which is the first corpus for emotion recognition for Vietnamese social media text. As a result, we achieved 6,927 emotion annotated-sentences. To ensure that only the best results with high consistency and accuracy are reached, we built a very coherent and thorough annotation guideline for the dataset. The corpus is publicly available for research purpose. + +\item The second one, we tried using four learning algorithms on our UIT-VSMEC corpus, two machine learning models consisting of Support Vector Machine (SVM) and Random Forest versus two deep learning models including Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). +\end{itemize} + +The structure of the paper is organized thusly. Related documents and studies are presented in Section II. The process of building corpus, annotation guidelines, and dataset evaluation are described in Section III. In Section IV, we show how to apply SVM, Random Forest, CNN, and LSTM for this task. The experimental results are analyzed in Section V. Conclusion and future work are deduced in Section VI. \ No newline at end of file diff --git a/references/2019.arxiv.ho/source/sections/2-relatedwork.tex b/references/2019.arxiv.ho/source/sections/2-relatedwork.tex new file mode 100644 index 0000000000000000000000000000000000000000..9942696eaee2decc48e2bd85a139f7890882c8e8 --- /dev/null +++ b/references/2019.arxiv.ho/source/sections/2-relatedwork.tex @@ -0,0 +1,8 @@ +\section{Related Work} \label{wsmethod} +There are some related work in English and Chinese. In 2007, the SemEval-2007 Task 14 \cite{CarloStrapparava} developed a dataset for emotion recognition with six emotion classes (enjoyment, anger, disgust, sadness, fear and surprise) including 1,250 newspaper headline human-annotated sentences. In 2012, Mohammad \cite{Mohammad} published an emotion corpus with 21,052 comments from Tweets annotated also by six labels of emotion (enjoyment, anger, disgust, sadness, fear and surprise). In 2017, Mohammad \cite{SaifMohammad2017} again published a corpus annotated with only four emotion labels (anger, fear, enjoyment and sadness) for 7,079 comments from Tweets. In 2018, Wang \cite{ZhongqingWang} put out a bilingual corpus in Chinese and English for emotion recognition including 6,382 sentences tagged by five different emotions (enjoyment, sadness, fear, anger and surprise). In general, corpus for emotion recognition task use some out of six basic emotions of human (enjoyment, sadness, anger, disgust, fear and surprise) based on Ekman's emotion theory \cite{Ekman1993}. + +In terms of algorithms, Kratzwald \cite{BernhardKratzwald} tested the efficiency of machine learning algorithms (Random Forest and SVM) and deep learning algorithms (Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (BiLSTM)) combined with pre-trained word embeddings on multiple emotion corpora. In addition, the BiLSTM combined with pre-trained word embeddings reached the highest result of 58.2\% of F1-score compared to Random Forest and SVM with 52.6\% and 54.2\% respectively on the General Tweets corpus \cite{BernhardKratzwald}. Likewise, Wang \cite{TingweiWang} proposed the Bidirectional Long Short-Term Memory Multiple Classifiers (BLSTM-MC) model on a bilingual corpus in Chinese and English that achieved the F1-score of 46.7\%, ranked third in the shared task NLPCC2018 - Task 1 \cite{ZhongqingWang}. + +In Vietnamese, there are quite a quantity of research works on other NLP tasks such as parsing \cite{nguyen2014treebank,nguyen2016vietnamese}, part-of-speech \cite{bach2018empirical,nguyen2017word}, named entity recognition \cite{thao2007named,nguyen2016approach}, sentiment analysis \cite{KietVanNguyen,PhuNguyen,VuDucNguyen} and question answering \cite{Nguyen_2009,le2018factoid}. However, there are no research publications on emotion recognition for Vietnamese social media texts. Therefore, we decided to build a first corpus of Vietnamese emotion recognition for the research purposes. In this paper, we evaluate machine learning (SVM and Random Forest) and deep learning models (CNN and LSTM) on our corpus. + +%In Vietnamese, there are several related work in sentiment analysis such as aspect-based sentiment analysis in the VLSP shared task \cite{VLSPX} and sentiment analysis on students' feedback \cite{KietVanNguyen,PhuNguyen,VuDucNguyen}. However, after a comprehensive research, we learned that neither a corpus nor a work for the emotion recognition for Vietnamese text-based is currently on deck. Hence, we present the UIT-VSMEC corpus for this task then we test and compare the results based on F1-score measurement between machine learning (SVM and Random Forest) and deep learning models (CNN and LSTM) on our corpus as the first results. \ No newline at end of file diff --git a/references/2019.arxiv.ho/source/sections/3-corpus.tex b/references/2019.arxiv.ho/source/sections/3-corpus.tex new file mode 100644 index 0000000000000000000000000000000000000000..002c1f109764add5dc60ffc7d924a06906a62332 --- /dev/null +++ b/references/2019.arxiv.ho/source/sections/3-corpus.tex @@ -0,0 +1,126 @@ + +\section{Corpus Construction} +%Sarah check lai nha +In this section, we present the process of developing the UIT-VSMEC corpus in Section 3.1, annotation guidelines in Section 3.2, corpus evaluation in Section 3.3 and corpus analysis in Section 3.4. +\subsection{Process of Building the Corpus} + +The overview of corpus-building process which includes three phases is shown in Figure \ref{fig1} and the detailed description of each phase is presented shortly thereafter. + +\begin{figure}[!htbp] +\centering +\includegraphics[width=\textwidth]{./images/gettingData.pdf} +\caption{Overview of corpus-building process.} +\label{fig1} +\end{figure} + +\textbf{Phase 1 - Collecting data}: We collect data from Facebook which is the most popular social network in Vietnam. In particular, according to a survey of 810 Vietnamese people conducted by \cite{Jointstockcompany} in 2018, Facebook is used the most in Vietnam. Moreover, as reported by the statistic of the number of Facebook users by \cite{Nguyen}, there are 58 million Facebook users in Vietnam, ranking as the 7th country with the most users in the world. As the larger the number of the users and the more interaction between them, the richer the data. And to collect the data, we use Facebook API to get Vietnamese comments from public posts. + +\textbf{Phase 2 - Pre-processing data}: To ensure users' privacy, we replace users' names in the comments with PER tag. The rest of the comments is kept as it is to retain the properties of a comment on social network. + + +\textbf{Phase 3 - Annotating data}: This step is divided into two stages: \textbf{Stage 1}, building annotation guidelines and training for four annotators. Data tagging is repeated for 806 sentences along with editing guidelines until the consensus reaches more than 80\%. \textbf{Stage 2}, 6,121 sentences are shared equally for three annotators while the another checking the whole 6,121 sentences and the consensus between the two is above 80\%. + +\subsection{Annotation Guidelines} +There are many suggestions on the number of basic emotions of human. According to studies in the field of psychology, there are two outstanding viewpoints in basic human emotions: "Basic Emotions" by Paul Ekman and "Wheel of Emotions" by Robert Plutchik \cite{Kiritchenko}. Paul Ekman's studies point out that there are six basic emotions which are expressed through the face \cite{Ekman1993}: enjoyment, sadness, anger, fear, disgust and surprise. Eight years later, Robert Plutchik gave an another definition of emotion. In this concept, emotion is divided into eight basic ones which are polarized in pairs: enjoyment - sadness, anger - fear, trust - disgust and surprise - anticipation \cite{Kiritchenko}. Despite the agreement on basic emotions, psychologists dissent from the number of which are the most basic that may have in humans, some ideas are 6, 8 and 20 or even more \cite{SaifMohammad2017}. + +To that end, we chose six labels of basic emotions (enjoyment, sadness, anger, fear, disgust and surprise) for our UIT-VSMEC corpus based on six basic human emotions proposed by Ekman \cite{Ekman1993} together with Other label to mark a sentence with an emotion out of the six above or a sentence without emotion, considering most of the automated emotion recognition work in English \cite{SaifMohammad,Kiritchenko} are all established from Ekman's emotion theory (1993) and with a large amount of comments in the corpus, a small number of emotions makes the manual tagging process more convenient. + +Based on Ekman's instruction in basic human emotions \cite{PaulEkman}, we build annotation guidelines for Vietnamese text with seven emotion labels described as follows. +\begin{itemize} +\item \textbf{Enjoyment}: For comments with the states that are triggered by feeling connection or sensory pleasure. It contains both peace and ecstasy. The intensity of these states varies from the enjoyment of helping others, a warm uplifting feeling that people experience when they see kindness and compassion, an experience of ease and contentment or even the enjoyment of the misfortunes of another person to the joyful pride in the accomplishments or the experience of something that is very beautiful and amazing. For example, the emotion of the sentence "Nháy mắt thôi cũng đáng yêu, kkk" (English translation: "Just the act of winking is so lovely!") is Enjoyment. + +\item \textbf{Sadness}: For comments that contain both disappointment and despair. The intensity of its states varies from discouragement, distraughtness, helplessness, hopelessness to strong suffering, a feeling of distress and sadness often caused by a loss or sorrow and anguish. The Vietnamese sentence "Lúc đấy khổ lắm... kỉ niệm :(" (English translation: "It was hard that time..memory :(" ) has an emotion of Sadness, for instance. + +\item \textbf{Fear}: For comments that show anxiety and terror. The intensity of these states varies from trepidation - anticipation of the possibility of danger, nervousness, dread to desperation, a response to the inability to reduce danger, panic and horror - a mixture of fear, disgust and shock. A given sentence "Chuyện này làm tao nổi hết da gà" (English translation: "This story causes me goosebumps") is a Fear labeled-sentence. + +\item \textbf{Anger}: For comments with states that are triggered by a feeling of being blocked in our progress. It contains both annoyance and fury and varies from frustration which is a response to repeated failures to overcome an obstacle, exasperation - anger caused by strong nuisance, argumentativeness to bitterness - anger after unfair treatment and vengefulness. For example, "Biến mẹ mày đi!" (English translation: "You fucking get lost!") is labeled with Angry. + + +\item \textbf{Disgust}: For comments which show both dislike and loathing. Their intensity varies from an impulse to avoid something disgusting or aversion, the reaction to a bad taste, smell, thing or idea, repugnance to revulsion which is a mixture of disgust and loathing or abhorrence - a mixture of intense disgust and hatred. As "Làm bạn với mấy thể loại này nhục cả người" (English translation: "Making friends with such types humiliates you") has an emotion of Disgust. + +\item \textbf{Surprise}: For comments that express the feeling caused by unexpected events, something hard to believe and may shock you. This is the shortest emotion of all emotions, only takes a few seconds. And it passes when we understand what is happening, and it may become fear, anger, relief or nothing ... depends on the event that makes us surprise. "Trên đời còn tồn tại thứ này sao??" (English translation: "How the hell in this world this still exists??") is annotated with Surprise. + +\item \textbf{Other}: For comments that show none of those emotions above or comments that do not contain any emotions. For instance, "Mình đã xem rất nhiều video như này rồi nên thấy cũng bình thường" (English translation: "I have seen a lot of videos like this so it's kinda normal") is neutral, so its label is Other. +\end{itemize} +\subsection{Corpus Evaluation} + + +We use the A\textit{\textsubscript{m}} agreement measure \cite{PlabanKumarBhowmick} to evaluate the consensus between annotators of the corpus. This agreement measure was also utilized in the UIT-VSFC corpus \cite{KietVanNguyen}. A\textit{\textsubscript{m}} is calculated by the following formula. +\[A_m = \frac{P_o - P_e}{1 - P_e}\] + + +Where, P\textit{\textsubscript{o}} is the observed agreement which is the proportion of sentences with both of the annotators agreed on the classes pairs and P\textit{\textsubscript{e}} is the expected agreement that is the proportion of items for which agreement is expected by chance when the sentences are seen randomly. + +Table \ref{tab:2} presents the consensus of the entire UIT-VSMEC corpus with two separate parts. The consensus of 806 sentences which can be seen as the first stage of annotating data mentioned in section 3-3.1. A\textit{\textsubscript{m}} agreement level is high with 82.94\% by four annotators. And the annotation agreement in the second stage (3-3.1) where X$_1$, X$_2$, X$_3$ are 3 annotators independently tagging data and Y is the checker of one another. The A\textit{\textsubscript{m}} and P\textit{\textsubscript{o}} of annotation pair X$_3$-Y are highest of 89.12\% and 92.81\%. + +\begin{table}[] +\centering +\caption{Annotation agreement of the UIT-VSMEC corpus (\%)} +\label{tab:2} +\begin{tabular}{|c|c|c|c|c|} +\hline +\textbf{Stage} & \textbf{Annotators} & \textbf{P$_0$} & \textbf{P$_e$} & \textbf{A$_m$} \\ \hline +\multicolumn{1}{|l|}{1} & X$_1$-X$_2$-X$_3$-Y (806 sentences) & 92.66 & 56.98 & 82.94 \\ \hline +\multicolumn{1}{|l|}{2} & X$_1$-Y (2,032 sentences) & 88.00 & 18.61 & 85.25 \\ +\multicolumn{1}{|l|}{} & X$_2$-Y (2,112 sentences) & 86.27 & 31.23 & 80.03 \\ +\multicolumn{1}{|l|}{} & X$_3$-Y (1,977 sentences) & 92.81 & 33.95 & 89.12 \\ \hline +\end{tabular} +\end{table} + +\subsection{Corpus Analysis} +After building the UIT-VSMEC corpus, we obtained 6,927 human-annotated sentences with one of the seven emotion labels. Statistics of emotion labels of the corpus is presented in Table \ref{tab:3}. + +\begin{table}[!h] +\centering +\caption{Statistics of emotion labels of the UIT-VSMEC corpus } +\label{tab:3} +\begin{tabular}{|l|r|r|} +\hline +\textbf{Emotion} & \textbf{Sentences} & \textbf{Percentage} (\%) \\ \hline +Enjoyment & 1,965 & 28.36 \\ \hline +Disgust & 1,338 & 19.31 \\ \hline +Sadness & 1,149 & 16.59 \\ \hline +Anger & 480 & 6.92 \\ \hline +Fear & 395 & 5.70 \\ \hline +Surprise & 309 & 4.46 \\ \hline +Other & 1,291 & 18.66 \\ \hline +Total & 6,927 & 100 \\ \hline +\end{tabular} +\end{table} + +%\begin{figure}[!htbp] +%\centering +%\includegraphics[width=7cm]{./images/dataset.JPG} +%\caption{Proportions of emotion labels of the UIT-VSMEC corpus.} +%\label{fig:2} +%\end{figure} + +Through Table \ref{tab:3}, we concluded that the comments got from social network are uneven in number among different labels in which the enjoyment label reaches the highest number of 1,965 sentences (28.36\%) while the surprise label arrives at the lowest number of 309 sentences (4.46\%). + +Besides, we listed the number of the sentences of each label up to their lengths. Table \ref{tab:4} shows the distribution of emotion-annotated sentences according to their lengths. It is easy to see that most of the comments are from 1 to 20 words accounting for 81.76\%. + +\begin{table}[h] +\centering +\caption{Distribution of emotion-annotated sentences according to the length of the sentence (\%)} +\label{tab:4} +\setlength\tabcolsep{2.1pt} +% \resizebox{\columnwidth}{5}{ +\begin{tabular}{|c|c|c|c|c|c|c|c|c|} +\hline +\textbf{Length} & \textbf{Enjoyment} & \textbf{Disgust} & \textbf{Sadness} & \textbf{Anger} & \textbf{Fear} & \textbf{Surprise} & \textbf{Other} & \textbf{Overall} \\ \hline +1-5 & 5.16 & 2.84 & 1.70 & 0.69 & 0.94 & 0.85 & 1.87 & \textbf{14.05} \\ \hline +6-10 & 8.98 & 4.22 & 4.98 & 1.41 & 1.42 & 2.25 & 7.20 & \textbf{30.38} \\ \hline +10-15 & 5.87 & 3.99 & 4.11 & 1.40 & 1.27 & 0.94 & 5.00 & \textbf{22.58} \\ \hline +16-20 & 4.17 & 3.05 & 2.51 & 1.14 & 0.85 & 0.24 & 2.79 & \textbf{14.75} \\ \hline +21-25 & 1.96 & 1.93 & 1.50 & 0.66 & 0.40 & 0.15 & 1.11 & 7.71 \\ \hline +26-30 & 1.08 & 1.31 & 0.95 & 0.45 & 0.27 & 0.01 & 0.53 & 4.6 \\ \hline +\textgreater{}30 & 1.23 & 1.97 & 0.84 & 1.17 & 0.55 & 0.02 & 0.15 & 5.93 \\ \hline +Total & 28.36 & 19.31 & 16.59 & 6.92 & 5.70 & 4.46 & 18.66 & 100 \\ \hline +\end{tabular} +\end{table} +% \begin{figure*}[htbp] +% \centering +% \includegraphics[width=10cm]{./images/cnnmodel.pdf} +% \caption{The architecture of aspect detection.} +% \label{fig1} +% \end{figure*} diff --git a/references/2019.arxiv.ho/source/sections/4-method.tex b/references/2019.arxiv.ho/source/sections/4-method.tex new file mode 100644 index 0000000000000000000000000000000000000000..68ea52da5df825156444ea107a2791f4d6717684 --- /dev/null +++ b/references/2019.arxiv.ho/source/sections/4-method.tex @@ -0,0 +1,73 @@ +\section{Methodology} +In this paper, we use two kinds of methodologies to evaluate the UIT-VSMEC corpus including two machine learning models (Random Forest and SVM) and two deep learning models (CNN and LSTM) as the first models described as follows. +\subsection{Machine Learning Models} +The authors in \cite{BernhardKratzwald} proposed SVM and Random Forest algorithms for emotion recognition. Under which, we also tested three more machine learning algorithms including Decision Tree, kNN and Naive Bayes on 1,000 emotion-annotated sentences extracted from the UIT-VSMEC corpus by Orange3. Consequently, Random Forest achieved the second best result after SVM which is displayed in Table \ref{tab:5}. It is the main reason why we chose SVM and Random Forest for experiments on the UIT-VSMEC corpus. + +% \begin{table}[h] +% \centering +% \caption{Experimental results by Orange3 of machine learning models on 1,000 emotion-annotated sentences from the UIT-VSMEC corpus (\%)} +% \label{tab:5} +% \setlength\tabcolsep{2.1pt} +% % \resizebox{\columnwidth}{5}{ +% \begin{tabular}{|l|c|c|c|} +% \hline +% \textbf{Method} & \textbf{Precision} & \textbf{Recall} & \textbf{Weighted F1} \\ \hline +% \textbf{Random Forest} & 32.8 & 35.84 & \textbf{32.8} \\ \hline +% \textbf{SVM} & 36.7 & 37.6 & \textbf{37.0} \\ \hline +% Decision Tree & 29.3 & 30.05 & 29.6 \\ \hline +% kNN & 30.2 & 28.9 & 27.1 \\ \hline +% Naĩve Bayes & 38.1 & 20.8 & 19.2 \\ \hline +% \end{tabular} +% \end{table} +\begin{table}[h] +\centering +\caption{Experimental results by Orange3 of machine learning models on 1,000 emotion-annotated sentences from the UIT-VSMEC corpus (\%)} +\label{tab:5} +\begin{tabular}{|l|l|l|} +\hline +\textbf{Method} & \textbf{Accuracy} & \textbf{Weighted F1} \\ \hline +\textbf{Random Forest} & 35.8 & \textbf{32.8} \\ \hline +\textbf{SVM} & 37.6 & \textbf{37.0} \\ \hline +Decision Tree & 30.5 & 29.6 \\ \hline +kNN & 28.9 & 27.1 \\ \hline +Naĩve Bayes & 20.8 & 19.2 \\ \hline +\end{tabular} +\end{table} + +\subsubsection{Random Forest} +Random Forest is a versatile machine learning algorithm when used for classification problems, predicting linear regression values and multi-output tasks. The idea of Random Forest is to use a set of Decision Tree classification, each of which is trained on different parts of the dataset. After that, Random Forest will get back all the classification results of the seedlings from which it chooses the most voted one to give the final result. Despite of its simplicity, Random Forest is one of the most effective machine learning algorithms today \cite{AurelienGeron}. +\subsubsection{Support Vector Machine (SVM)} + We use the SVM machine learning algorithm as a baseline result for this emotion recognition problem. According to the authors in \cite{SaifMohammad}, SVM is an effective algorithm for classification problems with high features. Here, we use SVM model supported by scikit-learn library. +\subsection{Deep Learning Models} +\subsubsection{Long Short-Term Memory (LSTM)} +LSTM is also applied for the UIT-VSMEC corpus for various reasons. To begin with, LSTM is considered as the state-of-the-art method of almost sequence prediction problems. Moreover, through the two competitions WASSA-2018 \cite{RomanKlinger} and SemEval-2018 Task 1 \cite{Mohammad2018} for emotion recognition task, we acknowledged that LSTM was effectively used the most. Furthermore, LSTM has advantages over conventional neural networks and Recurrent Neural Network (RNN) in many different ways due to its selective memory characteristic in a long period. This is also the reason why the authors in \cite{BernhardKratzwald} chose to use it in his paper. Therefore, we decided to use LSTM on the same problem on our corpus. + + +LSTM consists of four main parts: Word embeddings input, LSTM cell network, fully connected and softmax. With the input, each cell in the LSTM network receives a word vector represented by word embeddings with the form [1 x n] where n is the fixed length of the sentence. Then cells calculate the values and gets the results as vectors in LSTM cell network. These vectors will go through fully connected and the output values will then pass through softmax function to give an appropriate classification for each label. + +\subsubsection{Convolutional Neural Network (CNN)} + We use Convolutional Neural Network (CNN) algorithm which is proposed in \cite{Kim} to recognize emotions in a sentence. CNN is the algorithm that achieves the best results in four out of the seven major problems of Natural Language Processing which includes both emotion recognition and question classification tasks (text classification, language model, speech recognition, title generator, machine translation, text summarization and Q\&A systems) \cite{Kim,YingjieZhang}. + +A CNN model consists of three main parts: Convolution layer, pooling layer and fully connected layer. In convolution layer - the Kernel, we used 3 types of filters of different sizes with total 512 filters to extract the high-level features and obtain convolved feature maps. These then go through the pooling layer which is responsible for reducing the spatial size of the convolved feature and decreasing the computational power required to process the data through dimensionality reduction. The convolutional layer and the pooling layer together form the i-th layer of a Convolutional Neural Network. Moving on, the final output will be flattened and fed to a regular neural network in the fully connected layer for classification purposes using the softmax classification technique. +% An overview of the CNN model architecture for text (Kim 2014) is introduced in Figure 2, namely each \textit{i}-th word in the sentence is represented as a k-dimensional word vector, $x_i \in R^k$. And a sentence of length n will be padded from n vector from $x_i$. +% \[x_{1:n} = x_1 \bigoplus x_2 \bigoplus … \bigoplus x_n\] +% Where $\bigoplus$ is the concatenation operator. For sentence of length that not equal to n, the padding values will be added. A filter has the form $w \in R^{h,k}$, where $h$ is a size of a window to calculate high-level features of the input of which each value equals +% \[z_i = w.x_{i:i+h-1} + b\] +% \[c_i = \int (z) \] +% Here, $b \in R$ is the bias term and $\int$ is a nonlinear function. This filter is applied to all window of words from ${x_{1:h}, x_{2:h+1}, … x_{n-h+1:n}}$ to produce a feature map as follows + +% \[c = [c_1, c_2, …, c_{n-h+1]}\] + +% with $c \in R^{n-h+1} $ . Next, we use apply the max pooling operation over each feature map to take the most important value - the maximum value $\textit{ĉ} = max(c)$ corresponding to a particular filter. Then add value $\textit{ĉ}$ to a fully connected neural network. + + + + + +% Figure 2: Overview of using simple CNN model in text classification problem proposed by (Kim 2014) (ở đây chèn hình nhưng đang lỗi) + +% \begin{figure*}[!htp] +% \includegraphics[width=5cm]{./images/cnnmodel.pdf} +% \caption{The architecture for aspect detection.} +% \label{fig1} +% \end{figure*} diff --git a/references/2019.arxiv.ho/source/sections/5-experiments.tex b/references/2019.arxiv.ho/source/sections/5-experiments.tex new file mode 100644 index 0000000000000000000000000000000000000000..5b020a8894d605b8835e167548ead1fe87436c6b --- /dev/null +++ b/references/2019.arxiv.ho/source/sections/5-experiments.tex @@ -0,0 +1,125 @@ +\section{Experiments and Error Analysis} \label{experiment and Evaluation} +\subsection{Corpus Preparation} + +We at first built a normalized corpus for comparison and evaluation where spelling errors have been corrected and acronyms in various forms have been converted back to their original words, seeing it is impossible to avoid such problems of text on social networks when it does not distinguish any type of users. Table \ref{tab:6} shows some examples being encountered the most in the dataset. + +\begin{table}[!htp] +\centering +\caption{Vietnamese abbreviations in the dataset.} +\label{tab:6} +\begin{tabular}{|c|l|l|l|} +\hline +\textbf{No.} & \multicolumn{1}{c|}{\textbf{Abbreviation}}& \multicolumn{1}{c|}{\textbf{Vietnamese meaning}}& \multicolumn{1}{c|}{\textbf{English meaning}}\\ \hline +1 & “dc” or “dk” or “duoc” & "được" & "ok" \\ \hline +2 & “ng” or “ngừi” & "người" & "people" \\ \hline +3 & "trc" or "trk" & "trước" & "before" \\ \hline +4 & "cg" or "cug" or "cũg" & "cũng" & "also" \\ \hline +5 & "mk" or "mik" or "mh" & "mình" & "I" \\ \hline +\end{tabular} +\end{table} + +We then divided the UIT-VSMEC corpus into the ratio of 80:10:10, in which 80\% of the corpus is the training set, 10\% is the validation one and the rest is the test set. The UIT-VSMEC corpus is an imbalanced-labels corpus, therefore, to ensure that sentences in low-volume labels are distributed fully in each set, we use stratified sampling method utilizing train\_test\_split() function supported by scikit learn library to distribute them into training, validation and test sets. The result is presented in Table \ref{tab:7}. +\begin{table}[!htp] +\centering +\caption{Statistics of emotion-labeled sentences in training, validation and test sets.} +\label{tab:7} +\begin{tabular}{|l|r|r|r|r|} +\hline + \textbf{Emotion} & \textbf{Train} & \textbf{Dev} & \textbf{Test} & \textbf{Total} \\ \hline +Enjoyment & 1,573 & 205 & 187 & 1,965 \\ \hline +Disgust & 1,064 & 141 & 133 & 1,338 \\ \hline +Sadness & 938 & 92 & 119 & 1,149 \\ \hline +Anger & 395 & 38 & 47 & 480 \\ \hline +Fear & 317 & 38 & 47 & 395 \\ \hline +Surprise & 242 & 36 & 31 & 309 \\ \hline +Other & 1,019 & 132 & 140 & 1,291 \\ \hline +All & 5,548 & 686 & 693 & 6,927 \\ \hline +\end{tabular} +\end{table} + +\subsection{Experimental Settings} +%%--> Viet lai cai nay + +%Đối với biểu diễn từ cho phương pháp máy học và học sâu +%-->cite các bộ word embedding nếu sử dụng của người ta. +In this paper, to represent words in vector form, we use two different methods word embeddings and bag of words. For the two machine learning models SVM and Random Forest, we use bag of words in conjunction with TF-IDF. For the two other deep learning models LSTM and CNN, we utilize pre-trained word embeddings including word2vec \footnote[3]{\url{ https://github.com/vncorenlp/VnCoreNLP}} and fastText \footnote[4]{\url{https://fasttext.cc/docs/en/crawl-vectors.html}} used as its main techniques. + +%Thông số của từng mô hình áp dụng. +With machine learning models SVM and Random Forest, grid-search method is utilized to get the most appropriate parameters for the task. In particular, with SVM we use word of tag (1, 3) combined with bag of char (1,7) features and loss function hinge, and to reduce overfitting we apply the l2-regularization technique with lambd. = 1e-4. About Random Forest model, the number of decision trees is 256 and the depth of the trees is 64. + +For LSTM model, we use the many-to-one architecture due to the classification requirement of the problem. To select proper parameters for emotion recognition in Vietnamese, we add two drop-out classes of 0.75 and 0.5 respectively to increase processing time as well as to avoid overfitting. + +Regarding deep learning CNN model, we apply three main kernels: 3, 4 and 5 with a number of each is 128. Besides, drop-out of 0.95 and l2 of 0.01 are adopted to avoid overfitting. Properties and models are developed from Yoon Kim's work \cite{Kim}. + + +%Bên cạnh đó chúng tôi cũng kiểm thử các kích thước của câu đầu vào để chọn được kích thước đầu vào là tốt nhất cùng với bảng 4. Để chọn được đầu vào cho mô hình, chúng tôi thiết kế cài đặt chọn kích thước đầu vào từ 20 tới 60 từ trên câu và bước nhảy là 5. + +%(Dịch cái trên by Dương ngáo đá) +% Additionally, we also test the size of the input sentence (number of words per sentence) to choose the best sentence-input size. We design to setting to choose the size from 20 to 60 and jump-step is 5. Với kết quả thu được chúng tôi thấy rằng câu có độ dài là 40 cho kết quả cao nhất với 58.05\% từ độ đo weighted F1. + +% \begin{table}[] +% \centering +% \caption{Performance according to different sentence lengths.} +% \label{tab:8} +% \begin{tabular}{|c|c|} +% \hline +% \textbf{Sentence Length} & \textbf{weighted F1-Score} (\%) \\ \hline +% 20 & 56.59 \\ \hline +% 25 & 57.16 \\ \hline +% 30 & 56.33 \\ \hline +% 35 & 56.35 \\ \hline +% \textbf{40} & \textbf{58.65} \\ \hline +% 45 & 57.92 \\ \hline +% 50 & 57.63 \\ \hline +% 55 & 57.64 \\ \hline +% 60 & 57.96 \\ \hline +% \end{tabular} +% \end{table} + +\subsection{Experimental Results} + +In this section, we present the results of two experiments. Firstly, we test and compare the results of each model on the UIT-VSMEC corpus. Secondly, we evaluate the influence of Other label on this corpus after implementing these machine learning and deep learning models on the corpus yet without Other label. All models are evaluated by accuracy and weighted F1-score metrics. +\begin{table}[htb] +\centering +\caption{Experimental results of the UIT-VSMEC corpus.} +\label{tab:8} +\begin{tabular}{|l|l|r|r|} +\hline +\textbf{Corpus} & \textbf{Algorithm} & \textbf{Accuracy}(\%) & \textbf{Weighted F1-Score}(\%) \\ \hline +Original & RandomForest+BoW & 50.64 & 40.11 \\ + & SVM+BoW & 58.00 & 56.87 \\ + & LSTM+word2Vec & 53.39 & 53.30 \\ + & LSTM+fastText & 54.25 & 53.77 \\ + & \textbf{CNN+word2Vec} & \textbf{59.74} & \textbf{59.74} \\ + & CNN+fastText & 56.85 & 56.79 \\ \hline +Without Other & RandomForest+BoW & 50.64 & 49.14 \\ +label & SVM+BoW & 63.12 & 62.45 \\ + & LSTM+word2Vec & 61.70 & 61.09 \\ + & LSTM+fastText & 62.06 & 61.83 \\ + & \textbf{CNN+word2Vec} & \textbf{66.48} & \textbf{66.34} \\ + & CNN+fastText & 63.47 & 62.68 \\ \hline +\end{tabular} +\end{table} + +Through this, we concluded that, when removing Other label, the weighted F1-score reaches higher results with the same methods. Firstly, it is because of the decrease in number of emotion labels in the UIT-VSMEC corpus from 7 to 6 (anger, enjoyment, surprise, sadness, fear and disgust). Secondly, sentences not affected by noise data from Other label gives better results. To conclude, Other label does affect the performance of these algorithms. This will be our focus in building data in the future. Apart from that, we evaluate the learning curves of the four models proposed with the original dataset (the seven labels dataset) in which Random Forest and SVM utilize BoW feature, CNN and LSTM utilize word2vec embeddings. To conduct this experiment, we keep the test and the validation sets while putting training set in stages from 2,000 with 500 sentence-jump until the end of the set (5,548 sentences). + +\subsection{Error Analysis} + +\begin{figure}[htb] +\centering +\includegraphics[width=7cm]{./images/foo.pdf} +\caption{Learning curves of the classification models on the UIT-VSMEC corpus.} +\label{fig:3} +\end{figure} + +As can be seen in Figure \ref{fig:3}, when the size of the training set increases, so does the weighted F1-scores of the four models despite the slightly drop of 0.092\% in Random Forest when the number of the set grows from 5,000 to 5,500 sentences. In the meanwhile, compared to LSTM, the two deep learning models reach significant higher results, principally CNN combined with word2vec. Thus, we take this point to continue expanding the corpus as well as improving the performance of these models. + +%%--> Bỏ ma trận confusion matrix nha +To demonstrate the performance of classification models, we use confusion matrix to visualize the ambiguity between actual labels and predicted labels. +Figure \ref{fig3} is the confusion matrix of the best classification model (CNN + word2vec) on the UIT-VSMEC corpus. As can be seen, the model performs well on classifying enjoyment, fear and sadness labels while it confuses between anger and disgust labels as their ambiguities are at the highest percentage of 39.1\%. There are two reasons causing this confusion. Primarily, it is the inherent vagueness of the definitions of anger and disgust construed through \cite{Facial2007}. Secondly, the data limitation of these labels is an interference for the model to execute at its best. We noted this in our next step to continue building a thorough corpus for the task. +\begin{figure}[htb] +\centering +\includegraphics[width=8cm]{./images/con_matrix.png} +\caption{Confusion matrix of the best classification model on the UIT-VSMEC corpus.} +\label{fig3} +\end{figure} \ No newline at end of file diff --git a/references/2019.arxiv.ho/source/sections/6-conclusion.tex b/references/2019.arxiv.ho/source/sections/6-conclusion.tex new file mode 100644 index 0000000000000000000000000000000000000000..1a7524b2f9b350a2943850830fa0de4684f7cc0b --- /dev/null +++ b/references/2019.arxiv.ho/source/sections/6-conclusion.tex @@ -0,0 +1,4 @@ +\section{Conclusion and Future Work} \label{conclusion} +In this study, we built a human-annotated corpus for emotion recognition for Vietnamese social media text for research purpose and achieved 6,927 sentences annotated with one of the seven emotion labels namely enjoyment, sadness, anger, surprise, fear, disgust and other with the annotation agreement of over 82\%. We also presented machine learning and deep neural network models used for classifying emotions of Vietnamese social media text. In addition, we reached the best overall weighted F1-score of 59.74\% on the original UIT-VSMEC corpus with CNN using the word2vec word embeddings. + +In the future, we want to improve the quantity as well as the quality of the corpus due to its limitation of comments expressing emotions of anger, fear and surprise. Besides, we aim to conduct experiments using other machine learning models with distinctive features as well as deep learning models with various word representations or combine both methods on this corpus. diff --git a/references/2019.arxiv.ho/source/splncs04.bst b/references/2019.arxiv.ho/source/splncs04.bst new file mode 100644 index 0000000000000000000000000000000000000000..3be8de3ac7c33fd679d5fd4f6709a6fcc4d21bc2 --- /dev/null +++ b/references/2019.arxiv.ho/source/splncs04.bst @@ -0,0 +1,1548 @@ +%% BibTeX bibliography style `splncs03' +%% +%% BibTeX bibliography style for use with numbered references in +%% Springer Verlag's "Lecture Notes in Computer Science" series. +%% (See Springer's documentation for llncs.cls for +%% more details of the suggested reference format.) Note that this +%% file will not work for author-year style citations. +%% +%% Use \documentclass{llncs} and \bibliographystyle{splncs03}, and cite +%% a reference with (e.g.) \cite{smith77} to get a "[1]" in the text. +%% +%% This file comes to you courtesy of Maurizio "Titto" Patrignani of +%% Dipartimento di Informatica e Automazione Universita' Roma Tre +%% +%% ================================================================================================ +%% This was file `titto-lncs-02.bst' produced on Wed Apr 1, 2009 +%% Edited by hand by titto based on `titto-lncs-01.bst' (see below) +%% +%% CHANGES (with respect to titto-lncs-01.bst): +%% - Removed the call to \urlprefix (thus no "URL" string is added to the output) +%% ================================================================================================ +%% This was file `titto-lncs-01.bst' produced on Fri Aug 22, 2008 +%% Edited by hand by titto based on `titto.bst' (see below) +%% +%% CHANGES (with respect to titto.bst): +%% - Removed the "capitalize" command for editors string "(eds.)" and "(ed.)" +%% - Introduced the functions titto.bbl.pages and titto.bbl.page for journal pages (without "pp.") +%% - Added a new.sentence command to separate with a dot booktitle and series in the inproceedings +%% - Commented all new.block commands before urls and notes (to separate them with a comma) +%% - Introduced the functions titto.bbl.volume for handling journal volumes (without "vol." label) +%% - Used for editors the same name conventions used for authors (see function format.in.ed.booktitle) +%% - Removed a \newblock to avoid long spaces between title and "In: ..." +%% - Added function titto.space.prefix to add a space instead of "~" after the (removed) "vol." label +%% - Added doi +%% ================================================================================================ +%% This was file `titto.bst', +%% generated with the docstrip utility. +%% +%% The original source files were: +%% +%% merlin.mbs (with options: `vonx,nm-rvvc,yr-par,jttl-rm,volp-com,jwdpg,jwdvol,numser,ser-vol,jnm-x,btit-rm,bt-rm,edparxc,bkedcap,au-col,in-col,fin-bare,pp,ed,abr,mth-bare,xedn,jabr,and-com,and-com-ed,xand,url,url-blk,em-x,nfss,') +%% ---------------------------------------- +%% *** Tentative .bst file for Springer LNCS *** +%% +%% Copyright 1994-2007 Patrick W Daly + % =============================================================== + % IMPORTANT NOTICE: + % This bibliographic style (bst) file has been generated from one or + % more master bibliographic style (mbs) files, listed above. + % + % This generated file can be redistributed and/or modified under the terms + % of the LaTeX Project Public License Distributed from CTAN + % archives in directory macros/latex/base/lppl.txt; either + % version 1 of the License, or any later version. + % =============================================================== + % Name and version information of the main mbs file: + % \ProvidesFile{merlin.mbs}[2007/04/24 4.20 (PWD, AO, DPC)] + % For use with BibTeX version 0.99a or later + %------------------------------------------------------------------- + % This bibliography style file is intended for texts in ENGLISH + % This is a numerical citation style, and as such is standard LaTeX. + % It requires no extra package to interface to the main text. + % The form of the \bibitem entries is + % \bibitem{key}... + % Usage of \cite is as follows: + % \cite{key} ==>> [#] + % \cite[chap. 2]{key} ==>> [#, chap. 2] + % where # is a number determined by the ordering in the reference list. + % The order in the reference list is alphabetical by authors. + %--------------------------------------------------------------------- + +ENTRY + { address + author + booktitle + chapter + doi + edition + editor + eid + howpublished + institution + journal + key + month + note + number + organization + pages + publisher + school + series + title + type + url + volume + year + } + {} + { label } +INTEGERS { output.state before.all mid.sentence after.sentence after.block } +FUNCTION {init.state.consts} +{ #0 'before.all := + #1 'mid.sentence := + #2 'after.sentence := + #3 'after.block := +} +STRINGS { s t} +FUNCTION {output.nonnull} +{ 's := + output.state mid.sentence = + { ", " * write$ } + { output.state after.block = + { add.period$ write$ +% newline$ +% "\newblock " write$ % removed for titto-lncs-01 + " " write$ % to avoid long spaces between title and "In: ..." + } + { output.state before.all = + 'write$ + { add.period$ " " * write$ } + if$ + } + if$ + mid.sentence 'output.state := + } + if$ + s +} +FUNCTION {output} +{ duplicate$ empty$ + 'pop$ + 'output.nonnull + if$ +} +FUNCTION {output.check} +{ 't := + duplicate$ empty$ + { pop$ "empty " t * " in " * cite$ * warning$ } + 'output.nonnull + if$ +} +FUNCTION {fin.entry} +{ duplicate$ empty$ + 'pop$ + 'write$ + if$ + newline$ +} + +FUNCTION {new.block} +{ output.state before.all = + 'skip$ + { after.block 'output.state := } + if$ +} +FUNCTION {new.sentence} +{ output.state after.block = + 'skip$ + { output.state before.all = + 'skip$ + { after.sentence 'output.state := } + if$ + } + if$ +} +FUNCTION {add.blank} +{ " " * before.all 'output.state := +} + + +FUNCTION {add.colon} +{ duplicate$ empty$ + 'skip$ + { ":" * add.blank } + if$ +} + +FUNCTION {date.block} +{ + new.block +} + +FUNCTION {not} +{ { #0 } + { #1 } + if$ +} +FUNCTION {and} +{ 'skip$ + { pop$ #0 } + if$ +} +FUNCTION {or} +{ { pop$ #1 } + 'skip$ + if$ +} +STRINGS {z} +FUNCTION {remove.dots} +{ 'z := + "" + { z empty$ not } + { z #1 #1 substring$ + z #2 global.max$ substring$ 'z := + duplicate$ "." = 'pop$ + { * } + if$ + } + while$ +} +FUNCTION {new.block.checka} +{ empty$ + 'skip$ + 'new.block + if$ +} +FUNCTION {new.block.checkb} +{ empty$ + swap$ empty$ + and + 'skip$ + 'new.block + if$ +} +FUNCTION {new.sentence.checka} +{ empty$ + 'skip$ + 'new.sentence + if$ +} +FUNCTION {new.sentence.checkb} +{ empty$ + swap$ empty$ + and + 'skip$ + 'new.sentence + if$ +} +FUNCTION {field.or.null} +{ duplicate$ empty$ + { pop$ "" } + 'skip$ + if$ +} +FUNCTION {emphasize} +{ skip$ } + +FUNCTION {embolden} +{ duplicate$ empty$ +{ pop$ "" } +{ "\textbf{" swap$ * "}" * } +if$ +} +FUNCTION {tie.or.space.prefix} +{ duplicate$ text.length$ #5 < + { "~" } + { " " } + if$ + swap$ +} +FUNCTION {titto.space.prefix} % always introduce a space +{ duplicate$ text.length$ #3 < + { " " } + { " " } + if$ + swap$ +} + + +FUNCTION {capitalize} +{ "u" change.case$ "t" change.case$ } + +FUNCTION {space.word} +{ " " swap$ * " " * } + % Here are the language-specific definitions for explicit words. + % Each function has a name bbl.xxx where xxx is the English word. + % The language selected here is ENGLISH +FUNCTION {bbl.and} +{ "and"} + +FUNCTION {bbl.etal} +{ "et~al." } + +FUNCTION {bbl.editors} +{ "eds." } + +FUNCTION {bbl.editor} +{ "ed." } + +FUNCTION {bbl.edby} +{ "edited by" } + +FUNCTION {bbl.edition} +{ "edn." } + +FUNCTION {bbl.volume} +{ "vol." } + +FUNCTION {titto.bbl.volume} % for handling journals +{ "" } + +FUNCTION {bbl.of} +{ "of" } + +FUNCTION {bbl.number} +{ "no." } + +FUNCTION {bbl.nr} +{ "no." } + +FUNCTION {bbl.in} +{ "in" } + +FUNCTION {bbl.pages} +{ "pp." } + +FUNCTION {bbl.page} +{ "p." } + +FUNCTION {titto.bbl.pages} % for journals +{ "" } + +FUNCTION {titto.bbl.page} % for journals +{ "" } + +FUNCTION {bbl.chapter} +{ "chap." } + +FUNCTION {bbl.techrep} +{ "Tech. Rep." } + +FUNCTION {bbl.mthesis} +{ "Master's thesis" } + +FUNCTION {bbl.phdthesis} +{ "Ph.D. thesis" } + +MACRO {jan} {"Jan."} + +MACRO {feb} {"Feb."} + +MACRO {mar} {"Mar."} + +MACRO {apr} {"Apr."} + +MACRO {may} {"May"} + +MACRO {jun} {"Jun."} + +MACRO {jul} {"Jul."} + +MACRO {aug} {"Aug."} + +MACRO {sep} {"Sep."} + +MACRO {oct} {"Oct."} + +MACRO {nov} {"Nov."} + +MACRO {dec} {"Dec."} + +MACRO {acmcs} {"ACM Comput. Surv."} + +MACRO {acta} {"Acta Inf."} + +MACRO {cacm} {"Commun. ACM"} + +MACRO {ibmjrd} {"IBM J. Res. Dev."} + +MACRO {ibmsj} {"IBM Syst.~J."} + +MACRO {ieeese} {"IEEE Trans. Software Eng."} + +MACRO {ieeetc} {"IEEE Trans. Comput."} + +MACRO {ieeetcad} + {"IEEE Trans. Comput. Aid. Des."} + +MACRO {ipl} {"Inf. Process. Lett."} + +MACRO {jacm} {"J.~ACM"} + +MACRO {jcss} {"J.~Comput. Syst. Sci."} + +MACRO {scp} {"Sci. Comput. Program."} + +MACRO {sicomp} {"SIAM J. Comput."} + +MACRO {tocs} {"ACM Trans. Comput. Syst."} + +MACRO {tods} {"ACM Trans. Database Syst."} + +MACRO {tog} {"ACM Trans. Graphic."} + +MACRO {toms} {"ACM Trans. Math. Software"} + +MACRO {toois} {"ACM Trans. Office Inf. Syst."} + +MACRO {toplas} {"ACM Trans. Progr. Lang. Syst."} + +MACRO {tcs} {"Theor. Comput. Sci."} + +FUNCTION {bibinfo.check} +{ swap$ + duplicate$ missing$ + { + pop$ pop$ + "" + } + { duplicate$ empty$ + { + swap$ pop$ + } + { swap$ + pop$ + } + if$ + } + if$ +} +FUNCTION {bibinfo.warn} +{ swap$ + duplicate$ missing$ + { + swap$ "missing " swap$ * " in " * cite$ * warning$ pop$ + "" + } + { duplicate$ empty$ + { + swap$ "empty " swap$ * " in " * cite$ * warning$ + } + { swap$ + pop$ + } + if$ + } + if$ +} +FUNCTION {format.url} +{ url empty$ + { "" } +% { "\urlprefix\url{" url * "}" * } + { "\url{" url * "}" * } % changed in titto-lncs-02.bst + if$ +} + +FUNCTION {format.doi} % added in splncs04.bst +{ doi empty$ + { "" } + { after.block 'output.state := + "\doi{" doi * "}" * } + if$ +} + +INTEGERS { nameptr namesleft numnames } + + +STRINGS { bibinfo} + +FUNCTION {format.names} +{ 'bibinfo := + duplicate$ empty$ 'skip$ { + 's := + "" 't := + #1 'nameptr := + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + "{vv~}{ll}{, jj}{, f{.}.}" + format.name$ + bibinfo bibinfo.check + 't := + nameptr #1 > + { + namesleft #1 > + { ", " * t * } + { + s nameptr "{ll}" format.name$ duplicate$ "others" = + { 't := } + { pop$ } + if$ + "," * + t "others" = + { + " " * bbl.etal * + } + { " " * t * } + if$ + } + if$ + } + 't + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ + } if$ +} +FUNCTION {format.names.ed} +{ + 'bibinfo := + duplicate$ empty$ 'skip$ { + 's := + "" 't := + #1 'nameptr := + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + "{f{.}.~}{vv~}{ll}{ jj}" + format.name$ + bibinfo bibinfo.check + 't := + nameptr #1 > + { + namesleft #1 > + { ", " * t * } + { + s nameptr "{ll}" format.name$ duplicate$ "others" = + { 't := } + { pop$ } + if$ + "," * + t "others" = + { + + " " * bbl.etal * + } + { " " * t * } + if$ + } + if$ + } + 't + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ + } if$ +} +FUNCTION {format.authors} +{ author "author" format.names +} +FUNCTION {get.bbl.editor} +{ editor num.names$ #1 > 'bbl.editors 'bbl.editor if$ } + +FUNCTION {format.editors} +{ editor "editor" format.names duplicate$ empty$ 'skip$ + { + " " * + get.bbl.editor +% capitalize + "(" swap$ * ")" * + * + } + if$ +} +FUNCTION {format.note} +{ + note empty$ + { "" } + { note #1 #1 substring$ + duplicate$ "{" = + 'skip$ + { output.state mid.sentence = + { "l" } + { "u" } + if$ + change.case$ + } + if$ + note #2 global.max$ substring$ * "note" bibinfo.check + } + if$ +} + +FUNCTION {format.title} +{ title + duplicate$ empty$ 'skip$ + { "t" change.case$ } + if$ + "title" bibinfo.check +} +FUNCTION {output.bibitem} +{ newline$ + "\bibitem{" write$ + cite$ write$ + "}" write$ + newline$ + "" + before.all 'output.state := +} + +FUNCTION {n.dashify} +{ + 't := + "" + { t empty$ not } + { t #1 #1 substring$ "-" = + { t #1 #2 substring$ "--" = not + { "--" * + t #2 global.max$ substring$ 't := + } + { { t #1 #1 substring$ "-" = } + { "-" * + t #2 global.max$ substring$ 't := + } + while$ + } + if$ + } + { t #1 #1 substring$ * + t #2 global.max$ substring$ 't := + } + if$ + } + while$ +} + +FUNCTION {word.in} +{ bbl.in capitalize + ":" * + " " * } + +FUNCTION {format.date} +{ + month "month" bibinfo.check + duplicate$ empty$ + year "year" bibinfo.check duplicate$ empty$ + { swap$ 'skip$ + { "there's a month but no year in " cite$ * warning$ } + if$ + * + } + { swap$ 'skip$ + { + swap$ + " " * swap$ + } + if$ + * + remove.dots + } + if$ + duplicate$ empty$ + 'skip$ + { + before.all 'output.state := + " (" swap$ * ")" * + } + if$ +} +FUNCTION {format.btitle} +{ title "title" bibinfo.check + duplicate$ empty$ 'skip$ + { + } + if$ +} +FUNCTION {either.or.check} +{ empty$ + 'pop$ + { "can't use both " swap$ * " fields in " * cite$ * warning$ } + if$ +} +FUNCTION {format.bvolume} +{ volume empty$ + { "" } + { bbl.volume volume tie.or.space.prefix + "volume" bibinfo.check * * + series "series" bibinfo.check + duplicate$ empty$ 'pop$ + { emphasize ", " * swap$ * } + if$ + "volume and number" number either.or.check + } + if$ +} +FUNCTION {format.number.series} +{ volume empty$ + { number empty$ + { series field.or.null } + { output.state mid.sentence = + { bbl.number } + { bbl.number capitalize } + if$ + number tie.or.space.prefix "number" bibinfo.check * * + series empty$ + { "there's a number but no series in " cite$ * warning$ } + { bbl.in space.word * + series "series" bibinfo.check * + } + if$ + } + if$ + } + { "" } + if$ +} + +FUNCTION {format.edition} +{ edition duplicate$ empty$ 'skip$ + { + output.state mid.sentence = + { "l" } + { "t" } + if$ change.case$ + "edition" bibinfo.check + " " * bbl.edition * + } + if$ +} +INTEGERS { multiresult } +FUNCTION {multi.page.check} +{ 't := + #0 'multiresult := + { multiresult not + t empty$ not + and + } + { t #1 #1 substring$ + duplicate$ "-" = + swap$ duplicate$ "," = + swap$ "+" = + or or + { #1 'multiresult := } + { t #2 global.max$ substring$ 't := } + if$ + } + while$ + multiresult +} +FUNCTION {format.pages} +{ pages duplicate$ empty$ 'skip$ + { duplicate$ multi.page.check + { + bbl.pages swap$ + n.dashify + } + { + bbl.page swap$ + } + if$ + tie.or.space.prefix + "pages" bibinfo.check + * * + } + if$ +} +FUNCTION {format.journal.pages} +{ pages duplicate$ empty$ 'pop$ + { swap$ duplicate$ empty$ + { pop$ pop$ format.pages } + { + ", " * + swap$ + n.dashify + pages multi.page.check + 'titto.bbl.pages + 'titto.bbl.page + if$ + swap$ tie.or.space.prefix + "pages" bibinfo.check + * * + * + } + if$ + } + if$ +} +FUNCTION {format.journal.eid} +{ eid "eid" bibinfo.check + duplicate$ empty$ 'pop$ + { swap$ duplicate$ empty$ 'skip$ + { + ", " * + } + if$ + swap$ * + } + if$ +} +FUNCTION {format.vol.num.pages} % this function is used only for journal entries +{ volume field.or.null embolden + duplicate$ empty$ 'skip$ + { +% bbl.volume swap$ tie.or.space.prefix + titto.bbl.volume swap$ titto.space.prefix +% rationale for the change above: for journals you don't want "vol." label +% hence it does not make sense to attach the journal number to the label when +% it is short + "volume" bibinfo.check + * * + } + if$ + number "number" bibinfo.check duplicate$ empty$ 'skip$ + { + swap$ duplicate$ empty$ + { "there's a number but no volume in " cite$ * warning$ } + 'skip$ + if$ + swap$ + "(" swap$ * ")" * + } + if$ * + eid empty$ + { format.journal.pages } + { format.journal.eid } + if$ +} + +FUNCTION {format.chapter.pages} +{ chapter empty$ + 'format.pages + { type empty$ + { bbl.chapter } + { type "l" change.case$ + "type" bibinfo.check + } + if$ + chapter tie.or.space.prefix + "chapter" bibinfo.check + * * + pages empty$ + 'skip$ + { ", " * format.pages * } + if$ + } + if$ +} + +FUNCTION {format.booktitle} +{ + booktitle "booktitle" bibinfo.check +} +FUNCTION {format.in.ed.booktitle} +{ format.booktitle duplicate$ empty$ 'skip$ + { +% editor "editor" format.names.ed duplicate$ empty$ 'pop$ % changed by titto + editor "editor" format.names duplicate$ empty$ 'pop$ + { + " " * + get.bbl.editor +% capitalize + "(" swap$ * ") " * + * swap$ + * } + if$ + word.in swap$ * + } + if$ +} +FUNCTION {empty.misc.check} +{ author empty$ title empty$ howpublished empty$ + month empty$ year empty$ note empty$ + and and and and and + key empty$ not and + { "all relevant fields are empty in " cite$ * warning$ } + 'skip$ + if$ +} +FUNCTION {format.thesis.type} +{ type duplicate$ empty$ + 'pop$ + { swap$ pop$ + "t" change.case$ "type" bibinfo.check + } + if$ +} +FUNCTION {format.tr.number} +{ number "number" bibinfo.check + type duplicate$ empty$ + { pop$ bbl.techrep } + 'skip$ + if$ + "type" bibinfo.check + swap$ duplicate$ empty$ + { pop$ "t" change.case$ } + { tie.or.space.prefix * * } + if$ +} +FUNCTION {format.article.crossref} +{ + key duplicate$ empty$ + { pop$ + journal duplicate$ empty$ + { "need key or journal for " cite$ * " to crossref " * crossref * warning$ } + { "journal" bibinfo.check emphasize word.in swap$ * } + if$ + } + { word.in swap$ * " " *} + if$ + " \cite{" * crossref * "}" * +} +FUNCTION {format.crossref.editor} +{ editor #1 "{vv~}{ll}" format.name$ + "editor" bibinfo.check + editor num.names$ duplicate$ + #2 > + { pop$ + "editor" bibinfo.check + " " * bbl.etal + * + } + { #2 < + 'skip$ + { editor #2 "{ff }{vv }{ll}{ jj}" format.name$ "others" = + { + "editor" bibinfo.check + " " * bbl.etal + * + } + { + bbl.and space.word + * editor #2 "{vv~}{ll}" format.name$ + "editor" bibinfo.check + * + } + if$ + } + if$ + } + if$ +} +FUNCTION {format.book.crossref} +{ volume duplicate$ empty$ + { "empty volume in " cite$ * "'s crossref of " * crossref * warning$ + pop$ word.in + } + { bbl.volume + capitalize + swap$ tie.or.space.prefix "volume" bibinfo.check * * bbl.of space.word * + } + if$ + editor empty$ + editor field.or.null author field.or.null = + or + { key empty$ + { series empty$ + { "need editor, key, or series for " cite$ * " to crossref " * + crossref * warning$ + "" * + } + { series emphasize * } + if$ + } + { key * } + if$ + } + { format.crossref.editor * } + if$ + " \cite{" * crossref * "}" * +} +FUNCTION {format.incoll.inproc.crossref} +{ + editor empty$ + editor field.or.null author field.or.null = + or + { key empty$ + { format.booktitle duplicate$ empty$ + { "need editor, key, or booktitle for " cite$ * " to crossref " * + crossref * warning$ + } + { word.in swap$ * } + if$ + } + { word.in key * " " *} + if$ + } + { word.in format.crossref.editor * " " *} + if$ + " \cite{" * crossref * "}" * +} +FUNCTION {format.org.or.pub} +{ 't := + "" + address empty$ t empty$ and + 'skip$ + { + t empty$ + { address "address" bibinfo.check * + } + { t * + address empty$ + 'skip$ + { ", " * address "address" bibinfo.check * } + if$ + } + if$ + } + if$ +} +FUNCTION {format.publisher.address} +{ publisher "publisher" bibinfo.warn format.org.or.pub +} + +FUNCTION {format.organization.address} +{ organization "organization" bibinfo.check format.org.or.pub +} + +FUNCTION {article} +{ output.bibitem + format.authors "author" output.check + add.colon + new.block + format.title "title" output.check + new.block + crossref missing$ + { + journal + "journal" bibinfo.check + "journal" output.check + add.blank + format.vol.num.pages output + format.date "year" output.check + } + { format.article.crossref output.nonnull + format.pages output + } + if$ +% new.block + format.doi output + format.url output +% new.block + format.note output + fin.entry +} +FUNCTION {book} +{ output.bibitem + author empty$ + { format.editors "author and editor" output.check + add.colon + } + { format.authors output.nonnull + add.colon + crossref missing$ + { "author and editor" editor either.or.check } + 'skip$ + if$ + } + if$ + new.block + format.btitle "title" output.check + crossref missing$ + { format.bvolume output + new.block + new.sentence + format.number.series output + format.publisher.address output + } + { + new.block + format.book.crossref output.nonnull + } + if$ + format.edition output + format.date "year" output.check +% new.block + format.doi output + format.url output +% new.block + format.note output + fin.entry +} +FUNCTION {booklet} +{ output.bibitem + format.authors output + add.colon + new.block + format.title "title" output.check + new.block + howpublished "howpublished" bibinfo.check output + address "address" bibinfo.check output + format.date output +% new.block + format.doi output + format.url output +% new.block + format.note output + fin.entry +} + +FUNCTION {inbook} +{ output.bibitem + author empty$ + { format.editors "author and editor" output.check + add.colon + } + { format.authors output.nonnull + add.colon + crossref missing$ + { "author and editor" editor either.or.check } + 'skip$ + if$ + } + if$ + new.block + format.btitle "title" output.check + crossref missing$ + { + format.bvolume output + format.chapter.pages "chapter and pages" output.check + new.block + new.sentence + format.number.series output + format.publisher.address output + } + { + format.chapter.pages "chapter and pages" output.check + new.block + format.book.crossref output.nonnull + } + if$ + format.edition output + format.date "year" output.check +% new.block + format.doi output + format.url output +% new.block + format.note output + fin.entry +} + +FUNCTION {incollection} +{ output.bibitem + format.authors "author" output.check + add.colon + new.block + format.title "title" output.check + new.block + crossref missing$ + { format.in.ed.booktitle "booktitle" output.check + format.bvolume output + format.chapter.pages output + new.sentence + format.number.series output + format.publisher.address output + format.edition output + format.date "year" output.check + } + { format.incoll.inproc.crossref output.nonnull + format.chapter.pages output + } + if$ +% new.block + format.doi output + format.url output +% new.block + format.note output + fin.entry +} +FUNCTION {inproceedings} +{ output.bibitem + format.authors "author" output.check + add.colon + new.block + format.title "title" output.check + new.block + crossref missing$ + { format.in.ed.booktitle "booktitle" output.check + new.sentence % added by titto + format.bvolume output + format.pages output + new.sentence + format.number.series output + publisher empty$ + { format.organization.address output } + { organization "organization" bibinfo.check output + format.publisher.address output + } + if$ + format.date "year" output.check + } + { format.incoll.inproc.crossref output.nonnull + format.pages output + } + if$ +% new.block + format.doi output + format.url output +% new.block + format.note output + fin.entry +} +FUNCTION {conference} { inproceedings } +FUNCTION {manual} +{ output.bibitem + author empty$ + { organization "organization" bibinfo.check + duplicate$ empty$ 'pop$ + { output + address "address" bibinfo.check output + } + if$ + } + { format.authors output.nonnull } + if$ + add.colon + new.block + format.btitle "title" output.check + author empty$ + { organization empty$ + { + address new.block.checka + address "address" bibinfo.check output + } + 'skip$ + if$ + } + { + organization address new.block.checkb + organization "organization" bibinfo.check output + address "address" bibinfo.check output + } + if$ + format.edition output + format.date output +% new.block + format.doi output + format.url output +% new.block + format.note output + fin.entry +} + +FUNCTION {mastersthesis} +{ output.bibitem + format.authors "author" output.check + add.colon + new.block + format.btitle + "title" output.check + new.block + bbl.mthesis format.thesis.type output.nonnull + school "school" bibinfo.warn output + address "address" bibinfo.check output + format.date "year" output.check +% new.block + format.doi output + format.url output +% new.block + format.note output + fin.entry +} + +FUNCTION {misc} +{ output.bibitem + format.authors output + add.colon + title howpublished new.block.checkb + format.title output + howpublished new.block.checka + howpublished "howpublished" bibinfo.check output + format.date output +% new.block + format.doi output + format.url output +% new.block + format.note output + fin.entry + empty.misc.check +} +FUNCTION {phdthesis} +{ output.bibitem + format.authors "author" output.check + add.colon + new.block + format.btitle + "title" output.check + new.block + bbl.phdthesis format.thesis.type output.nonnull + school "school" bibinfo.warn output + address "address" bibinfo.check output + format.date "year" output.check +% new.block + format.doi output + format.url output +% new.block + format.note output + fin.entry +} + +FUNCTION {proceedings} +{ output.bibitem + editor empty$ + { organization "organization" bibinfo.check output + } + { format.editors output.nonnull } + if$ + add.colon + new.block + format.btitle "title" output.check + format.bvolume output + editor empty$ + { publisher empty$ + { format.number.series output } + { + new.sentence + format.number.series output + format.publisher.address output + } + if$ + } + { publisher empty$ + { + new.sentence + format.number.series output + format.organization.address output } + { + new.sentence + format.number.series output + organization "organization" bibinfo.check output + format.publisher.address output + } + if$ + } + if$ + format.date "year" output.check +% new.block + format.doi output + format.url output +% new.block + format.note output + fin.entry +} + +FUNCTION {techreport} +{ output.bibitem + format.authors "author" output.check + add.colon + new.block + format.title + "title" output.check + new.block + format.tr.number output.nonnull + institution "institution" bibinfo.warn output + address "address" bibinfo.check output + format.date "year" output.check +% new.block + format.doi output + format.url output +% new.block + format.note output + fin.entry +} + +FUNCTION {unpublished} +{ output.bibitem + format.authors "author" output.check + add.colon + new.block + format.title "title" output.check + format.date output +% new.block + format.url output +% new.block + format.note "note" output.check + fin.entry +} + +FUNCTION {default.type} { misc } +READ +FUNCTION {sortify} +{ purify$ + "l" change.case$ +} +INTEGERS { len } +FUNCTION {chop.word} +{ 's := + 'len := + s #1 len substring$ = + { s len #1 + global.max$ substring$ } + 's + if$ +} +FUNCTION {sort.format.names} +{ 's := + #1 'nameptr := + "" + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + "{ll{ }}{ ff{ }}{ jj{ }}" + format.name$ 't := + nameptr #1 > + { + " " * + namesleft #1 = t "others" = and + { "zzzzz" * } + { t sortify * } + if$ + } + { t sortify * } + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ +} + +FUNCTION {sort.format.title} +{ 't := + "A " #2 + "An " #3 + "The " #4 t chop.word + chop.word + chop.word + sortify + #1 global.max$ substring$ +} +FUNCTION {author.sort} +{ author empty$ + { key empty$ + { "to sort, need author or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { author sort.format.names } + if$ +} +FUNCTION {author.editor.sort} +{ author empty$ + { editor empty$ + { key empty$ + { "to sort, need author, editor, or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { editor sort.format.names } + if$ + } + { author sort.format.names } + if$ +} +FUNCTION {author.organization.sort} +{ author empty$ + { organization empty$ + { key empty$ + { "to sort, need author, organization, or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { "The " #4 organization chop.word sortify } + if$ + } + { author sort.format.names } + if$ +} +FUNCTION {editor.organization.sort} +{ editor empty$ + { organization empty$ + { key empty$ + { "to sort, need editor, organization, or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { "The " #4 organization chop.word sortify } + if$ + } + { editor sort.format.names } + if$ +} +FUNCTION {presort} +{ type$ "book" = + type$ "inbook" = + or + 'author.editor.sort + { type$ "proceedings" = + 'editor.organization.sort + { type$ "manual" = + 'author.organization.sort + 'author.sort + if$ + } + if$ + } + if$ + " " + * + year field.or.null sortify + * + " " + * + title field.or.null + sort.format.title + * + #1 entry.max$ substring$ + 'sort.key$ := +} +ITERATE {presort} +SORT +STRINGS { longest.label } +INTEGERS { number.label longest.label.width } +FUNCTION {initialize.longest.label} +{ "" 'longest.label := + #1 'number.label := + #0 'longest.label.width := +} +FUNCTION {longest.label.pass} +{ number.label int.to.str$ 'label := + number.label #1 + 'number.label := + label width$ longest.label.width > + { label 'longest.label := + label width$ 'longest.label.width := + } + 'skip$ + if$ +} +EXECUTE {initialize.longest.label} +ITERATE {longest.label.pass} +FUNCTION {begin.bib} +{ preamble$ empty$ + 'skip$ + { preamble$ write$ newline$ } + if$ + "\begin{thebibliography}{" longest.label * "}" * + write$ newline$ + "\providecommand{\url}[1]{\texttt{#1}}" + write$ newline$ + "\providecommand{\urlprefix}{URL }" + write$ newline$ + "\providecommand{\doi}[1]{https://doi.org/#1}" + write$ newline$ +} +EXECUTE {begin.bib} +EXECUTE {init.state.consts} +ITERATE {call.type$} +FUNCTION {end.bib} +{ newline$ + "\end{thebibliography}" write$ newline$ +} +EXECUTE {end.bib} +%% End of customized bst file +%% +%% End of file `titto.bst'. diff --git a/references/2019.arxiv.liu/paper.md b/references/2019.arxiv.liu/paper.md new file mode 100644 index 0000000000000000000000000000000000000000..f9c2ea62bd39fd112020e9e7339163fa3116b946 --- /dev/null +++ b/references/2019.arxiv.liu/paper.md @@ -0,0 +1,34 @@ +--- +title: "RoBERTa: A Robustly Optimized BERT Pretraining Approach" +authors: + - "Yinhan Liu" + - "Myle Ott" + - "Naman Goyal" + - "Jingfei Du" + - "Mandar Joshi" + - "Danqi Chen" + - "Omer Levy" + - "Mike Lewis" + - "Luke Zettlemoyer" + - "Veselin Stoyanov" +year: 2019 +venue: "arXiv" +url: "https://arxiv.org/abs/1907.11692" +arxiv: "1907.11692" +--- + +\maketitle + +\input{00-abstract.tex} +\input{01-intro.tex} +\input{02-background.tex} +\input{03-exp_setup.tex} +\input{04-design.tex} +\input{05-roberta.tex} +\input{06-related_work.tex} +\input{07-conclusion.tex} + +\bibliography{main} +\bibliographystyle{acl_natbib} + +\input{08-appendix.tex} \ No newline at end of file diff --git a/references/2019.arxiv.liu/paper.pdf b/references/2019.arxiv.liu/paper.pdf new file mode 100644 index 0000000000000000000000000000000000000000..8d3ee28a5926bf58fa013d649f967e82500bf2c0 --- /dev/null +++ b/references/2019.arxiv.liu/paper.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:76a3872d244793563a5b000b818cbaf0ca8972ab1145d32be474c05b2a8f3070 +size 209675 diff --git a/references/2019.arxiv.liu/paper.tex b/references/2019.arxiv.liu/paper.tex new file mode 100644 index 0000000000000000000000000000000000000000..82e114461648ece5de7ae182755a4a8818262ecb --- /dev/null +++ b/references/2019.arxiv.liu/paper.tex @@ -0,0 +1,83 @@ +\documentclass[11pt]{article} +\PassOptionsToPackage{hyphens}{url}\usepackage[hyperref]{acl2019} +\usepackage{times} +\aclfinalcopy +\usepackage{latexsym} +\usepackage{xcolor} +\usepackage{graphicx} +\usepackage{amsmath} +\usepackage{siunitx} +\usepackage{booktabs} +\usepackage{multirow} +\newcommand*{\round}[1]{\num[round-mode=places,round-precision=1]{#1}} +\usepackage{arydshln} +\usepackage{enumitem} + +\makeatletter +\def\adl@drawiv#1#2#3{% + \hskip.5\tabcolsep + \xleaders#3{#2.5\@tempdimb #1{1}#2.5\@tempdimb}% + #2\z@ plus1fil minus1fil\relax + \hskip.5\tabcolsep} +\newcommand{\cdashlinelr}[1]{% + \noalign{\vskip\aboverulesep + \global\let\@dashdrawstore\adl@draw + \global\let\adl@draw\adl@drawiv} + \cdashline{#1} + \noalign{\global\let\adl@draw\@dashdrawstore + \vskip\belowrulesep}} +\makeatother + +\newcommand{\ourmodel}{RoBERTa} +\newcommand{\ourmodelbase}{RoBERTa$_{\textsc{base}}$} +\newcommand{\ourmodellarge}{RoBERTa$_{\textsc{large}}$} +\newcommand{\bertbase}{BERT$_{\textsc{base}}$} +\newcommand{\bertlarge}{BERT$_{\textsc{large}}$} +\newcommand{\xlnetbase}{XLNet$_{\textsc{base}}$} +\newcommand{\xlnetlarge}{XLNet$_{\textsc{large}}$} + + +\newcommand{\omer}[1]{\textcolor{blue}{[Omer: #1]}} +\newcommand{\danqi}[1]{\textcolor{magenta}{[Danqi: #1]}} +\newcommand{\mandar}[1]{\textcolor{red}{[Mandar: #1]}} +\newcommand{\ves}[1]{\textcolor{green}{[Ves: #1]}} +\newcommand{\myle}[1]{\textcolor{cyan}{[Myle: #1]}} +\newcommand{\yinhan}[1]{\textcolor{purple}{[Yinhan: #1]}} +\newcommand{\jingfei}[1]{\textcolor{yellow}{[Jingfei: #1]}} +\newcommand{\luke}[1]{\textcolor{orange}{[Luke: #1]}} +\newcommand{\naman}[1]{\textcolor{brown}{[Naman: #1]}} + + +\setlength\titlebox{7cm} + +\title{\ourmodel{}: A Robustly Optimized BERT Pretraining Approach} + +\author{Yinhan Liu\thanks{~~Equal contribution.} $^{\mathsection}$ \quad Myle Ott$^{*\mathsection}$ \quad Naman Goyal$^{* \mathsection}$ \quad Jingfei Du$^{* \mathsection}$ \quad Mandar Joshi$^{\dagger}$\\ +{ \bf Danqi Chen$^{\mathsection}$ \quad Omer Levy$^{\mathsection}$ \quad Mike Lewis$^{\mathsection}$ \quad Luke Zettlemoyer$^{\dagger\mathsection}$\quad Veselin Stoyanov$^{\mathsection}$} \\[8pt] +$^{\dagger}$ Paul G. Allen School of Computer Science \& Engineering, \\ University of Washington, Seattle, WA \\ +{\tt \{mandar90,lsz\}@cs.washington.edu}\\[4pt] +$^{\mathsection}$ Facebook AI \\ +{\tt \{yinhanliu,myleott,naman,jingfeidu,}\\ +{\tt \quad\quad\quad\quad danqi,omerlevy,mikelewis,lsz,ves\}@fb.com} +} +\date{} + +\begin{document} + +\maketitle + +\input{00-abstract.tex} +\input{01-intro.tex} +\input{02-background.tex} +\input{03-exp_setup.tex} +\input{04-design.tex} +\input{05-roberta.tex} +\input{06-related_work.tex} +\input{07-conclusion.tex} + +\bibliography{main} +\bibliographystyle{acl_natbib} + +\input{08-appendix.tex} + +\end{document} diff --git a/references/2019.arxiv.liu/source/00-abstract.tex b/references/2019.arxiv.liu/source/00-abstract.tex new file mode 100644 index 0000000000000000000000000000000000000000..04ec559ac1f07a51748fc0c5dee7023d0c7d4ef4 --- /dev/null +++ b/references/2019.arxiv.liu/source/00-abstract.tex @@ -0,0 +1,9 @@ +\begin{abstract} + +Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. +Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. +We present a replication study of BERT pretraining~\cite{devlin2018bert} that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. +These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code.\footnote{Our models and code are available at: \\ +\url{https://github.com/pytorch/fairseq}} + +\end{abstract} \ No newline at end of file diff --git a/references/2019.arxiv.liu/source/01-intro.tex b/references/2019.arxiv.liu/source/01-intro.tex new file mode 100644 index 0000000000000000000000000000000000000000..9f517c2599d0ba81da693c58dbef39fe46ac150b --- /dev/null +++ b/references/2019.arxiv.liu/source/01-intro.tex @@ -0,0 +1,18 @@ +\section{Introduction} +\label{intro} + +Self-training methods such as ELMo~\cite{peters2018deep}, GPT~\cite{radford2018gpt}, BERT \cite{devlin2018bert}, XLM~\cite{lample2019cross}, and XLNet \cite{yang2019xlnet} have brought significant performance gains, but it can be challenging to determine which aspects of the methods contribute the most. % +Training is computationally expensive, limiting the amount of tuning that can be done, and is often done with private training data of varying sizes, limiting our ability to measure the effects of the modeling advances. + + +We present a replication study of BERT pretraining~\cite{devlin2018bert}, which includes a careful evaluation of the effects of hyperparmeter tuning and training set size. % +We find that BERT was significantly undertrained and propose an improved recipe for training BERT models, which we call \ourmodel{}, that can match or exceed the performance of all of the post-BERT methods. +Our modifications are simple, they include: (1) training the model longer, with bigger batches, over more data; (2) removing the next sentence prediction objective; (3) training on longer sequences; and (4) dynamically changing the masking pattern applied to the training data. We also collect a large new dataset (\textsc{CC-News}) of comparable size to other privately used datasets, to better control for training set size effects. + +When controlling for training data, our improved training procedure improves upon the published BERT results on both GLUE and SQuAD. +When trained for longer over additional data, our model achieves a score of 88.5 on the public GLUE leaderboard, matching the 88.4 reported by \newcite{yang2019xlnet}. +Our model establishes a new state-of-the-art on 4/9 of the GLUE tasks: MNLI, QNLI, RTE and STS-B. +We also match state-of-the-art results on SQuAD and RACE. +Overall, we re-establish that BERT's masked language model training objective is competitive with other recently proposed training objectives such as perturbed autoregressive language modeling~\cite{yang2019xlnet}.\footnote{It is possible that these other methods could also improve with more tuning. We leave this exploration to future work.} + +In summary, the contributions of this paper are: (1) We present a set of important BERT design choices and training strategies and introduce alternatives that lead to better downstream task performance; (2) We use a novel dataset, \textsc{CC-News}, and confirm that using more data for pretraining further improves performance on downstream tasks; (3) Our training improvements show that masked language model pretraining, under the right design choices, is competitive with all other recently published methods. We release our model, pretraining and fine-tuning code implemented in PyTorch~\cite{paszke2017automatic}. \ No newline at end of file diff --git a/references/2019.arxiv.liu/source/02-background.tex b/references/2019.arxiv.liu/source/02-background.tex new file mode 100644 index 0000000000000000000000000000000000000000..02011a4e20f3bb3838d74fdfe5fdc9f023bb5526 --- /dev/null +++ b/references/2019.arxiv.liu/source/02-background.tex @@ -0,0 +1,42 @@ +\section{Background} \label{sec:background} + +In this section, we give a brief overview of the BERT~\cite{devlin2018bert} pretraining approach and some of the training choices that we will examine experimentally in the following section. + +\subsection{Setup} +BERT takes as input a concatenation of two segments (sequences of tokens), $x_1 , \ldots , x_N$ and $y_1 , \ldots , y_M$. +Segments usually consist of more than one natural sentence. +The two segments are presented as a single input sequence to BERT with special tokens delimiting them: $[\mathit{CLS}], x_1 , \ldots , x_N, [\mathit{SEP}], y_1 , \ldots , y_M, [\mathit{EOS}]$. +$M$ and $N$ are constrained such that $M + N < T$, where $T$ is a parameter that controls the maximum sequence length during training. + +The model is first pretrained on a large unlabeled text corpus and subsequently finetuned using end-task labeled data. + +\subsection{Architecture} +BERT uses the now ubiquitous transformer architecture \cite{vaswani2017attention}, which we will not review in detail. We use a transformer architecture with $L$ layers. Each block uses $A$ self-attention heads and hidden dimension $H$. + +\subsection{Training Objectives} + +During pretraining, BERT uses two objectives: masked language modeling and next sentence prediction. + +\paragraph{Masked Language Model (MLM)} A random sample of the tokens in the input sequence is selected and replaced with the special token $[\mathit{MASK}]$. The MLM objective is a cross-entropy loss on predicting the masked tokens. BERT uniformly selects 15\% of the input tokens for possible replacement. Of the selected tokens, 80\% are replaced with $[\mathit{MASK}]$, 10\% are left unchanged, and 10\% are replaced by a randomly selected vocabulary token. + +In the original implementation, random masking and replacement is performed once in the beginning and saved for the duration of training, although in practice, data is duplicated so the mask is not always the same for every training sentence (see Section \ref{sec:dynamic_masking}). + +\paragraph{Next Sentence Prediction (NSP)} NSP is a binary classification loss for predicting whether two segments follow each other in the original text. Positive examples are created by taking consecutive sentences from the text corpus. Negative examples are created by pairing segments from different documents. Positive and negative examples are sampled with equal probability. + +The NSP objective was designed to improve performance on downstream tasks, such as Natural Language Inference \cite{bowman2015large}, which require reasoning about the relationships between pairs of sentences. + + + + + + + + +\subsection{Optimization} + +BERT is optimized with Adam \cite{kingma2014adam} using the following parameters: $\beta_1 = 0.9$, $\beta_2= 0.999$, $\epsilon = \text{1e-6}$ and $L_2$ weight decay of $0.01$. The learning rate is warmed up over the first 10,000 steps to a peak value of 1e-4, and then linearly decayed. BERT trains with a dropout of 0.1 on all layers and attention weights, and a GELU activation function~\cite{hendrycks2016gelu}. Models are pretrained for $S=\text{1,000,000}$ updates, with minibatches containing $B=\text{256}$ sequences of maximum length $T=\text{512}$ tokens. + +\subsection{Data} + +BERT is trained on a combination of \textsc{BookCorpus}~\cite{moviebook} plus English \textsc{Wikipedia}, which totals 16GB of uncompressed text.\footnote{\newcite{yang2019xlnet} use the same dataset but report having only 13GB of text after data cleaning. This is most likely due to subtle differences in cleaning of the Wikipedia data.} + diff --git a/references/2019.arxiv.liu/source/03-exp_setup.tex b/references/2019.arxiv.liu/source/03-exp_setup.tex new file mode 100644 index 0000000000000000000000000000000000000000..f8051b17d395662be83ed26e219ac0e2727953ae --- /dev/null +++ b/references/2019.arxiv.liu/source/03-exp_setup.tex @@ -0,0 +1,63 @@ +\section{Experimental Setup} \label{sec:exp} + +In this section, we describe the experimental setup for our replication study of BERT. + +\subsection{Implementation} \label{sec:implementation} + +We reimplement BERT in \textsc{fairseq}~\cite{ott2019fairseq}. +We primarily follow the original BERT optimization hyperparameters, given in Section~\ref{sec:background}, except for the peak learning rate and number of warmup steps, which are tuned separately for each setting. +We additionally found training to be very sensitive to the Adam epsilon term, and in some cases we obtained better performance or improved stability after tuning it. +Similarly, we found setting $\beta_2 = 0.98$ to improve stability when training with large batch sizes. + +We pretrain with sequences of at most $T=512$ tokens. +Unlike \newcite{devlin2018bert}, we do not randomly inject short sequences, and we do not train with a reduced sequence length for the first 90\% of updates. +We train only with full-length sequences. + +We train with mixed precision floating point arithmetic on DGX-1 machines, each with 8 $\times$ 32GB Nvidia V100 GPUs interconnected by Infiniband~\cite{micikevicius2018mixed}. + +\subsection{Data} \label{sec:data} + +BERT-style pretraining crucially relies on large quantities of text. \newcite{baevski2019cloze} demonstrate that increasing data size can result in improved end-task performance. Several efforts have trained on datasets larger and more diverse than the original BERT~\cite{radford2019language,yang2019xlnet,zellers2019neuralfakenews}. +Unfortunately, not all of the additional datasets can be publicly released. For our study, we focus on gathering as much data as possible for experimentation, allowing us to match the overall quality and quantity of data as appropriate for each comparison. + +We consider five English-language corpora of varying sizes and domains, totaling over 160GB of uncompressed text. We use the following text corpora: +\begin{itemize}[leftmargin=*] +\setlength\itemsep{0em} +\item \textsc{BookCorpus}~\cite{moviebook} plus English \textsc{Wikipedia}. This is the original data used to train BERT. (16GB). +\item \textsc{CC-News}, which we collected from the English portion of the CommonCrawl News dataset~\cite{nagel2016ccnews}. The data contains 63 million English news articles crawled between September 2016 and February 2019. (76GB after filtering).\footnote{We use \texttt{news-please}~\cite{hamborg2017newsplease} to collect and extract \textsc{CC-News}. \textsc{CC-News} is similar to the \textsc{RealNews} dataset described in~\newcite{zellers2019neuralfakenews}.} +\item \textsc{OpenWebText}~\cite{gokaslan2019openwebtext}, an open-source recreation of the WebText corpus described in~\newcite{radford2019language}. The text is web content extracted from URLs shared on Reddit with at least three upvotes. (38GB).\footnote{The authors and their affiliated institutions are not in any way affiliated with the creation of the OpenWebText dataset.} +\item \textsc{Stories}, a dataset introduced in~\newcite{trinh2018simple} containing a subset of CommonCrawl data filtered to match the story-like style of Winograd schemas. (31GB). +\end{itemize} + + + + + +\subsection{Evaluation} \label{sec:evaluation} + +Following previous work, we evaluate our pretrained models on downstream tasks using the following three benchmarks. + +\paragraph{GLUE} \label{sec:glue} + +The General Language Understanding Evaluation (GLUE) benchmark \cite{wang2019glue} is a collection of 9 datasets for evaluating natural language understanding systems.\footnote{The datasets are: CoLA~\cite{warstadt2018neural}, Stanford Sentiment Treebank (SST)~\cite{socher2013recursive}, Microsoft Research Paragraph Corpus (MRPC)~\cite{dolan2005automatically}, Semantic Textual Similarity Benchmark (STS)~\cite{agirre2007semantic}, Quora Question Pairs (QQP)~\cite{iyer2016quora}, Multi-Genre NLI (MNLI)~\cite{williams2018broad}, Question NLI (QNLI)~\cite{rajpurkar2016squad}, Recognizing Textual Entailment (RTE)~\cite{dagan2006pascal,bar2006second,giampiccolo2007third,bentivogli2009fifth} and Winograd NLI (WNLI)~\cite{levesque2011winograd}.} +Tasks are framed as either single-sentence classification or sentence-pair classification tasks. +The GLUE organizers provide training and development data splits as well as a submission server and leaderboard that allows participants to evaluate and compare their systems on private held-out test data. + +For the replication study in Section~\ref{sec:design}, we report results on the development sets after finetuning the pretrained models on the corresponding single-task training data (i.e., without multi-task training or ensembling). +Our finetuning procedure follows the original BERT paper~\cite{devlin2018bert}. + +In Section~\ref{sec:roberta} we additionally report test set results obtained from the public leaderboard. These results depend on a several task-specific modifications, which we describe in Section~\ref{sec:results_glue}. + +\paragraph{SQuAD} \label{sec:squad} +The Stanford Question Answering Dataset (SQuAD) provides a paragraph of context and a question. +The task is to answer the question by extracting the relevant span from the context. +We evaluate on two versions of SQuAD: V1.1 and V2.0~\cite{rajpurkar2016squad,rajpurkar2018know}. +In V1.1 the context always contains an answer, whereas in V2.0 some questions are not answered in the provided context, making the task more challenging. + +For SQuAD V1.1 we adopt the same span prediction method as BERT~\cite{devlin2018bert}. +For SQuAD V2.0, we add an additional binary classifier to predict whether the question is answerable, which we train jointly by summing the classification and span loss terms. +During evaluation, we only predict span indices on pairs that are classified as answerable. + +\paragraph{RACE} \label{sec:race} +The ReAding Comprehension from Examinations (RACE)~\cite{lai2017large} task is a large-scale reading comprehension dataset with more than 28,000 passages and nearly 100,000 questions. The dataset is collected from English examinations in China, which are designed for middle and high school students. In RACE, each passage is associated with multiple questions. For every question, the task is to select one correct answer from four options. RACE has significantly longer context than other popular reading comprehension datasets and the proportion of questions +that requires reasoning is very large. \ No newline at end of file diff --git a/references/2019.arxiv.liu/source/04-design.tex b/references/2019.arxiv.liu/source/04-design.tex new file mode 100644 index 0000000000000000000000000000000000000000..580fa1c4316e573c517e98571186d00a406f5ccf --- /dev/null +++ b/references/2019.arxiv.liu/source/04-design.tex @@ -0,0 +1,102 @@ +\section{Training Procedure Analysis} \label{sec:design} + +This section explores and quantifies which choices are important for successfully pretraining BERT models. +We keep the model architecture fixed.\footnote{Studying architectural changes, including larger architectures, is an important area for future work.} +Specifically, we begin by training BERT models with the same configuration as BERT$_{\textsc{base}}$ ($L=12$, +$H=768$, $A=12$, 110M params). + + + +\subsection{Static vs. Dynamic Masking} \label{sec:dynamic_masking} + +As discussed in Section \ref{sec:background}, BERT relies on randomly masking and predicting tokens. +The original BERT implementation performed masking once during data preprocessing, resulting in a single \emph{static} mask. +To avoid using the same mask for each training instance in every epoch, training data was duplicated 10 times so that each sequence is masked in 10 different ways over the 40 epochs of training. +Thus, each training sequence was seen with the same mask four times during training. + +We compare this strategy with \emph{dynamic masking} where we generate the masking pattern every time we feed a sequence to the model. +This becomes crucial when pretraining for more steps or with larger datasets. + +\paragraph{Results} + +\input{tables/static_vs_dynamic_masking.tex} + +Table~\ref{tab:static_vs_dynamic_masking} compares the published \bertbase{} results from \newcite{devlin2018bert} to our reimplementation with either static or dynamic masking. +We find that our reimplementation with static masking performs similar to the original BERT model, and dynamic masking is comparable or slightly better than static masking. + +Given these results and the additional efficiency benefits of dynamic masking, we use dynamic masking in the remainder of the experiments. + + + +\subsection{Model Input Format and Next Sentence Prediction} \label{sec:model_input_nsp} + +\input{tables/base_apples_to_apples.tex} + +In the original BERT pretraining procedure, the model observes two concatenated document segments, which are either sampled contiguously from the same document (with $p=0.5$) or from distinct documents. +In addition to the masked language modeling objective, the model is trained to predict whether the observed document segments come from the same or distinct documents via an auxiliary Next Sentence Prediction (NSP) loss. + +The NSP loss was hypothesized to be an important factor in training the original BERT model. \newcite{devlin2018bert} observe that removing NSP hurts performance, with significant performance degradation on QNLI, MNLI, and SQuAD 1.1. +However, some recent work has questioned the necessity of the NSP loss~\cite{lample2019cross,yang2019xlnet,joshi2019spanbert}. + + +To better understand this discrepancy, we compare several alternative training formats: +\begin{itemize}[leftmargin=*] +\setlength\itemsep{0em} +\item \textsc{segment-pair+nsp}: This follows the original input format used in BERT~\cite{devlin2018bert}, with the NSP loss. Each input has a pair of segments, which can each contain multiple natural sentences, but the total combined length must be less than 512 tokens. +\item \textsc{sentence-pair+nsp}: Each input contains a pair of natural \emph{sentences}, either sampled from a contiguous portion of one document or from separate documents. Since these inputs are significantly shorter than 512 tokens, we increase the batch size so that the total number of tokens remains similar to \textsc{segment-pair+nsp}. We retain the NSP loss. +\item \textsc{full-sentences}: Each input is packed with full sentences sampled contiguously from one or more documents, such that the total length is at most 512 tokens. Inputs may cross document boundaries. When we reach the end of one document, we begin sampling sentences from the next document and add an extra separator token between documents. We remove the NSP loss. +\item \textsc{doc-sentences}: Inputs are constructed similarly to \textsc{full-sentences}, except that they may not cross document boundaries. Inputs sampled near the end of a document may be shorter than 512 tokens, so we dynamically increase the batch size in these cases to achieve a similar number of total tokens as \textsc{full-sentences}. We remove the NSP loss. +\end{itemize} + +\paragraph{Results} + +Table~\ref{tab:base_apples_to_apples} shows results for the four different settings. +We first compare the original \textsc{segment-pair} input format from \newcite{devlin2018bert} to the \textsc{sentence-pair} format; both formats retain the NSP loss, but the latter uses single sentences. +We find that \textbf{using individual sentences hurts performance on downstream tasks}, which we hypothesize is because the model is not able to learn long-range dependencies. + +We next compare training without the NSP loss and training with blocks of text from a single document (\textsc{doc-sentences}). +We find that this setting outperforms the originally published \bertbase{} results and that \textbf{removing the NSP loss matches or slightly improves downstream task performance}, in contrast to \newcite{devlin2018bert}. +It is possible that the original BERT implementation may only have removed the loss term while still retaining the \textsc{segment-pair} input format. + +Finally we find that restricting sequences to come from a single document (\textsc{doc-sentences}) performs slightly better than packing sequences from multiple documents (\textsc{full-sentences}). +However, because the \textsc{doc-sentences} format results in variable batch sizes, we use \textsc{full-sentences} in the remainder of our experiments for easier comparison with related work. + + +\subsection{Training with large batches} +\label{sec:large_batches} + +Past work in Neural Machine Translation has shown that training with very large mini-batches can both improve optimization speed and end-task performance when the learning rate is increased appropriately~\cite{ott2018scaling}. +Recent work has shown that BERT is also amenable to large batch training~\cite{you2019reducing}. + +\newcite{devlin2018bert} originally trained \bertbase{} for 1M steps with a batch size of 256 sequences. +This is equivalent in computational cost, via gradient accumulation, to training for 125K steps with a batch size of 2K sequences, or for 31K steps with a batch size of 8K. + +\input{tables/large_batches.tex} + +In Table~\ref{tab:large_batches} we compare perplexity and end-task performance of \bertbase{} as we increase the batch size, controlling for the number of passes through the training data. +We observe that training with large batches improves perplexity for the masked language modeling objective, as well as end-task accuracy. +Large batches are also easier to parallelize via distributed data parallel training,\footnote{Large batch training can improve training efficiency even without large scale parallel hardware through \emph{gradient accumulation}, whereby gradients from multiple mini-batches are accumulated locally before each optimization step. This functionality is supported natively in \textsc{fairseq}~\cite{ott2019fairseq}.} and in later experiments we train with batches of 8K sequences. + +Notably \newcite{you2019reducing} train BERT with even larger batche sizes, up to 32K sequences. +We leave further exploration of the limits of large batch training to future work. + + + + +\subsection{Text Encoding} +\label{sec:bpe} + +Byte-Pair Encoding (BPE)~\cite{sennrich2016neural} is a hybrid between character- and word-level representations that allows handling the large vocabularies common in natural language corpora. +Instead of full words, BPE relies on subwords units, which are extracted by performing statistical analysis of the training corpus. + +BPE vocabulary sizes typically range from 10K-100K subword units. However, unicode characters can account for a sizeable portion of this vocabulary when modeling large and diverse corpora, such as the ones considered in this work. +\newcite{radford2019language} introduce a clever implementation of BPE that uses \emph{bytes} instead of unicode characters as the base subword units. +Using bytes makes it possible to learn a subword vocabulary of a modest size (50K units) that can still encode any input text without introducing any ``unknown" tokens. + +The original BERT implementation~\cite{devlin2018bert} uses a character-level BPE vocabulary of size 30K, which is learned after preprocessing the input with heuristic tokenization rules. +Following \newcite{radford2019language}, we instead consider training BERT with a larger byte-level BPE vocabulary containing 50K subword units, without any additional preprocessing or tokenization of the input. +This adds approximately 15M and 20M additional parameters for \bertbase{} and \bertlarge{}, respectively. + +Early experiments revealed only slight differences between these encodings, with the \newcite{radford2019language} BPE achieving slightly worse end-task performance on some tasks. +Nevertheless, we believe the advantages of a universal encoding scheme outweighs the minor degredation in performance and use this encoding in the remainder of our experiments. +A more detailed comparison of these encodings is left to future work. \ No newline at end of file diff --git a/references/2019.arxiv.liu/source/05-roberta.tex b/references/2019.arxiv.liu/source/05-roberta.tex new file mode 100644 index 0000000000000000000000000000000000000000..19f45c0396449d697f51ecd33dccf6217dacb32f --- /dev/null +++ b/references/2019.arxiv.liu/source/05-roberta.tex @@ -0,0 +1,114 @@ +\section{\ourmodel{}} \label{sec:roberta} + +\input{tables/ablation.tex} + +In the previous section we propose modifications to the BERT pretraining procedure that improve end-task performance. +We now aggregate these improvements and evaluate their combined impact. +We call this configuration \textbf{\ourmodel{}} for \underline{\textbf{R}}obustly \underline{\textbf{o}}ptimized \underline{\textbf{BERT}} \underline{\textbf{a}}pproach. +Specifically, \ourmodel{} is trained with dynamic masking (Section~\ref{sec:dynamic_masking}), \textsc{full-sentences} without NSP loss (Section~\ref{sec:model_input_nsp}), large mini-batches (Section~\ref{sec:large_batches}) and a larger byte-level BPE (Section~\ref{sec:bpe}). + +Additionally, we investigate two other important factors that have been under-emphasized in previous work: (1) the data used for pretraining, and (2) the number of training passes through the data. +For example, the recently proposed XLNet architecture~\cite{yang2019xlnet} is pretrained using nearly 10 times more data than the original BERT~\cite{devlin2018bert}. +It is also trained with a batch size eight times larger for half as many optimization steps, thus seeing four times as many sequences in pretraining compared to BERT. + +To help disentangle the importance of these factors from other modeling choices (e.g., the pretraining objective), we begin by training \ourmodel{} following the \bertlarge{} architecture ($L=24$, $H=1024$, $A=16$, 355M parameters). +We pretrain for 100K steps over a comparable \textsc{BookCorpus} plus \textsc{Wikipedia} dataset as was used in \newcite{devlin2018bert}. +We pretrain our model using 1024 V100 GPUs for approximately one day. + +\paragraph{Results} + +We present our results in Table~\ref{tab:ablation}. +When controlling for training data, we observe that \ourmodel{} provides a large improvement over the originally reported \bertlarge{} results, reaffirming the importance of the design choices we explored in Section~\ref{sec:design}. + +Next, we combine this data with the three additional datasets described in Section~\ref{sec:data}. +We train \ourmodel{} over the combined data with the same number of training steps as before (100K). +In total, we pretrain over 160GB of text. +We observe further improvements in performance across all downstream tasks, validating the importance of data size and diversity in pretraining.\footnote{Our experiments conflate increases in data size and diversity. We leave a more careful analysis of these two dimensions to future work.} + +\input{tables/roberta_glue.tex} + +Finally, we pretrain \ourmodel{} for significantly longer, increasing the number of pretraining steps from 100K to 300K, and then further to 500K. +We again observe significant gains in downstream task performance, and the 300K and 500K step models outperform \xlnetlarge{} across most tasks. +We note that even our longest-trained model does not appear to overfit our data and would likely benefit from additional training. + +In the rest of the paper, we evaluate our best \ourmodel{} model on the three different benchmarks: GLUE, SQuaD and RACE. +Specifically we consider \ourmodel{} trained for 500K steps over all five of the datasets introduced in Section~\ref{sec:data}. + +\subsection{GLUE Results} \label{sec:results_glue} + +For GLUE we consider two finetuning settings. +In the first setting (\emph{single-task, dev}) we finetune \ourmodel{} separately for each of the GLUE tasks, using only the training data for the corresponding task. +We consider a limited hyperparameter sweep for each task, with batch sizes $\in \{16, 32\}$ and learning rates $\in \{1e-5, 2e-5, 3e-5\}$, with a linear warmup for the first 6\% of steps followed by a linear decay to 0. +We finetune for 10 epochs and perform early stopping based on each task's evaluation metric on the dev set. +The rest of the hyperparameters remain the same as during pretraining. +In this setting, we report the median development set results for each task over five random initializations, without model ensembling. + +In the second setting (\emph{ensembles, test}), we compare \ourmodel{} to other approaches on the test set via the GLUE leaderboard. +While many submissions to the GLUE leaderboard depend on multi-task finetuning, \textbf{our submission depends only on single-task finetuning}. +For RTE, STS and MRPC we found it helpful to finetune starting from the MNLI single-task model, rather than the baseline pretrained \ourmodel{}. +We explore a slightly wider hyperparameter space, described in the Appendix, and ensemble between 5 and 7 models per task. + +\paragraph{Task-specific modifications} + +Two of the GLUE tasks require task-specific finetuning approaches to achieve competitive leaderboard results. + +\underline{QNLI}: +Recent submissions on the GLUE leaderboard adopt a pairwise ranking formulation for the QNLI task, in which candidate answers are mined from the training set and compared to one another, and a single (question, candidate) pair is classified as positive~\cite{liu2019mtdnn,liu2019improving,yang2019xlnet}. +This formulation significantly simplifies the task, but is not directly comparable to BERT~\cite{devlin2018bert}. +Following recent work, we adopt the ranking approach for our test submission, but for direct comparison with BERT we report development set results based on a pure classification approach. + +\underline{WNLI}: We found the provided NLI-format data to be challenging to work with. +Instead we use the reformatted WNLI data from SuperGLUE~\cite{wang2019superglue}, which indicates the span of the query pronoun and referent. +We finetune \ourmodel{} using the margin ranking loss from \newcite{kocijan2019surprisingly}. +For a given input sentence, we use spaCy~\cite{spacy2} to extract additional candidate noun phrases from the sentence and finetune our model so that it assigns higher scores to positive referent phrases than for any of the generated negative candidate phrases. +One unfortunate consequence of this formulation is that we can only make use of the positive training examples, which excludes over half of the provided training examples.\footnote{While we only use the provided WNLI training data, our results could potentially be improved by augmenting this with additional pronoun disambiguation datasets.} + +\paragraph{Results} + +We present our results in Table~\ref{tab:roberta_glue}. +In the first setting (\emph{single-task, dev}), \ourmodel{} achieves state-of-the-art results on all 9 of the GLUE task development sets. +Crucially, \ourmodel{} uses the same masked language modeling pretraining objective and architecture as \bertlarge{}, yet consistently outperforms both \bertlarge{} and \xlnetlarge{}. +This raises questions about the relative importance of model architecture and pretraining objective, compared to more mundane details like dataset size and training time that we explore in this work. + +In the second setting (\emph{ensembles, test}), we submit \ourmodel{} to the GLUE leaderboard and achieve state-of-the-art results on 4 out of 9 tasks and the highest average score to date. +This is especially exciting because \ourmodel{} does not depend on multi-task finetuning, unlike most of the other top submissions. +We expect future work may further improve these results by incorporating more sophisticated multi-task finetuning procedures. + +\subsection{SQuAD Results} \label{sec:results_squad} + +We adopt a much simpler approach for SQuAD compared to past work. +In particular, while both BERT~\cite{devlin2018bert} and XLNet~\cite{yang2019xlnet} augment their training data with additional QA datasets, \textbf{we only finetune \ourmodel{} using the provided SQuAD training data}. +\newcite{yang2019xlnet} also employed a custom layer-wise learning rate schedule to finetune XLNet, while we use the same learning rate for all layers. + +For SQuAD v1.1 we follow the same finetuning procedure as \newcite{devlin2018bert}. +For SQuAD v2.0, we additionally classify whether a given question is answerable; we train this classifier jointly with the span predictor by summing the classification and span loss terms. + +\paragraph{Results} + +\input{tables/roberta_squad.tex} + +We present our results in Table~\ref{tab:roberta_squad}. +On the SQuAD v1.1 development set, \ourmodel{} matches the state-of-the-art set by XLNet. +On the SQuAD v2.0 development set, \ourmodel{} sets a new state-of-the-art, improving over XLNet by 0.4 points (EM) and 0.6 points (F1). + +We also submit \ourmodel{} to the public SQuAD 2.0 leaderboard and evaluate its performance relative to other systems. +Most of the top systems build upon either BERT~\cite{devlin2018bert} or XLNet~\cite{yang2019xlnet}, both of which rely on additional external training data. +In contrast, our submission does not use any additional data. + +Our single \ourmodel{} model outperforms all but one of the single model submissions, and is the top scoring system among those that do not rely on data augmentation. + +\subsection{RACE Results} \label{sec:results_race} + +In RACE, systems are provided with a passage of text, an associated question, and four candidate answers. Systems are required to classify which of the four candidate answers is correct. + +We modify \ourmodel{} for this task by concatenating each candidate answer with the corresponding question and passage. +We then encode each of these four sequences and pass the resulting \emph{[CLS]} representations through a fully-connected layer, which is used to predict the correct answer. +We truncate question-answer pairs that are longer than 128 tokens and, if needed, the passage so that the total length is at most 512 tokens. + + +\input{tables/roberta_race.tex} + +Results on the RACE test sets are presented in Table~\ref{tab:roberta_race}. +\ourmodel{} achieves state-of-the-art results on both middle-school and high-school settings. + + diff --git a/references/2019.arxiv.liu/source/06-related_work.tex b/references/2019.arxiv.liu/source/06-related_work.tex new file mode 100644 index 0000000000000000000000000000000000000000..a86913d68a1540fd4b86e4c75276185f312fe803 --- /dev/null +++ b/references/2019.arxiv.liu/source/06-related_work.tex @@ -0,0 +1,4 @@ +\section{Related Work} \label{sec:relwork} + +Pretraining methods have been designed with different training objectives, including language modeling~\cite{dai2015semi,peters2018deep,howard2018universal}, machine translation~\cite{mccann2017learned}, and masked language modeling~\cite{devlin2018bert,lample2019cross}. Many recent papers have used a basic recipe of finetuning models for each end task~\cite{howard2018universal,radford2018gpt}, and pretraining with some variant of a masked language model objective. However, newer methods have improved performance by multi-task fine tuning~\cite{dong2019unified}, incorporating entity embeddings~\cite{sun2019ernie}, span prediction~\cite{joshi2019spanbert}, and multiple variants of autoregressive pretraining~\cite{song2019mass,chan2019kermit,yang2019xlnet}. Performance is also typically improved by training bigger models on more data~\cite{devlin2018bert,baevski2019cloze,yang2019xlnet,radford2019language}. Our goal was to replicate, simplify, and better tune the training of BERT, as a reference point for better understanding the relative performance of all of these methods. + diff --git a/references/2019.arxiv.liu/source/07-conclusion.tex b/references/2019.arxiv.liu/source/07-conclusion.tex new file mode 100644 index 0000000000000000000000000000000000000000..7052d1546f9c00d2c0a80208cbc7d968f15ee537 --- /dev/null +++ b/references/2019.arxiv.liu/source/07-conclusion.tex @@ -0,0 +1,8 @@ +\section{Conclusion} \label{sec:conclusion} + +We carefully evaluate a number of design decisions when pretraining BERT models. +We find that performance can be substantially improved by training the model longer, with bigger batches over more data; removing the next sentence prediction objective; training on longer sequences; and dynamically changing the masking pattern applied to the training data. +Our improved pretraining procedure, which we call \ourmodel{}, achieves state-of-the-art results on GLUE, RACE and SQuAD, without multi-task finetuning for GLUE or additional data for SQuAD. +These results illustrate the importance of these previously overlooked design decisions and suggest that BERT's pretraining objective remains competitive with recently proposed alternatives. + +We additionally use a novel dataset, \textsc{CC-News}, and release our models and code for pretraining and finetuning at: \url{https://github.com/pytorch/fairseq}. \ No newline at end of file diff --git a/references/2019.arxiv.liu/source/08-appendix.tex b/references/2019.arxiv.liu/source/08-appendix.tex new file mode 100644 index 0000000000000000000000000000000000000000..335a04e9c9fb16ba03411feff6b4749859941eca --- /dev/null +++ b/references/2019.arxiv.liu/source/08-appendix.tex @@ -0,0 +1,25 @@ +\appendix + +\input{tables/roberta_all_large_glue.tex} +\input{tables/pretraining_hyperparams.tex} +\input{tables/roberta_glue_finetune_hyperparams.tex} + +\section*{Appendix for ``RoBERTa: A Robustly Optimized BERT Pretraining Approach"} + +\section{Full results on GLUE} + +In Table~\ref{tab:roberta_all_large_glue} we present the full set of development set results for \ourmodel{}. +We present results for a $\textsc{large}$ configuration that follows \bertlarge{}, as well as a $\textsc{base}$ configuration that follows \bertbase{}. + +\section{Pretraining Hyperparameters} +Table~\ref{tab:pretraining_hyperparams} describes the hyperparameters for pretraining of \ourmodellarge{} and \ourmodelbase{} + +\section{Finetuning Hyperparameters} +\label{app:hyperparams} + +Finetuning hyperparameters for RACE, SQuAD and GLUE are given in Table~\ref{tab:roberta_glue_finetune_hyperparams}. +We select the best hyperparameter values based on the median of 5 random seeds for each task. + + + + diff --git a/references/2019.arxiv.liu/source/Makefile b/references/2019.arxiv.liu/source/Makefile new file mode 100644 index 0000000000000000000000000000000000000000..56885d790e6af157d04e1cc99d4bff93af730757 --- /dev/null +++ b/references/2019.arxiv.liu/source/Makefile @@ -0,0 +1,14 @@ +main.pdf: $(wildcard *.tex) roberta.bib + @pdflatex main + @bibtex main + @pdflatex main + @pdflatex main + +clean: + rm -f *.aux *.log *.bbl *.blg present.pdf *.bak *.ps *.dvi *.lot *.bcf main.pdf + +dist: main.pdf + @pdflatex --file-line-errors main + +default: main.pdf + diff --git a/references/2019.arxiv.liu/source/acl.bst b/references/2019.arxiv.liu/source/acl.bst new file mode 100644 index 0000000000000000000000000000000000000000..d55e905eccb24d1335a823020fe00cea4cd67603 --- /dev/null +++ b/references/2019.arxiv.liu/source/acl.bst @@ -0,0 +1,1320 @@ +% BibTeX `acl' style file for BibTeX version 0.99c, LaTeX version 2.09 +% This version was made by modifying `aaai-named' format based on the master +% file by Oren Patashnik (PATASHNIK@SCORE.STANFORD.EDU) + +% Copyright (C) 1985, all rights reserved. +% Modifications Copyright 1988, Peter F. Patel-Schneider +% Further modifictions by Stuart Shieber, 1991, and Fernando Pereira, 1992. +% Copying of this file is authorized only if either +% (1) you make absolutely no changes to your copy, including name, or +% (2) if you do make changes, you name it something other than +% btxbst.doc, plain.bst, unsrt.bst, alpha.bst, and abbrv.bst. +% This restriction helps ensure that all standard styles are identical. + +% There are undoubtably bugs in this style. If you make bug fixes, +% improvements, etc. please let me know. My e-mail address is: +% pfps@spar.slb.com + +% Citation format: [author-last-name, year] +% [author-last-name and author-last-name, year] +% [author-last-name {\em et al.}, year] +% +% Reference list ordering: alphabetical by author or whatever passes +% for author in the absence of one. +% +% This BibTeX style has support for short (year only) citations. This +% is done by having the citations actually look like +% \citename{name-info, }year +% The LaTeX style has to have the following +% \let\@internalcite\cite +% \def\cite{\def\citename##1{##1}\@internalcite} +% \def\shortcite{\def\citename##1{}\@internalcite} +% \def\@biblabel#1{\def\citename##1{##1}[#1]\hfill} +% which makes \shortcite the macro for short citations. + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Changes made by SMS for thesis style +% no emphasis on "et al." +% "Ph.D." includes periods (not "PhD") +% moved year to immediately after author's name +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +ENTRY + { address + author + booktitle + chapter + edition + editor + howpublished + institution + journal + key + month + note + number + organization + pages + publisher + school + series + title + type + volume + year + } + {} + { label extra.label sort.label } + +INTEGERS { output.state before.all mid.sentence after.sentence after.block } + +FUNCTION {init.state.consts} +{ #0 'before.all := + #1 'mid.sentence := + #2 'after.sentence := + #3 'after.block := +} + +STRINGS { s t } + +FUNCTION {output.nonnull} +{ 's := + output.state mid.sentence = + { ", " * write$ } + { output.state after.block = + { add.period$ write$ + newline$ + "\newblock " write$ + } + { output.state before.all = + 'write$ + { add.period$ " " * write$ } + if$ + } + if$ + mid.sentence 'output.state := + } + if$ + s +} + +FUNCTION {output} +{ duplicate$ empty$ + 'pop$ + 'output.nonnull + if$ +} + +FUNCTION {output.check} +{ 't := + duplicate$ empty$ + { pop$ "empty " t * " in " * cite$ * warning$ } + 'output.nonnull + if$ +} + +FUNCTION {output.bibitem} +{ newline$ + + "\bibitem[" write$ + label write$ + "]{" write$ + + cite$ write$ + "}" write$ + newline$ + "" + before.all 'output.state := +} + +FUNCTION {fin.entry} +{ add.period$ + write$ + newline$ +} + +FUNCTION {new.block} +{ output.state before.all = + 'skip$ + { after.block 'output.state := } + if$ +} + +FUNCTION {new.sentence} +{ output.state after.block = + 'skip$ + { output.state before.all = + 'skip$ + { after.sentence 'output.state := } + if$ + } + if$ +} + +FUNCTION {not} +{ { #0 } + { #1 } + if$ +} + +FUNCTION {and} +{ 'skip$ + { pop$ #0 } + if$ +} + +FUNCTION {or} +{ { pop$ #1 } + 'skip$ + if$ +} + +FUNCTION {new.block.checka} +{ empty$ + 'skip$ + 'new.block + if$ +} + +FUNCTION {new.block.checkb} +{ empty$ + swap$ empty$ + and + 'skip$ + 'new.block + if$ +} + +FUNCTION {new.sentence.checka} +{ empty$ + 'skip$ + 'new.sentence + if$ +} + +FUNCTION {new.sentence.checkb} +{ empty$ + swap$ empty$ + and + 'skip$ + 'new.sentence + if$ +} + +FUNCTION {field.or.null} +{ duplicate$ empty$ + { pop$ "" } + 'skip$ + if$ +} + +FUNCTION {emphasize} +{ duplicate$ empty$ + { pop$ "" } + { "{\em " swap$ * "}" * } + if$ +} + +INTEGERS { nameptr namesleft numnames } + +FUNCTION {format.names} +{ 's := + #1 'nameptr := + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + + { s nameptr "{ff~}{vv~}{ll}{, jj}" format.name$ 't := + + nameptr #1 > + { namesleft #1 > + { ", " * t * } + { numnames #2 > + { "," * } + 'skip$ + if$ + t "others" = + { " et~al." * } + { " and " * t * } + if$ + } + if$ + } + 't + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ +} + +FUNCTION {format.authors} +{ author empty$ + { "" } + { author format.names } + if$ +} + +FUNCTION {format.editors} +{ editor empty$ + { "" } + { editor format.names + editor num.names$ #1 > + { ", editors" * } + { ", editor" * } + if$ + } + if$ +} + +FUNCTION {format.title} +{ title empty$ + { "" } + + { title "t" change.case$ } + + if$ +} + +FUNCTION {n.dashify} +{ 't := + "" + { t empty$ not } + { t #1 #1 substring$ "-" = + { t #1 #2 substring$ "--" = not + { "--" * + t #2 global.max$ substring$ 't := + } + { { t #1 #1 substring$ "-" = } + { "-" * + t #2 global.max$ substring$ 't := + } + while$ + } + if$ + } + { t #1 #1 substring$ * + t #2 global.max$ substring$ 't := + } + if$ + } + while$ +} + +FUNCTION {format.date} +{ year empty$ + { month empty$ + { "" } + { "there's a month but no year in " cite$ * warning$ + month + } + if$ + } + { month empty$ + { "" } + { month } + if$ + } + if$ +} + +FUNCTION {format.btitle} +{ title emphasize +} + +FUNCTION {tie.or.space.connect} +{ duplicate$ text.length$ #3 < + { "~" } + { " " } + if$ + swap$ * * +} + +FUNCTION {either.or.check} +{ empty$ + 'pop$ + { "can't use both " swap$ * " fields in " * cite$ * warning$ } + if$ +} + +FUNCTION {format.bvolume} +{ volume empty$ + { "" } + { "volume" volume tie.or.space.connect + series empty$ + 'skip$ + { " of " * series emphasize * } + if$ + "volume and number" number either.or.check + } + if$ +} + +FUNCTION {format.number.series} +{ volume empty$ + { number empty$ + { series field.or.null } + { output.state mid.sentence = + { "number" } + { "Number" } + if$ + number tie.or.space.connect + series empty$ + { "there's a number but no series in " cite$ * warning$ } + { " in " * series * } + if$ + } + if$ + } + { "" } + if$ +} + +FUNCTION {format.edition} +{ edition empty$ + { "" } + { output.state mid.sentence = + { edition "l" change.case$ " edition" * } + { edition "t" change.case$ " edition" * } + if$ + } + if$ +} + +INTEGERS { multiresult } + +FUNCTION {multi.page.check} +{ 't := + #0 'multiresult := + { multiresult not + t empty$ not + and + } + { t #1 #1 substring$ + duplicate$ "-" = + swap$ duplicate$ "," = + swap$ "+" = + or or + { #1 'multiresult := } + { t #2 global.max$ substring$ 't := } + if$ + } + while$ + multiresult +} + +FUNCTION {format.pages} +{ pages empty$ + { "" } + { pages multi.page.check + { "pages" pages n.dashify tie.or.space.connect } + { "page" pages tie.or.space.connect } + if$ + } + if$ +} + +FUNCTION {format.year.label} +{ year extra.label * +} + +FUNCTION {format.vol.num.pages} +{ volume field.or.null + number empty$ + 'skip$ + { "(" number * ")" * * + volume empty$ + { "there's a number but no volume in " cite$ * warning$ } + 'skip$ + if$ + } + if$ + pages empty$ + 'skip$ + { duplicate$ empty$ + { pop$ format.pages } + { ":" * pages n.dashify * } + if$ + } + if$ +} + +FUNCTION {format.chapter.pages} +{ chapter empty$ + 'format.pages + { type empty$ + { "chapter" } + { type "l" change.case$ } + if$ + chapter tie.or.space.connect + pages empty$ + 'skip$ + { ", " * format.pages * } + if$ + } + if$ +} + +FUNCTION {format.in.ed.booktitle} +{ booktitle empty$ + { "" } + { editor empty$ + { "In " booktitle emphasize * } + { "In " format.editors * ", " * booktitle emphasize * } + if$ + } + if$ +} + +FUNCTION {empty.misc.check} +{ author empty$ title empty$ howpublished empty$ + month empty$ year empty$ note empty$ + and and and and and + + key empty$ not and + + { "all relevant fields are empty in " cite$ * warning$ } + 'skip$ + if$ +} + +FUNCTION {format.thesis.type} +{ type empty$ + 'skip$ + { pop$ + type "t" change.case$ + } + if$ +} + +FUNCTION {format.tr.number} +{ type empty$ + { "Technical Report" } + 'type + if$ + number empty$ + { "t" change.case$ } + { number tie.or.space.connect } + if$ +} + +FUNCTION {format.article.crossref} +{ key empty$ + { journal empty$ + { "need key or journal for " cite$ * " to crossref " * crossref * + warning$ + "" + } + { "In {\em " journal * "\/}" * } + if$ + } + { "In " key * } + if$ + " \cite{" * crossref * "}" * +} + +FUNCTION {format.crossref.editor} +{ editor #1 "{vv~}{ll}" format.name$ + editor num.names$ duplicate$ + #2 > + { pop$ " et~al." * } + { #2 < + 'skip$ + { editor #2 "{ff }{vv }{ll}{ jj}" format.name$ "others" = + { " et~al." * } + { " and " * editor #2 "{vv~}{ll}" format.name$ * } + if$ + } + if$ + } + if$ +} + +FUNCTION {format.book.crossref} +{ volume empty$ + { "empty volume in " cite$ * "'s crossref of " * crossref * warning$ + "In " + } + { "Volume" volume tie.or.space.connect + " of " * + } + if$ + editor empty$ + editor field.or.null author field.or.null = + or + { key empty$ + { series empty$ + { "need editor, key, or series for " cite$ * " to crossref " * + crossref * warning$ + "" * + } + { "{\em " * series * "\/}" * } + if$ + } + { key * } + if$ + } + { format.crossref.editor * } + if$ + " \cite{" * crossref * "}" * +} + +FUNCTION {format.incoll.inproc.crossref} +{ editor empty$ + editor field.or.null author field.or.null = + or + { key empty$ + { booktitle empty$ + { "need editor, key, or booktitle for " cite$ * " to crossref " * + crossref * warning$ + "" + } + { "In {\em " booktitle * "\/}" * } + if$ + } + { "In " key * } + if$ + } + { "In " format.crossref.editor * } + if$ + " \cite{" * crossref * "}" * +} + +FUNCTION {article} +{ output.bibitem + format.authors "author" output.check + new.block + format.year.label "year" output.check + new.block + format.title "title" output.check + new.block + crossref missing$ + { journal emphasize "journal" output.check + format.vol.num.pages output + format.date output + } + { format.article.crossref output.nonnull + format.pages output + } + if$ + new.block + note output + fin.entry +} + +FUNCTION {book} +{ output.bibitem + author empty$ + { format.editors "author and editor" output.check } + { format.authors output.nonnull + crossref missing$ + { "author and editor" editor either.or.check } + 'skip$ + if$ + } + if$ + new.block + format.year.label "year" output.check + new.block + format.btitle "title" output.check + crossref missing$ + { format.bvolume output + new.block + format.number.series output + new.sentence + publisher "publisher" output.check + address output + } + { new.block + format.book.crossref output.nonnull + } + if$ + format.edition output + format.date output + new.block + note output + fin.entry +} + +FUNCTION {booklet} +{ output.bibitem + format.authors output + new.block + format.year.label "year" output.check + new.block + format.title "title" output.check + howpublished address new.block.checkb + howpublished output + address output + format.date output + new.block + note output + fin.entry +} + +FUNCTION {inbook} +{ output.bibitem + author empty$ + { format.editors "author and editor" output.check } + { format.authors output.nonnull + crossref missing$ + { "author and editor" editor either.or.check } + 'skip$ + if$ + } + if$ + format.year.label "year" output.check + new.block + new.block + format.btitle "title" output.check + crossref missing$ + { format.bvolume output + format.chapter.pages "chapter and pages" output.check + new.block + format.number.series output + new.sentence + publisher "publisher" output.check + address output + } + { format.chapter.pages "chapter and pages" output.check + new.block + format.book.crossref output.nonnull + } + if$ + format.edition output + format.date output + new.block + note output + fin.entry +} + +FUNCTION {incollection} +{ output.bibitem + format.authors "author" output.check + new.block + format.year.label "year" output.check + new.block + format.title "title" output.check + new.block + crossref missing$ + { format.in.ed.booktitle "booktitle" output.check + format.bvolume output + format.number.series output + format.chapter.pages output + new.sentence + publisher "publisher" output.check + address output + format.edition output + format.date output + } + { format.incoll.inproc.crossref output.nonnull + format.chapter.pages output + } + if$ + new.block + note output + fin.entry +} + +FUNCTION {inproceedings} +{ output.bibitem + format.authors "author" output.check + new.block + format.year.label "year" output.check + new.block + format.title "title" output.check + new.block + crossref missing$ + { format.in.ed.booktitle "booktitle" output.check + format.bvolume output + format.number.series output + format.pages output + address empty$ + { organization publisher new.sentence.checkb + organization output + publisher output + format.date output + } + { address output.nonnull + format.date output + new.sentence + organization output + publisher output + } + if$ + } + { format.incoll.inproc.crossref output.nonnull + format.pages output + } + if$ + new.block + note output + fin.entry +} + +FUNCTION {conference} { inproceedings } + +FUNCTION {manual} +{ output.bibitem + author empty$ + { organization empty$ + 'skip$ + { organization output.nonnull + address output + } + if$ + } + { format.authors output.nonnull } + if$ + format.year.label "year" output.check + new.block + new.block + format.btitle "title" output.check + author empty$ + { organization empty$ + { address new.block.checka + address output + } + 'skip$ + if$ + } + { organization address new.block.checkb + organization output + address output + } + if$ + format.edition output + format.date output + new.block + note output + fin.entry +} + +FUNCTION {mastersthesis} +{ output.bibitem + format.authors "author" output.check + new.block + format.year.label "year" output.check + new.block + format.title "title" output.check + new.block + "Master's thesis" format.thesis.type output.nonnull + school "school" output.check + address output + format.date output + new.block + note output + fin.entry +} + +FUNCTION {misc} +{ output.bibitem + format.authors output + new.block + format.year.label output + new.block + title howpublished new.block.checkb + format.title output + howpublished new.block.checka + howpublished output + format.date output + new.block + note output + fin.entry + empty.misc.check +} + +FUNCTION {phdthesis} +{ output.bibitem + format.authors "author" output.check + new.block + format.year.label "year" output.check + new.block + format.btitle "title" output.check + new.block + "{Ph.D.} thesis" format.thesis.type output.nonnull + school "school" output.check + address output + format.date output + new.block + note output + fin.entry +} + +FUNCTION {proceedings} +{ output.bibitem + editor empty$ + { organization output } + { format.editors output.nonnull } + if$ + new.block + format.year.label "year" output.check + new.block + format.btitle "title" output.check + format.bvolume output + format.number.series output + address empty$ + { editor empty$ + { publisher new.sentence.checka } + { organization publisher new.sentence.checkb + organization output + } + if$ + publisher output + format.date output + } + { address output.nonnull + format.date output + new.sentence + editor empty$ + 'skip$ + { organization output } + if$ + publisher output + } + if$ + new.block + note output + fin.entry +} + +FUNCTION {techreport} +{ output.bibitem + format.authors "author" output.check + new.block + format.year.label "year" output.check + new.block + format.title "title" output.check + new.block + format.tr.number output.nonnull + institution "institution" output.check + address output + format.date output + new.block + note output + fin.entry +} + +FUNCTION {unpublished} +{ output.bibitem + format.authors "author" output.check + new.block + format.year.label "year" output.check + new.block + format.title "title" output.check + new.block + note "note" output.check + format.date output + fin.entry +} + +FUNCTION {default.type} { misc } + +MACRO {jan} {"January"} + +MACRO {feb} {"February"} + +MACRO {mar} {"March"} + +MACRO {apr} {"April"} + +MACRO {may} {"May"} + +MACRO {jun} {"June"} + +MACRO {jul} {"July"} + +MACRO {aug} {"August"} + +MACRO {sep} {"September"} + +MACRO {oct} {"October"} + +MACRO {nov} {"November"} + +MACRO {dec} {"December"} + +MACRO {acmcs} {"ACM Computing Surveys"} + +MACRO {acta} {"Acta Informatica"} + +MACRO {cacm} {"Communications of the ACM"} + +MACRO {ibmjrd} {"IBM Journal of Research and Development"} + +MACRO {ibmsj} {"IBM Systems Journal"} + +MACRO {ieeese} {"IEEE Transactions on Software Engineering"} + +MACRO {ieeetc} {"IEEE Transactions on Computers"} + +MACRO {ieeetcad} + {"IEEE Transactions on Computer-Aided Design of Integrated Circuits"} + +MACRO {ipl} {"Information Processing Letters"} + +MACRO {jacm} {"Journal of the ACM"} + +MACRO {jcss} {"Journal of Computer and System Sciences"} + +MACRO {scp} {"Science of Computer Programming"} + +MACRO {sicomp} {"SIAM Journal on Computing"} + +MACRO {tocs} {"ACM Transactions on Computer Systems"} + +MACRO {tods} {"ACM Transactions on Database Systems"} + +MACRO {tog} {"ACM Transactions on Graphics"} + +MACRO {toms} {"ACM Transactions on Mathematical Software"} + +MACRO {toois} {"ACM Transactions on Office Information Systems"} + +MACRO {toplas} {"ACM Transactions on Programming Languages and Systems"} + +MACRO {tcs} {"Theoretical Computer Science"} + +READ + +FUNCTION {sortify} +{ purify$ + "l" change.case$ +} + +INTEGERS { len } + +FUNCTION {chop.word} +{ 's := + 'len := + s #1 len substring$ = + { s len #1 + global.max$ substring$ } + 's + if$ +} + +INTEGERS { et.al.char.used } + +FUNCTION {initialize.et.al.char.used} +{ #0 'et.al.char.used := +} + +EXECUTE {initialize.et.al.char.used} + +FUNCTION {format.lab.names} +{ 's := + s num.names$ 'numnames := + + numnames #1 = + { s #1 "{vv }{ll}" format.name$ } + { numnames #2 = + { s #1 "{vv }{ll }and " format.name$ s #2 "{vv }{ll}" format.name$ * + } + { s #1 "{vv }{ll }\bgroup et al.\egroup " format.name$ } + if$ + } + if$ + +} + +FUNCTION {author.key.label} +{ author empty$ + { key empty$ + + { cite$ #1 #3 substring$ } + + { key #3 text.prefix$ } + if$ + } + { author format.lab.names } + if$ +} + +FUNCTION {author.editor.key.label} +{ author empty$ + { editor empty$ + { key empty$ + + { cite$ #1 #3 substring$ } + + { key #3 text.prefix$ } + if$ + } + { editor format.lab.names } + if$ + } + { author format.lab.names } + if$ +} + +FUNCTION {author.key.organization.label} +{ author empty$ + { key empty$ + { organization empty$ + + { cite$ #1 #3 substring$ } + + { "The " #4 organization chop.word #3 text.prefix$ } + if$ + } + { key #3 text.prefix$ } + if$ + } + { author format.lab.names } + if$ +} + +FUNCTION {editor.key.organization.label} +{ editor empty$ + { key empty$ + { organization empty$ + + { cite$ #1 #3 substring$ } + + { "The " #4 organization chop.word #3 text.prefix$ } + if$ + } + { key #3 text.prefix$ } + if$ + } + { editor format.lab.names } + if$ +} + +FUNCTION {calc.label} +{ type$ "book" = + type$ "inbook" = + or + 'author.editor.key.label + { type$ "proceedings" = + 'editor.key.organization.label + { type$ "manual" = + 'author.key.organization.label + 'author.key.label + if$ + } + if$ + } + if$ + duplicate$ + + "\protect\citename{" swap$ * "}" * + year field.or.null purify$ * + 'label := + year field.or.null purify$ * + + sortify 'sort.label := +} + +FUNCTION {sort.format.names} +{ 's := + #1 'nameptr := + "" + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { nameptr #1 > + { " " * } + 'skip$ + if$ + + s nameptr "{vv{ } }{ll{ }}{ ff{ }}{ jj{ }}" format.name$ 't := + + nameptr numnames = t "others" = and + { "et al" * } + { t sortify * } + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ +} + +FUNCTION {sort.format.title} +{ 't := + "A " #2 + "An " #3 + "The " #4 t chop.word + chop.word + chop.word + sortify + #1 global.max$ substring$ +} + +FUNCTION {author.sort} +{ author empty$ + { key empty$ + { "to sort, need author or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { author sort.format.names } + if$ +} + +FUNCTION {author.editor.sort} +{ author empty$ + { editor empty$ + { key empty$ + { "to sort, need author, editor, or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { editor sort.format.names } + if$ + } + { author sort.format.names } + if$ +} + +FUNCTION {author.organization.sort} +{ author empty$ + { organization empty$ + { key empty$ + { "to sort, need author, organization, or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { "The " #4 organization chop.word sortify } + if$ + } + { author sort.format.names } + if$ +} + +FUNCTION {editor.organization.sort} +{ editor empty$ + { organization empty$ + { key empty$ + { "to sort, need editor, organization, or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { "The " #4 organization chop.word sortify } + if$ + } + { editor sort.format.names } + if$ +} + +FUNCTION {presort} + +{ calc.label + sort.label + " " + * + type$ "book" = + + type$ "inbook" = + or + 'author.editor.sort + { type$ "proceedings" = + 'editor.organization.sort + { type$ "manual" = + 'author.organization.sort + 'author.sort + if$ + } + if$ + } + if$ + + * + + " " + * + year field.or.null sortify + * + " " + * + title field.or.null + sort.format.title + * + #1 entry.max$ substring$ + 'sort.key$ := +} + +ITERATE {presort} + +SORT + +STRINGS { longest.label last.sort.label next.extra } + +INTEGERS { longest.label.width last.extra.num } + +FUNCTION {initialize.longest.label} +{ "" 'longest.label := + #0 int.to.chr$ 'last.sort.label := + "" 'next.extra := + #0 'longest.label.width := + #0 'last.extra.num := +} + +FUNCTION {forward.pass} +{ last.sort.label sort.label = + { last.extra.num #1 + 'last.extra.num := + last.extra.num int.to.chr$ 'extra.label := + } + { "a" chr.to.int$ 'last.extra.num := + "" 'extra.label := + sort.label 'last.sort.label := + } + if$ +} + +FUNCTION {reverse.pass} +{ next.extra "b" = + { "a" 'extra.label := } + 'skip$ + if$ + label extra.label * 'label := + label width$ longest.label.width > + { label 'longest.label := + label width$ 'longest.label.width := + } + 'skip$ + if$ + extra.label 'next.extra := +} + +EXECUTE {initialize.longest.label} + +ITERATE {forward.pass} + +REVERSE {reverse.pass} + +FUNCTION {begin.bib} + +{ et.al.char.used + { "\newcommand{\etalchar}[1]{$^{#1}$}" write$ newline$ } + 'skip$ + if$ + preamble$ empty$ + + 'skip$ + { preamble$ write$ newline$ } + if$ + + "\begin{thebibliography}{" "}" * write$ newline$ + +} + +EXECUTE {begin.bib} + +EXECUTE {init.state.consts} + +ITERATE {call.type$} + +FUNCTION {end.bib} +{ newline$ + "\end{thebibliography}" write$ newline$ +} + +EXECUTE {end.bib} \ No newline at end of file diff --git a/references/2019.arxiv.liu/source/acl2019.sty b/references/2019.arxiv.liu/source/acl2019.sty new file mode 100644 index 0000000000000000000000000000000000000000..92f4ac4961a5556c560c254ef7b37744d9b92f99 --- /dev/null +++ b/references/2019.arxiv.liu/source/acl2019.sty @@ -0,0 +1,559 @@ +% This is the LaTex style file for ACL 2019, based off of ACL 2018 and NAACL 2019. +% Addressing bibtex issues mentioned in https://github.com/acl-org/acl-pub/issues/2 +% Other major modifications include +% changing the color of the line numbers to a light gray; changing font size of abstract to be 10pt; changing caption font size to be 10pt. +% -- M Mitchell and Stephanie Lukin + +% 2017: modified to support DOI links in bibliography. Now uses +% natbib package rather than defining citation commands in this file. +% Use with acl_natbib.bst bib style. -- Dan Gildea + +% This is the LaTeX style for ACL 2016. It contains Margaret Mitchell's +% line number adaptations (ported by Hai Zhao and Yannick Versley). + +% It is nearly identical to the style files for ACL 2015, +% ACL 2014, EACL 2006, ACL2005, ACL 2002, ACL 2001, ACL 2000, +% EACL 95 and EACL 99. +% +% Changes made include: adapt layout to A4 and centimeters, widen abstract + +% This is the LaTeX style file for ACL 2000. It is nearly identical to the +% style files for EACL 95 and EACL 99. Minor changes include editing the +% instructions to reflect use of \documentclass rather than \documentstyle +% and removing the white space before the title on the first page +% -- John Chen, June 29, 2000 + +% This is the LaTeX style file for EACL-95. It is identical to the +% style file for ANLP '94 except that the margins are adjusted for A4 +% paper. -- abney 13 Dec 94 + +% The ANLP '94 style file is a slightly modified +% version of the style used for AAAI and IJCAI, using some changes +% prepared by Fernando Pereira and others and some minor changes +% by Paul Jacobs. + +% Papers prepared using the aclsub.sty file and acl.bst bibtex style +% should be easily converted to final format using this style. +% (1) Submission information (\wordcount, \subject, and \makeidpage) +% should be removed. +% (2) \summary should be removed. The summary material should come +% after \maketitle and should be in the ``abstract'' environment +% (between \begin{abstract} and \end{abstract}). +% (3) Check all citations. This style should handle citations correctly +% and also allows multiple citations separated by semicolons. +% (4) Check figures and examples. Because the final format is double- +% column, some adjustments may have to be made to fit text in the column +% or to choose full-width (\figure*} figures. + +% Place this in a file called aclap.sty in the TeX search path. +% (Placing it in the same directory as the paper should also work.) + +% Prepared by Peter F. Patel-Schneider, liberally using the ideas of +% other style hackers, including Barbara Beeton. +% This style is NOT guaranteed to work. It is provided in the hope +% that it will make the preparation of papers easier. +% +% There are undoubtably bugs in this style. If you make bug fixes, +% improvements, etc. please let me know. My e-mail address is: +% pfps@research.att.com + +% Papers are to be prepared using the ``acl_natbib'' bibliography style, +% as follows: +% \documentclass[11pt]{article} +% \usepackage{acl2000} +% \title{Title} +% \author{Author 1 \and Author 2 \\ Address line \\ Address line \And +% Author 3 \\ Address line \\ Address line} +% \begin{document} +% ... +% \bibliography{bibliography-file} +% \bibliographystyle{acl_natbib} +% \end{document} + +% Author information can be set in various styles: +% For several authors from the same institution: +% \author{Author 1 \and ... \and Author n \\ +% Address line \\ ... \\ Address line} +% if the names do not fit well on one line use +% Author 1 \\ {\bf Author 2} \\ ... \\ {\bf Author n} \\ +% For authors from different institutions: +% \author{Author 1 \\ Address line \\ ... \\ Address line +% \And ... \And +% Author n \\ Address line \\ ... \\ Address line} +% To start a seperate ``row'' of authors use \AND, as in +% \author{Author 1 \\ Address line \\ ... \\ Address line +% \AND +% Author 2 \\ Address line \\ ... \\ Address line \And +% Author 3 \\ Address line \\ ... \\ Address line} + +% If the title and author information does not fit in the area allocated, +% place \setlength\titlebox{} right after +% \usepackage{acl2015} +% where can be something larger than 5cm + +% include hyperref, unless user specifies nohyperref option like this: +% \usepackage[nohyperref]{naaclhlt2018} +\newif\ifacl@hyperref +\DeclareOption{hyperref}{\acl@hyperreftrue} +\DeclareOption{nohyperref}{\acl@hyperreffalse} +\ExecuteOptions{hyperref} % default is to use hyperref +\ProcessOptions\relax +\ifacl@hyperref + \RequirePackage{hyperref} + \usepackage{xcolor} % make links dark blue + \definecolor{darkblue}{rgb}{0, 0, 0.5} + \hypersetup{colorlinks=true,citecolor=darkblue, linkcolor=darkblue, urlcolor=darkblue} +\else + % This definition is used if the hyperref package is not loaded. + % It provides a backup, no-op definiton of \href. + % This is necessary because \href command is used in the acl_natbib.bst file. + \def\href#1#2{{#2}} + % We still need to load xcolor in this case because the lighter line numbers require it. (SC/KG/WL) + \usepackage{xcolor} +\fi + +\typeout{Conference Style for ACL 2019} + +% NOTE: Some laser printers have a serious problem printing TeX output. +% These printing devices, commonly known as ``write-white'' laser +% printers, tend to make characters too light. To get around this +% problem, a darker set of fonts must be created for these devices. +% + +\newcommand{\Thanks}[1]{\thanks{\ #1}} + +% A4 modified by Eneko; again modified by Alexander for 5cm titlebox +\setlength{\paperwidth}{21cm} % A4 +\setlength{\paperheight}{29.7cm}% A4 +\setlength\topmargin{-0.5cm} +\setlength\oddsidemargin{0cm} +\setlength\textheight{24.7cm} +\setlength\textwidth{16.0cm} +\setlength\columnsep{0.6cm} +\newlength\titlebox +\setlength\titlebox{5cm} +\setlength\headheight{5pt} +\setlength\headsep{0pt} +\thispagestyle{empty} +\pagestyle{empty} + + +\flushbottom \twocolumn \sloppy + +% We're never going to need a table of contents, so just flush it to +% save space --- suggested by drstrip@sandia-2 +\def\addcontentsline#1#2#3{} + +\newif\ifaclfinal +\aclfinalfalse +\def\aclfinalcopy{\global\aclfinaltrue} + +%% ----- Set up hooks to repeat content on every page of the output doc, +%% necessary for the line numbers in the submitted version. --MM +%% +%% Copied from CVPR 2015's cvpr_eso.sty, which appears to be largely copied from everyshi.sty. +%% +%% Original cvpr_eso.sty available at: http://www.pamitc.org/cvpr15/author_guidelines.php +%% Original evershi.sty available at: https://www.ctan.org/pkg/everyshi +%% +%% Copyright (C) 2001 Martin Schr\"oder: +%% +%% Martin Schr"oder +%% Cr"usemannallee 3 +%% D-28213 Bremen +%% Martin.Schroeder@ACM.org +%% +%% This program may be redistributed and/or modified under the terms +%% of the LaTeX Project Public License, either version 1.0 of this +%% license, or (at your option) any later version. +%% The latest version of this license is in +%% CTAN:macros/latex/base/lppl.txt. +%% +%% Happy users are requested to send [Martin] a postcard. :-) +%% +\newcommand{\@EveryShipoutACL@Hook}{} +\newcommand{\@EveryShipoutACL@AtNextHook}{} +\newcommand*{\EveryShipoutACL}[1] + {\g@addto@macro\@EveryShipoutACL@Hook{#1}} +\newcommand*{\AtNextShipoutACL@}[1] + {\g@addto@macro\@EveryShipoutACL@AtNextHook{#1}} +\newcommand{\@EveryShipoutACL@Shipout}{% + \afterassignment\@EveryShipoutACL@Test + \global\setbox\@cclv= % + } +\newcommand{\@EveryShipoutACL@Test}{% + \ifvoid\@cclv\relax + \aftergroup\@EveryShipoutACL@Output + \else + \@EveryShipoutACL@Output + \fi% + } +\newcommand{\@EveryShipoutACL@Output}{% + \@EveryShipoutACL@Hook% + \@EveryShipoutACL@AtNextHook% + \gdef\@EveryShipoutACL@AtNextHook{}% + \@EveryShipoutACL@Org@Shipout\box\@cclv% + } +\newcommand{\@EveryShipoutACL@Org@Shipout}{} +\newcommand*{\@EveryShipoutACL@Init}{% + \message{ABD: EveryShipout initializing macros}% + \let\@EveryShipoutACL@Org@Shipout\shipout + \let\shipout\@EveryShipoutACL@Shipout + } +\AtBeginDocument{\@EveryShipoutACL@Init} + +%% ----- Set up for placing additional items into the submitted version --MM +%% +%% Based on eso-pic.sty +%% +%% Original available at: https://www.ctan.org/tex-archive/macros/latex/contrib/eso-pic +%% Copyright (C) 1998-2002 by Rolf Niepraschk +%% +%% Which may be distributed and/or modified under the conditions of +%% the LaTeX Project Public License, either version 1.2 of this license +%% or (at your option) any later version. The latest version of this +%% license is in: +%% +%% http://www.latex-project.org/lppl.txt +%% +%% and version 1.2 or later is part of all distributions of LaTeX version +%% 1999/12/01 or later. +%% +%% In contrast to the original, we do not include the definitions for/using: +%% gridpicture, div[2], isMEMOIR[1], gridSetup[6][], subgridstyle{dotted}, labelfactor{}, gap{}, gridunitname{}, gridunit{}, gridlines{\thinlines}, subgridlines{\thinlines}, the {keyval} package, evenside margin, nor any definitions with 'color'. +%% +%% These are beyond what is needed for the NAACL/ACL style. +%% +\newcommand\LenToUnit[1]{#1\@gobble} +\newcommand\AtPageUpperLeft[1]{% + \begingroup + \@tempdima=0pt\relax\@tempdimb=\ESO@yoffsetI\relax + \put(\LenToUnit{\@tempdima},\LenToUnit{\@tempdimb}){#1}% + \endgroup +} +\newcommand\AtPageLowerLeft[1]{\AtPageUpperLeft{% + \put(0,\LenToUnit{-\paperheight}){#1}}} +\newcommand\AtPageCenter[1]{\AtPageUpperLeft{% + \put(\LenToUnit{.5\paperwidth},\LenToUnit{-.5\paperheight}){#1}}} +\newcommand\AtPageLowerCenter[1]{\AtPageUpperLeft{% + \put(\LenToUnit{.5\paperwidth},\LenToUnit{-\paperheight}){#1}}}% +\newcommand\AtPageLowishCenter[1]{\AtPageUpperLeft{% + \put(\LenToUnit{.5\paperwidth},\LenToUnit{-.96\paperheight}){#1}}} +\newcommand\AtTextUpperLeft[1]{% + \begingroup + \setlength\@tempdima{1in}% + \advance\@tempdima\oddsidemargin% + \@tempdimb=\ESO@yoffsetI\relax\advance\@tempdimb-1in\relax% + \advance\@tempdimb-\topmargin% + \advance\@tempdimb-\headheight\advance\@tempdimb-\headsep% + \put(\LenToUnit{\@tempdima},\LenToUnit{\@tempdimb}){#1}% + \endgroup +} +\newcommand\AtTextLowerLeft[1]{\AtTextUpperLeft{% + \put(0,\LenToUnit{-\textheight}){#1}}} +\newcommand\AtTextCenter[1]{\AtTextUpperLeft{% + \put(\LenToUnit{.5\textwidth},\LenToUnit{-.5\textheight}){#1}}} +\newcommand{\ESO@HookI}{} \newcommand{\ESO@HookII}{} +\newcommand{\ESO@HookIII}{} +\newcommand{\AddToShipoutPicture}{% + \@ifstar{\g@addto@macro\ESO@HookII}{\g@addto@macro\ESO@HookI}} +\newcommand{\ClearShipoutPicture}{\global\let\ESO@HookI\@empty} +\newcommand{\@ShipoutPicture}{% + \bgroup + \@tempswafalse% + \ifx\ESO@HookI\@empty\else\@tempswatrue\fi% + \ifx\ESO@HookII\@empty\else\@tempswatrue\fi% + \ifx\ESO@HookIII\@empty\else\@tempswatrue\fi% + \if@tempswa% + \@tempdima=1in\@tempdimb=-\@tempdima% + \advance\@tempdimb\ESO@yoffsetI% + \unitlength=1pt% + \global\setbox\@cclv\vbox{% + \vbox{\let\protect\relax + \pictur@(0,0)(\strip@pt\@tempdima,\strip@pt\@tempdimb)% + \ESO@HookIII\ESO@HookI\ESO@HookII% + \global\let\ESO@HookII\@empty% + \endpicture}% + \nointerlineskip% + \box\@cclv}% + \fi + \egroup +} +\EveryShipoutACL{\@ShipoutPicture} +\newif\ifESO@dvips\ESO@dvipsfalse +\newif\ifESO@grid\ESO@gridfalse +\newif\ifESO@texcoord\ESO@texcoordfalse +\newcommand*\ESO@griddelta{}\newcommand*\ESO@griddeltaY{} +\newcommand*\ESO@gridDelta{}\newcommand*\ESO@gridDeltaY{} +\newcommand*\ESO@yoffsetI{}\newcommand*\ESO@yoffsetII{} +\ifESO@texcoord + \def\ESO@yoffsetI{0pt}\def\ESO@yoffsetII{-\paperheight} + \edef\ESO@griddeltaY{-\ESO@griddelta}\edef\ESO@gridDeltaY{-\ESO@gridDelta} +\else + \def\ESO@yoffsetI{\paperheight}\def\ESO@yoffsetII{0pt} + \edef\ESO@griddeltaY{\ESO@griddelta}\edef\ESO@gridDeltaY{\ESO@gridDelta} +\fi + + +%% ----- Submitted version markup: Page numbers, ruler, and confidentiality. Using ideas/code from cvpr.sty 2015. --MM + +\font\aclhv = phvb at 8pt + +%% Define vruler %% + +%\makeatletter +\newbox\aclrulerbox +\newcount\aclrulercount +\newdimen\aclruleroffset +\newdimen\cv@lineheight +\newdimen\cv@boxheight +\newbox\cv@tmpbox +\newcount\cv@refno +\newcount\cv@tot +% NUMBER with left flushed zeros \fillzeros[] +\newcount\cv@tmpc@ \newcount\cv@tmpc +\def\fillzeros[#1]#2{\cv@tmpc@=#2\relax\ifnum\cv@tmpc@<0\cv@tmpc@=-\cv@tmpc@\fi +\cv@tmpc=1 % +\loop\ifnum\cv@tmpc@<10 \else \divide\cv@tmpc@ by 10 \advance\cv@tmpc by 1 \fi + \ifnum\cv@tmpc@=10\relax\cv@tmpc@=11\relax\fi \ifnum\cv@tmpc@>10 \repeat +\ifnum#2<0\advance\cv@tmpc1\relax-\fi +\loop\ifnum\cv@tmpc<#1\relax0\advance\cv@tmpc1\relax\fi \ifnum\cv@tmpc<#1 \repeat +\cv@tmpc@=#2\relax\ifnum\cv@tmpc@<0\cv@tmpc@=-\cv@tmpc@\fi \relax\the\cv@tmpc@}% +% \makevruler[][][][][] +\def\makevruler[#1][#2][#3][#4][#5]{\begingroup\offinterlineskip +\textheight=#5\vbadness=10000\vfuzz=120ex\overfullrule=0pt% +\global\setbox\aclrulerbox=\vbox to \textheight{% +{\parskip=0pt\hfuzz=150em\cv@boxheight=\textheight +\color{gray} +\cv@lineheight=#1\global\aclrulercount=#2% +\cv@tot\cv@boxheight\divide\cv@tot\cv@lineheight\advance\cv@tot2% +\cv@refno1\vskip-\cv@lineheight\vskip1ex% +\loop\setbox\cv@tmpbox=\hbox to0cm{{\aclhv\hfil\fillzeros[#4]\aclrulercount}}% +\ht\cv@tmpbox\cv@lineheight\dp\cv@tmpbox0pt\box\cv@tmpbox\break +\advance\cv@refno1\global\advance\aclrulercount#3\relax +\ifnum\cv@refno<\cv@tot\repeat}}\endgroup}% +%\makeatother + + +\def\aclpaperid{***} +\def\confidential{\textcolor{black}{ACL 2019 Submission~\aclpaperid. Confidential Review Copy. DO NOT DISTRIBUTE.}} + +%% Page numbering, Vruler and Confidentiality %% +% \makevruler[][][][][] + +% SC/KG/WL - changed line numbering to gainsboro +\definecolor{gainsboro}{rgb}{0.8, 0.8, 0.8} +%\def\aclruler#1{\makevruler[14.17pt][#1][1][3][\textheight]\usebox{\aclrulerbox}} %% old line +\def\aclruler#1{\textcolor{gainsboro}{\makevruler[14.17pt][#1][1][3][\textheight]\usebox{\aclrulerbox}}} + +\def\leftoffset{-2.1cm} %original: -45pt +\def\rightoffset{17.5cm} %original: 500pt +\ifaclfinal\else\pagenumbering{arabic} +\AddToShipoutPicture{% +\ifaclfinal\else +\AtPageLowishCenter{\textcolor{black}{\thepage}} +\aclruleroffset=\textheight +\advance\aclruleroffset4pt + \AtTextUpperLeft{% + \put(\LenToUnit{\leftoffset},\LenToUnit{-\aclruleroffset}){%left ruler + \aclruler{\aclrulercount}} + \put(\LenToUnit{\rightoffset},\LenToUnit{-\aclruleroffset}){%right ruler + \aclruler{\aclrulercount}} + } + \AtTextUpperLeft{%confidential + \put(0,\LenToUnit{1cm}){\parbox{\textwidth}{\centering\aclhv\confidential}} + } +\fi +} + +%%%% ----- End settings for placing additional items into the submitted version --MM ----- %%%% + +%%%% ----- Begin settings for both submitted and camera-ready version ----- %%%% + +%% Title and Authors %% + +\newcommand\outauthor{ + \begin{tabular}[t]{c} + \ifaclfinal + \bf\@author + \else + % Avoiding common accidental de-anonymization issue. --MM + \bf Anonymous ACL submission + \fi + \end{tabular}} + +% Changing the expanded titlebox for submissions to 2.5 in (rather than 6.5cm) +% and moving it to the style sheet, rather than within the example tex file. --MM +\ifaclfinal +\else + \addtolength\titlebox{.25in} +\fi +% Mostly taken from deproc. +\def\maketitle{\par + \begingroup + \def\thefootnote{\fnsymbol{footnote}} + \def\@makefnmark{\hbox to 0pt{$^{\@thefnmark}$\hss}} + \twocolumn[\@maketitle] \@thanks + \endgroup + \setcounter{footnote}{0} + \let\maketitle\relax \let\@maketitle\relax + \gdef\@thanks{}\gdef\@author{}\gdef\@title{}\let\thanks\relax} +\def\@maketitle{\vbox to \titlebox{\hsize\textwidth + \linewidth\hsize \vskip 0.125in minus 0.125in \centering + {\Large\bf \@title \par} \vskip 0.2in plus 1fil minus 0.1in + {\def\and{\unskip\enspace{\rm and}\enspace}% + \def\And{\end{tabular}\hss \egroup \hskip 1in plus 2fil + \hbox to 0pt\bgroup\hss \begin{tabular}[t]{c}\bf}% + \def\AND{\end{tabular}\hss\egroup \hfil\hfil\egroup + \vskip 0.25in plus 1fil minus 0.125in + \hbox to \linewidth\bgroup\large \hfil\hfil + \hbox to 0pt\bgroup\hss \begin{tabular}[t]{c}\bf} + \hbox to \linewidth\bgroup\large \hfil\hfil + \hbox to 0pt\bgroup\hss + \outauthor + \hss\egroup + \hfil\hfil\egroup} + \vskip 0.3in plus 2fil minus 0.1in +}} + +% margins and font size for abstract +\renewenvironment{abstract}% + {\centerline{\large\bf Abstract}% + \begin{list}{}% + {\setlength{\rightmargin}{0.6cm}% + \setlength{\leftmargin}{0.6cm}}% + \item[]\ignorespaces% + \@setsize\normalsize{12pt}\xpt\@xpt + }% + {\unskip\end{list}} + +%\renewenvironment{abstract}{\centerline{\large\bf +% Abstract}\vspace{0.5ex}\begin{quote}}{\par\end{quote}\vskip 1ex} + +% Resizing figure and table captions - SL +\newcommand{\figcapfont}{\rm} +\newcommand{\tabcapfont}{\rm} +\renewcommand{\fnum@figure}{\figcapfont Figure \thefigure} +\renewcommand{\fnum@table}{\tabcapfont Table \thetable} +\renewcommand{\figcapfont}{\@setsize\normalsize{12pt}\xpt\@xpt} +\renewcommand{\tabcapfont}{\@setsize\normalsize{12pt}\xpt\@xpt} +% Support for interacting with the caption, subfigure, and subcaption packages - SL +\usepackage{caption} +\DeclareCaptionFont{10pt}{\fontsize{10pt}{12pt}\selectfont} +\captionsetup{font=10pt} + +\RequirePackage{natbib} +% for citation commands in the .tex, authors can use: +% \citep, \citet, and \citeyearpar for compatibility with natbib, or +% \cite, \newcite, and \shortcite for compatibility with older ACL .sty files +\renewcommand\cite{\citep} % to get "(Author Year)" with natbib +\newcommand\shortcite{\citeyearpar}% to get "(Year)" with natbib +\newcommand\newcite{\citet} % to get "Author (Year)" with natbib + +% DK/IV: Workaround for annoying hyperref pagewrap bug +\RequirePackage{etoolbox} +\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{\errmessage{\noexpand patch failed}} + +% bibliography + +\def\@up#1{\raise.2ex\hbox{#1}} + +% Don't put a label in the bibliography at all. Just use the unlabeled format +% instead. +\def\thebibliography#1{\vskip\parskip% +\vskip\baselineskip% +\def\baselinestretch{1}% +\ifx\@currsize\normalsize\@normalsize\else\@currsize\fi% +\vskip-\parskip% +\vskip-\baselineskip% +\section*{References\@mkboth + {References}{References}}\list + {}{\setlength{\labelwidth}{0pt}\setlength{\leftmargin}{\parindent} + \setlength{\itemindent}{-\parindent}} + \def\newblock{\hskip .11em plus .33em minus -.07em} + \sloppy\clubpenalty4000\widowpenalty4000 + \sfcode`\.=1000\relax} +\let\endthebibliography=\endlist + + +% Allow for a bibliography of sources of attested examples +\def\thesourcebibliography#1{\vskip\parskip% +\vskip\baselineskip% +\def\baselinestretch{1}% +\ifx\@currsize\normalsize\@normalsize\else\@currsize\fi% +\vskip-\parskip% +\vskip-\baselineskip% +\section*{Sources of Attested Examples\@mkboth + {Sources of Attested Examples}{Sources of Attested Examples}}\list + {}{\setlength{\labelwidth}{0pt}\setlength{\leftmargin}{\parindent} + \setlength{\itemindent}{-\parindent}} + \def\newblock{\hskip .11em plus .33em minus -.07em} + \sloppy\clubpenalty4000\widowpenalty4000 + \sfcode`\.=1000\relax} +\let\endthesourcebibliography=\endlist + +% sections with less space +\def\section{\@startsection {section}{1}{\z@}{-2.0ex plus + -0.5ex minus -.2ex}{1.5ex plus 0.3ex minus .2ex}{\large\bf\raggedright}} +\def\subsection{\@startsection{subsection}{2}{\z@}{-1.8ex plus + -0.5ex minus -.2ex}{0.8ex plus .2ex}{\normalsize\bf\raggedright}} +%% changed by KO to - values to get teh initial parindent right +\def\subsubsection{\@startsection{subsubsection}{3}{\z@}{-1.5ex plus + -0.5ex minus -.2ex}{0.5ex plus .2ex}{\normalsize\bf\raggedright}} +\def\paragraph{\@startsection{paragraph}{4}{\z@}{1.5ex plus + 0.5ex minus .2ex}{-1em}{\normalsize\bf}} +\def\subparagraph{\@startsection{subparagraph}{5}{\parindent}{1.5ex plus + 0.5ex minus .2ex}{-1em}{\normalsize\bf}} + +% Footnotes +\footnotesep 6.65pt % +\skip\footins 9pt plus 4pt minus 2pt +\def\footnoterule{\kern-3pt \hrule width 5pc \kern 2.6pt } +\setcounter{footnote}{0} + +% Lists and paragraphs +\parindent 1em +\topsep 4pt plus 1pt minus 2pt +\partopsep 1pt plus 0.5pt minus 0.5pt +\itemsep 2pt plus 1pt minus 0.5pt +\parsep 2pt plus 1pt minus 0.5pt + +\leftmargin 2em \leftmargini\leftmargin \leftmarginii 2em +\leftmarginiii 1.5em \leftmarginiv 1.0em \leftmarginv .5em \leftmarginvi .5em +\labelwidth\leftmargini\advance\labelwidth-\labelsep \labelsep 5pt + +\def\@listi{\leftmargin\leftmargini} +\def\@listii{\leftmargin\leftmarginii + \labelwidth\leftmarginii\advance\labelwidth-\labelsep + \topsep 2pt plus 1pt minus 0.5pt + \parsep 1pt plus 0.5pt minus 0.5pt + \itemsep \parsep} +\def\@listiii{\leftmargin\leftmarginiii + \labelwidth\leftmarginiii\advance\labelwidth-\labelsep + \topsep 1pt plus 0.5pt minus 0.5pt + \parsep \z@ \partopsep 0.5pt plus 0pt minus 0.5pt + \itemsep \topsep} +\def\@listiv{\leftmargin\leftmarginiv + \labelwidth\leftmarginiv\advance\labelwidth-\labelsep} +\def\@listv{\leftmargin\leftmarginv + \labelwidth\leftmarginv\advance\labelwidth-\labelsep} +\def\@listvi{\leftmargin\leftmarginvi + \labelwidth\leftmarginvi\advance\labelwidth-\labelsep} + +\abovedisplayskip 7pt plus2pt minus5pt% +\belowdisplayskip \abovedisplayskip +\abovedisplayshortskip 0pt plus3pt% +\belowdisplayshortskip 4pt plus3pt minus3pt% + +% Less leading in most fonts (due to the narrow columns) +% The choices were between 1-pt and 1.5-pt leading +\def\@normalsize{\@setsize\normalsize{11pt}\xpt\@xpt} +\def\small{\@setsize\small{10pt}\ixpt\@ixpt} +\def\footnotesize{\@setsize\footnotesize{10pt}\ixpt\@ixpt} +\def\scriptsize{\@setsize\scriptsize{8pt}\viipt\@viipt} +\def\tiny{\@setsize\tiny{7pt}\vipt\@vipt} +\def\large{\@setsize\large{14pt}\xiipt\@xiipt} +\def\Large{\@setsize\Large{16pt}\xivpt\@xivpt} +\def\LARGE{\@setsize\LARGE{20pt}\xviipt\@xviipt} +\def\huge{\@setsize\huge{23pt}\xxpt\@xxpt} +\def\Huge{\@setsize\Huge{28pt}\xxvpt\@xxvpt} diff --git a/references/2019.arxiv.liu/source/acl_natbib.bst b/references/2019.arxiv.liu/source/acl_natbib.bst new file mode 100644 index 0000000000000000000000000000000000000000..821195d8bbb77f882afb308a31e5f9da81720f6b --- /dev/null +++ b/references/2019.arxiv.liu/source/acl_natbib.bst @@ -0,0 +1,1975 @@ +%%% acl_natbib.bst +%%% Modification of BibTeX style file acl_natbib_nourl.bst +%%% ... by urlbst, version 0.7 (marked with "% urlbst") +%%% See +%%% Added webpage entry type, and url and lastchecked fields. +%%% Added eprint support. +%%% Added DOI support. +%%% Added PUBMED support. +%%% Added hyperref support. +%%% Original headers follow... + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% BibTeX style file acl_natbib_nourl.bst +% +% intended as input to urlbst script +% $ ./urlbst --hyperref --inlinelinks acl_natbib_nourl.bst > acl_natbib.bst +% +% adapted from compling.bst +% in order to mimic the style files for ACL conferences prior to 2017 +% by making the following three changes: +% - for @incollection, page numbers now follow volume title. +% - for @inproceedings, address now follows conference name. +% (address is intended as location of conference, +% not address of publisher.) +% - for papers with three authors, use et al. in citation +% Dan Gildea 2017/06/08 +% - fixed a bug with format.chapter - error given if chapter is empty +% with inbook. +% Shay Cohen 2018/02/16 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% BibTeX style file compling.bst +% +% Intended for the journal Computational Linguistics (ACL/MIT Press) +% Created by Ron Artstein on 2005/08/22 +% For use with for author-year citations. +% +% I created this file in order to allow submissions to the journal +% Computational Linguistics using the package for author-year +% citations, which offers a lot more flexibility than , CL's +% official citation package. This file adheres strictly to the official +% style guide available from the MIT Press: +% +% http://mitpress.mit.edu/journals/coli/compling_style.pdf +% +% This includes all the various quirks of the style guide, for example: +% - a chapter from a monograph (@inbook) has no page numbers. +% - an article from an edited volume (@incollection) has page numbers +% after the publisher and address. +% - an article from a proceedings volume (@inproceedings) has page +% numbers before the publisher and address. +% +% Where the style guide was inconsistent or not specific enough I +% looked at actual published articles and exercised my own judgment. +% I noticed two inconsistencies in the style guide: +% +% - The style guide gives one example of an article from an edited +% volume with the editor's name spelled out in full, and another +% with the editors' names abbreviated. I chose to accept the first +% one as correct, since the style guide generally shuns abbreviations, +% and editors' names are also spelled out in some recently published +% articles. +% +% - The style guide gives one example of a reference where the word +% "and" between two authors is preceded by a comma. This is most +% likely a typo, since in all other cases with just two authors or +% editors there is no comma before the word "and". +% +% One case where the style guide is not being specific is the placement +% of the edition number, for which no example is given. I chose to put +% it immediately after the title, which I (subjectively) find natural, +% and is also the place of the edition in a few recently published +% articles. +% +% This file correctly reproduces all of the examples in the official +% style guide, except for the two inconsistencies noted above. I even +% managed to get it to correctly format the proceedings example which +% has an organization, a publisher, and two addresses (the conference +% location and the publisher's address), though I cheated a bit by +% putting the conference location and month as part of the title field; +% I feel that in this case the conference location and month can be +% considered as part of the title, and that adding a location field +% is not justified. Note also that a location field is not standard, +% so entries made with this field would not port nicely to other styles. +% However, if authors feel that there's a need for a location field +% then tell me and I'll see what I can do. +% +% The file also produces to my satisfaction all the bibliographical +% entries in my recent (joint) submission to CL (this was the original +% motivation for creating the file). I also tested it by running it +% on a larger set of entries and eyeballing the results. There may of +% course still be errors, especially with combinations of fields that +% are not that common, or with cross-references (which I seldom use). +% If you find such errors please write to me. +% +% I hope people find this file useful. Please email me with comments +% and suggestions. +% +% Ron Artstein +% artstein [at] essex.ac.uk +% August 22, 2005. +% +% Some technical notes. +% +% This file is based on a file generated with the package +% by Patrick W. Daly (see selected options below), which was then +% manually customized to conform with certain CL requirements which +% cannot be met by . Departures from the generated file +% include: +% +% Function inbook: moved publisher and address to the end; moved +% edition after title; replaced function format.chapter.pages by +% new function format.chapter to output chapter without pages. +% +% Function inproceedings: moved publisher and address to the end; +% replaced function format.in.ed.booktitle by new function +% format.in.booktitle to output the proceedings title without +% the editor. +% +% Functions book, incollection, manual: moved edition after title. +% +% Function mastersthesis: formatted title as for articles (unlike +% phdthesis which is formatted as book) and added month. +% +% Function proceedings: added new.sentence between organization and +% publisher when both are present. +% +% Function format.lab.names: modified so that it gives all the +% authors' surnames for in-text citations for one, two and three +% authors and only uses "et. al" for works with four authors or more +% (thanks to Ken Shan for convincing me to go through the trouble of +% modifying this function rather than using unreliable hacks). +% +% Changes: +% +% 2006-10-27: Changed function reverse.pass so that the extra label is +% enclosed in parentheses when the year field ends in an uppercase or +% lowercase letter (change modeled after Uli Sauerland's modification +% of nals.bst). RA. +% +% +% The preamble of the generated file begins below: +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% +%% This is file `compling.bst', +%% generated with the docstrip utility. +%% +%% The original source files were: +%% +%% merlin.mbs (with options: `ay,nat,vonx,nm-revv1,jnrlst,keyxyr,blkyear,dt-beg,yr-per,note-yr,num-xser,pre-pub,xedn,nfss') +%% ---------------------------------------- +%% *** Intended for the journal Computational Linguistics *** +%% +%% Copyright 1994-2002 Patrick W Daly + % =============================================================== + % IMPORTANT NOTICE: + % This bibliographic style (bst) file has been generated from one or + % more master bibliographic style (mbs) files, listed above. + % + % This generated file can be redistributed and/or modified under the terms + % of the LaTeX Project Public License Distributed from CTAN + % archives in directory macros/latex/base/lppl.txt; either + % version 1 of the License, or any later version. + % =============================================================== + % Name and version information of the main mbs file: + % \ProvidesFile{merlin.mbs}[2002/10/21 4.05 (PWD, AO, DPC)] + % For use with BibTeX version 0.99a or later + %------------------------------------------------------------------- + % This bibliography style file is intended for texts in ENGLISH + % This is an author-year citation style bibliography. As such, it is + % non-standard LaTeX, and requires a special package file to function properly. + % Such a package is natbib.sty by Patrick W. Daly + % The form of the \bibitem entries is + % \bibitem[Jones et al.(1990)]{key}... + % \bibitem[Jones et al.(1990)Jones, Baker, and Smith]{key}... + % The essential feature is that the label (the part in brackets) consists + % of the author names, as they should appear in the citation, with the year + % in parentheses following. There must be no space before the opening + % parenthesis! + % With natbib v5.3, a full list of authors may also follow the year. + % In natbib.sty, it is possible to define the type of enclosures that is + % really wanted (brackets or parentheses), but in either case, there must + % be parentheses in the label. + % The \cite command functions as follows: + % \citet{key} ==>> Jones et al. (1990) + % \citet*{key} ==>> Jones, Baker, and Smith (1990) + % \citep{key} ==>> (Jones et al., 1990) + % \citep*{key} ==>> (Jones, Baker, and Smith, 1990) + % \citep[chap. 2]{key} ==>> (Jones et al., 1990, chap. 2) + % \citep[e.g.][]{key} ==>> (e.g. Jones et al., 1990) + % \citep[e.g.][p. 32]{key} ==>> (e.g. Jones et al., p. 32) + % \citeauthor{key} ==>> Jones et al. + % \citeauthor*{key} ==>> Jones, Baker, and Smith + % \citeyear{key} ==>> 1990 + %--------------------------------------------------------------------- + +ENTRY + { address + author + booktitle + chapter + edition + editor + howpublished + institution + journal + key + month + note + number + organization + pages + publisher + school + series + title + type + volume + year + eprint % urlbst + doi % urlbst + pubmed % urlbst + url % urlbst + lastchecked % urlbst + } + {} + { label extra.label sort.label short.list } +INTEGERS { output.state before.all mid.sentence after.sentence after.block } +% urlbst... +% urlbst constants and state variables +STRINGS { urlintro + eprinturl eprintprefix doiprefix doiurl pubmedprefix pubmedurl + citedstring onlinestring linktextstring + openinlinelink closeinlinelink } +INTEGERS { hrefform inlinelinks makeinlinelink + addeprints adddoiresolver addpubmedresolver } +FUNCTION {init.urlbst.variables} +{ + % The following constants may be adjusted by hand, if desired + + % The first set allow you to enable or disable certain functionality. + #1 'addeprints := % 0=no eprints; 1=include eprints + #1 'adddoiresolver := % 0=no DOI resolver; 1=include it + #1 'addpubmedresolver := % 0=no PUBMED resolver; 1=include it + #2 'hrefform := % 0=no crossrefs; 1=hypertex xrefs; 2=hyperref refs + #1 'inlinelinks := % 0=URLs explicit; 1=URLs attached to titles + + % String constants, which you _might_ want to tweak. + "URL: " 'urlintro := % prefix before URL; typically "Available from:" or "URL": + "online" 'onlinestring := % indication that resource is online; typically "online" + "cited " 'citedstring := % indicator of citation date; typically "cited " + "[link]" 'linktextstring := % dummy link text; typically "[link]" + "http://arxiv.org/abs/" 'eprinturl := % prefix to make URL from eprint ref + "arXiv:" 'eprintprefix := % text prefix printed before eprint ref; typically "arXiv:" + "https://doi.org/" 'doiurl := % prefix to make URL from DOI + "doi:" 'doiprefix := % text prefix printed before DOI ref; typically "doi:" + "http://www.ncbi.nlm.nih.gov/pubmed/" 'pubmedurl := % prefix to make URL from PUBMED + "PMID:" 'pubmedprefix := % text prefix printed before PUBMED ref; typically "PMID:" + + % The following are internal state variables, not configuration constants, + % so they shouldn't be fiddled with. + #0 'makeinlinelink := % state variable managed by possibly.setup.inlinelink + "" 'openinlinelink := % ditto + "" 'closeinlinelink := % ditto +} +INTEGERS { + bracket.state + outside.brackets + open.brackets + within.brackets + close.brackets +} +% ...urlbst to here +FUNCTION {init.state.consts} +{ #0 'outside.brackets := % urlbst... + #1 'open.brackets := + #2 'within.brackets := + #3 'close.brackets := % ...urlbst to here + + #0 'before.all := + #1 'mid.sentence := + #2 'after.sentence := + #3 'after.block := +} +STRINGS { s t} +% urlbst +FUNCTION {output.nonnull.original} +{ 's := + output.state mid.sentence = + { ", " * write$ } + { output.state after.block = + { add.period$ write$ + newline$ + "\newblock " write$ + } + { output.state before.all = + 'write$ + { add.period$ " " * write$ } + if$ + } + if$ + mid.sentence 'output.state := + } + if$ + s +} + +% urlbst... +% The following three functions are for handling inlinelink. They wrap +% a block of text which is potentially output with write$ by multiple +% other functions, so we don't know the content a priori. +% They communicate between each other using the variables makeinlinelink +% (which is true if a link should be made), and closeinlinelink (which holds +% the string which should close any current link. They can be called +% at any time, but start.inlinelink will be a no-op unless something has +% previously set makeinlinelink true, and the two ...end.inlinelink functions +% will only do their stuff if start.inlinelink has previously set +% closeinlinelink to be non-empty. +% (thanks to 'ijvm' for suggested code here) +FUNCTION {uand} +{ 'skip$ { pop$ #0 } if$ } % 'and' (which isn't defined at this point in the file) +FUNCTION {possibly.setup.inlinelink} +{ makeinlinelink hrefform #0 > uand + { doi empty$ adddoiresolver uand + { pubmed empty$ addpubmedresolver uand + { eprint empty$ addeprints uand + { url empty$ + { "" } + { url } + if$ } + { eprinturl eprint * } + if$ } + { pubmedurl pubmed * } + if$ } + { doiurl doi * } + if$ + % an appropriately-formatted URL is now on the stack + hrefform #1 = % hypertex + { "\special {html: }{" * 'openinlinelink := + "\special {html:}" 'closeinlinelink := } + { "\href {" swap$ * "} {" * 'openinlinelink := % hrefform=#2 -- hyperref + % the space between "} {" matters: a URL of just the right length can cause "\% newline em" + "}" 'closeinlinelink := } + if$ + #0 'makeinlinelink := + } + 'skip$ + if$ % makeinlinelink +} +FUNCTION {add.inlinelink} +{ openinlinelink empty$ + 'skip$ + { openinlinelink swap$ * closeinlinelink * + "" 'openinlinelink := + } + if$ +} +FUNCTION {output.nonnull} +{ % Save the thing we've been asked to output + 's := + % If the bracket-state is close.brackets, then add a close-bracket to + % what is currently at the top of the stack, and set bracket.state + % to outside.brackets + bracket.state close.brackets = + { "]" * + outside.brackets 'bracket.state := + } + 'skip$ + if$ + bracket.state outside.brackets = + { % We're outside all brackets -- this is the normal situation. + % Write out what's currently at the top of the stack, using the + % original output.nonnull function. + s + add.inlinelink + output.nonnull.original % invoke the original output.nonnull + } + { % Still in brackets. Add open-bracket or (continuation) comma, add the + % new text (in s) to the top of the stack, and move to the close-brackets + % state, ready for next time (unless inbrackets resets it). If we come + % into this branch, then output.state is carefully undisturbed. + bracket.state open.brackets = + { " [" * } + { ", " * } % bracket.state will be within.brackets + if$ + s * + close.brackets 'bracket.state := + } + if$ +} + +% Call this function just before adding something which should be presented in +% brackets. bracket.state is handled specially within output.nonnull. +FUNCTION {inbrackets} +{ bracket.state close.brackets = + { within.brackets 'bracket.state := } % reset the state: not open nor closed + { open.brackets 'bracket.state := } + if$ +} + +FUNCTION {format.lastchecked} +{ lastchecked empty$ + { "" } + { inbrackets citedstring lastchecked * } + if$ +} +% ...urlbst to here +FUNCTION {output} +{ duplicate$ empty$ + 'pop$ + 'output.nonnull + if$ +} +FUNCTION {output.check} +{ 't := + duplicate$ empty$ + { pop$ "empty " t * " in " * cite$ * warning$ } + 'output.nonnull + if$ +} +FUNCTION {fin.entry.original} % urlbst (renamed from fin.entry, so it can be wrapped below) +{ add.period$ + write$ + newline$ +} + +FUNCTION {new.block} +{ output.state before.all = + 'skip$ + { after.block 'output.state := } + if$ +} +FUNCTION {new.sentence} +{ output.state after.block = + 'skip$ + { output.state before.all = + 'skip$ + { after.sentence 'output.state := } + if$ + } + if$ +} +FUNCTION {add.blank} +{ " " * before.all 'output.state := +} + +FUNCTION {date.block} +{ + new.block +} + +FUNCTION {not} +{ { #0 } + { #1 } + if$ +} +FUNCTION {and} +{ 'skip$ + { pop$ #0 } + if$ +} +FUNCTION {or} +{ { pop$ #1 } + 'skip$ + if$ +} +FUNCTION {new.block.checkb} +{ empty$ + swap$ empty$ + and + 'skip$ + 'new.block + if$ +} +FUNCTION {field.or.null} +{ duplicate$ empty$ + { pop$ "" } + 'skip$ + if$ +} +FUNCTION {emphasize} +{ duplicate$ empty$ + { pop$ "" } + { "\emph{" swap$ * "}" * } + if$ +} +FUNCTION {tie.or.space.prefix} +{ duplicate$ text.length$ #3 < + { "~" } + { " " } + if$ + swap$ +} + +FUNCTION {capitalize} +{ "u" change.case$ "t" change.case$ } + +FUNCTION {space.word} +{ " " swap$ * " " * } + % Here are the language-specific definitions for explicit words. + % Each function has a name bbl.xxx where xxx is the English word. + % The language selected here is ENGLISH +FUNCTION {bbl.and} +{ "and"} + +FUNCTION {bbl.etal} +{ "et~al." } + +FUNCTION {bbl.editors} +{ "editors" } + +FUNCTION {bbl.editor} +{ "editor" } + +FUNCTION {bbl.edby} +{ "edited by" } + +FUNCTION {bbl.edition} +{ "edition" } + +FUNCTION {bbl.volume} +{ "volume" } + +FUNCTION {bbl.of} +{ "of" } + +FUNCTION {bbl.number} +{ "number" } + +FUNCTION {bbl.nr} +{ "no." } + +FUNCTION {bbl.in} +{ "in" } + +FUNCTION {bbl.pages} +{ "pages" } + +FUNCTION {bbl.page} +{ "page" } + +FUNCTION {bbl.chapter} +{ "chapter" } + +FUNCTION {bbl.techrep} +{ "Technical Report" } + +FUNCTION {bbl.mthesis} +{ "Master's thesis" } + +FUNCTION {bbl.phdthesis} +{ "Ph.D. thesis" } + +MACRO {jan} {"January"} + +MACRO {feb} {"February"} + +MACRO {mar} {"March"} + +MACRO {apr} {"April"} + +MACRO {may} {"May"} + +MACRO {jun} {"June"} + +MACRO {jul} {"July"} + +MACRO {aug} {"August"} + +MACRO {sep} {"September"} + +MACRO {oct} {"October"} + +MACRO {nov} {"November"} + +MACRO {dec} {"December"} + +MACRO {acmcs} {"ACM Computing Surveys"} + +MACRO {acta} {"Acta Informatica"} + +MACRO {cacm} {"Communications of the ACM"} + +MACRO {ibmjrd} {"IBM Journal of Research and Development"} + +MACRO {ibmsj} {"IBM Systems Journal"} + +MACRO {ieeese} {"IEEE Transactions on Software Engineering"} + +MACRO {ieeetc} {"IEEE Transactions on Computers"} + +MACRO {ieeetcad} + {"IEEE Transactions on Computer-Aided Design of Integrated Circuits"} + +MACRO {ipl} {"Information Processing Letters"} + +MACRO {jacm} {"Journal of the ACM"} + +MACRO {jcss} {"Journal of Computer and System Sciences"} + +MACRO {scp} {"Science of Computer Programming"} + +MACRO {sicomp} {"SIAM Journal on Computing"} + +MACRO {tocs} {"ACM Transactions on Computer Systems"} + +MACRO {tods} {"ACM Transactions on Database Systems"} + +MACRO {tog} {"ACM Transactions on Graphics"} + +MACRO {toms} {"ACM Transactions on Mathematical Software"} + +MACRO {toois} {"ACM Transactions on Office Information Systems"} + +MACRO {toplas} {"ACM Transactions on Programming Languages and Systems"} + +MACRO {tcs} {"Theoretical Computer Science"} +FUNCTION {bibinfo.check} +{ swap$ + duplicate$ missing$ + { + pop$ pop$ + "" + } + { duplicate$ empty$ + { + swap$ pop$ + } + { swap$ + pop$ + } + if$ + } + if$ +} +FUNCTION {bibinfo.warn} +{ swap$ + duplicate$ missing$ + { + swap$ "missing " swap$ * " in " * cite$ * warning$ pop$ + "" + } + { duplicate$ empty$ + { + swap$ "empty " swap$ * " in " * cite$ * warning$ + } + { swap$ + pop$ + } + if$ + } + if$ +} +STRINGS { bibinfo} +INTEGERS { nameptr namesleft numnames } + +FUNCTION {format.names} +{ 'bibinfo := + duplicate$ empty$ 'skip$ { + 's := + "" 't := + #1 'nameptr := + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + duplicate$ #1 > + { "{ff~}{vv~}{ll}{, jj}" } + { "{ff~}{vv~}{ll}{, jj}" } % first name first for first author +% { "{vv~}{ll}{, ff}{, jj}" } % last name first for first author + if$ + format.name$ + bibinfo bibinfo.check + 't := + nameptr #1 > + { + namesleft #1 > + { ", " * t * } + { + numnames #2 > + { "," * } + 'skip$ + if$ + s nameptr "{ll}" format.name$ duplicate$ "others" = + { 't := } + { pop$ } + if$ + t "others" = + { + " " * bbl.etal * + } + { + bbl.and + space.word * t * + } + if$ + } + if$ + } + 't + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ + } if$ +} +FUNCTION {format.names.ed} +{ + 'bibinfo := + duplicate$ empty$ 'skip$ { + 's := + "" 't := + #1 'nameptr := + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + "{ff~}{vv~}{ll}{, jj}" + format.name$ + bibinfo bibinfo.check + 't := + nameptr #1 > + { + namesleft #1 > + { ", " * t * } + { + numnames #2 > + { "," * } + 'skip$ + if$ + s nameptr "{ll}" format.name$ duplicate$ "others" = + { 't := } + { pop$ } + if$ + t "others" = + { + + " " * bbl.etal * + } + { + bbl.and + space.word * t * + } + if$ + } + if$ + } + 't + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ + } if$ +} +FUNCTION {format.key} +{ empty$ + { key field.or.null } + { "" } + if$ +} + +FUNCTION {format.authors} +{ author "author" format.names +} +FUNCTION {get.bbl.editor} +{ editor num.names$ #1 > 'bbl.editors 'bbl.editor if$ } + +FUNCTION {format.editors} +{ editor "editor" format.names duplicate$ empty$ 'skip$ + { + "," * + " " * + get.bbl.editor + * + } + if$ +} +FUNCTION {format.note} +{ + note empty$ + { "" } + { note #1 #1 substring$ + duplicate$ "{" = + 'skip$ + { output.state mid.sentence = + { "l" } + { "u" } + if$ + change.case$ + } + if$ + note #2 global.max$ substring$ * "note" bibinfo.check + } + if$ +} + +FUNCTION {format.title} +{ title + duplicate$ empty$ 'skip$ + { "t" change.case$ } + if$ + "title" bibinfo.check +} +FUNCTION {format.full.names} +{'s := + "" 't := + #1 'nameptr := + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + "{vv~}{ll}" format.name$ + 't := + nameptr #1 > + { + namesleft #1 > + { ", " * t * } + { + s nameptr "{ll}" format.name$ duplicate$ "others" = + { 't := } + { pop$ } + if$ + t "others" = + { + " " * bbl.etal * + } + { + numnames #2 > + { "," * } + 'skip$ + if$ + bbl.and + space.word * t * + } + if$ + } + if$ + } + 't + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ +} + +FUNCTION {author.editor.key.full} +{ author empty$ + { editor empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { editor format.full.names } + if$ + } + { author format.full.names } + if$ +} + +FUNCTION {author.key.full} +{ author empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { author format.full.names } + if$ +} + +FUNCTION {editor.key.full} +{ editor empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { editor format.full.names } + if$ +} + +FUNCTION {make.full.names} +{ type$ "book" = + type$ "inbook" = + or + 'author.editor.key.full + { type$ "proceedings" = + 'editor.key.full + 'author.key.full + if$ + } + if$ +} + +FUNCTION {output.bibitem.original} % urlbst (renamed from output.bibitem, so it can be wrapped below) +{ newline$ + "\bibitem[{" write$ + label write$ + ")" make.full.names duplicate$ short.list = + { pop$ } + { * } + if$ + "}]{" * write$ + cite$ write$ + "}" write$ + newline$ + "" + before.all 'output.state := +} + +FUNCTION {n.dashify} +{ + 't := + "" + { t empty$ not } + { t #1 #1 substring$ "-" = + { t #1 #2 substring$ "--" = not + { "--" * + t #2 global.max$ substring$ 't := + } + { { t #1 #1 substring$ "-" = } + { "-" * + t #2 global.max$ substring$ 't := + } + while$ + } + if$ + } + { t #1 #1 substring$ * + t #2 global.max$ substring$ 't := + } + if$ + } + while$ +} + +FUNCTION {word.in} +{ bbl.in capitalize + " " * } + +FUNCTION {format.date} +{ year "year" bibinfo.check duplicate$ empty$ + { + } + 'skip$ + if$ + extra.label * + before.all 'output.state := + after.sentence 'output.state := +} +FUNCTION {format.btitle} +{ title "title" bibinfo.check + duplicate$ empty$ 'skip$ + { + emphasize + } + if$ +} +FUNCTION {either.or.check} +{ empty$ + 'pop$ + { "can't use both " swap$ * " fields in " * cite$ * warning$ } + if$ +} +FUNCTION {format.bvolume} +{ volume empty$ + { "" } + { bbl.volume volume tie.or.space.prefix + "volume" bibinfo.check * * + series "series" bibinfo.check + duplicate$ empty$ 'pop$ + { swap$ bbl.of space.word * swap$ + emphasize * } + if$ + "volume and number" number either.or.check + } + if$ +} +FUNCTION {format.number.series} +{ volume empty$ + { number empty$ + { series field.or.null } + { series empty$ + { number "number" bibinfo.check } + { output.state mid.sentence = + { bbl.number } + { bbl.number capitalize } + if$ + number tie.or.space.prefix "number" bibinfo.check * * + bbl.in space.word * + series "series" bibinfo.check * + } + if$ + } + if$ + } + { "" } + if$ +} + +FUNCTION {format.edition} +{ edition duplicate$ empty$ 'skip$ + { + output.state mid.sentence = + { "l" } + { "t" } + if$ change.case$ + "edition" bibinfo.check + " " * bbl.edition * + } + if$ +} +INTEGERS { multiresult } +FUNCTION {multi.page.check} +{ 't := + #0 'multiresult := + { multiresult not + t empty$ not + and + } + { t #1 #1 substring$ + duplicate$ "-" = + swap$ duplicate$ "," = + swap$ "+" = + or or + { #1 'multiresult := } + { t #2 global.max$ substring$ 't := } + if$ + } + while$ + multiresult +} +FUNCTION {format.pages} +{ pages duplicate$ empty$ 'skip$ + { duplicate$ multi.page.check + { + bbl.pages swap$ + n.dashify + } + { + bbl.page swap$ + } + if$ + tie.or.space.prefix + "pages" bibinfo.check + * * + } + if$ +} +FUNCTION {format.journal.pages} +{ pages duplicate$ empty$ 'pop$ + { swap$ duplicate$ empty$ + { pop$ pop$ format.pages } + { + ":" * + swap$ + n.dashify + "pages" bibinfo.check + * + } + if$ + } + if$ +} +FUNCTION {format.vol.num.pages} +{ volume field.or.null + duplicate$ empty$ 'skip$ + { + "volume" bibinfo.check + } + if$ + number "number" bibinfo.check duplicate$ empty$ 'skip$ + { + swap$ duplicate$ empty$ + { "there's a number but no volume in " cite$ * warning$ } + 'skip$ + if$ + swap$ + "(" swap$ * ")" * + } + if$ * + format.journal.pages +} + +FUNCTION {format.chapter} +{ chapter empty$ + 'format.pages + { type empty$ + { bbl.chapter } + { type "l" change.case$ + "type" bibinfo.check + } + if$ + chapter tie.or.space.prefix + "chapter" bibinfo.check + * * + } + if$ +} + +FUNCTION {format.chapter.pages} +{ chapter empty$ + 'format.pages + { type empty$ + { bbl.chapter } + { type "l" change.case$ + "type" bibinfo.check + } + if$ + chapter tie.or.space.prefix + "chapter" bibinfo.check + * * + pages empty$ + 'skip$ + { ", " * format.pages * } + if$ + } + if$ +} + +FUNCTION {format.booktitle} +{ + booktitle "booktitle" bibinfo.check + emphasize +} +FUNCTION {format.in.booktitle} +{ format.booktitle duplicate$ empty$ 'skip$ + { + word.in swap$ * + } + if$ +} +FUNCTION {format.in.ed.booktitle} +{ format.booktitle duplicate$ empty$ 'skip$ + { + editor "editor" format.names.ed duplicate$ empty$ 'pop$ + { + "," * + " " * + get.bbl.editor + ", " * + * swap$ + * } + if$ + word.in swap$ * + } + if$ +} +FUNCTION {format.thesis.type} +{ type duplicate$ empty$ + 'pop$ + { swap$ pop$ + "t" change.case$ "type" bibinfo.check + } + if$ +} +FUNCTION {format.tr.number} +{ number "number" bibinfo.check + type duplicate$ empty$ + { pop$ bbl.techrep } + 'skip$ + if$ + "type" bibinfo.check + swap$ duplicate$ empty$ + { pop$ "t" change.case$ } + { tie.or.space.prefix * * } + if$ +} +FUNCTION {format.article.crossref} +{ + word.in + " \cite{" * crossref * "}" * +} +FUNCTION {format.book.crossref} +{ volume duplicate$ empty$ + { "empty volume in " cite$ * "'s crossref of " * crossref * warning$ + pop$ word.in + } + { bbl.volume + capitalize + swap$ tie.or.space.prefix "volume" bibinfo.check * * bbl.of space.word * + } + if$ + " \cite{" * crossref * "}" * +} +FUNCTION {format.incoll.inproc.crossref} +{ + word.in + " \cite{" * crossref * "}" * +} +FUNCTION {format.org.or.pub} +{ 't := + "" + address empty$ t empty$ and + 'skip$ + { + t empty$ + { address "address" bibinfo.check * + } + { t * + address empty$ + 'skip$ + { ", " * address "address" bibinfo.check * } + if$ + } + if$ + } + if$ +} +FUNCTION {format.publisher.address} +{ publisher "publisher" bibinfo.warn format.org.or.pub +} + +FUNCTION {format.organization.address} +{ organization "organization" bibinfo.check format.org.or.pub +} + +% urlbst... +% Functions for making hypertext links. +% In all cases, the stack has (link-text href-url) +% +% make 'null' specials +FUNCTION {make.href.null} +{ + pop$ +} +% make hypertex specials +FUNCTION {make.href.hypertex} +{ + "\special {html: }" * swap$ * + "\special {html:}" * +} +% make hyperref specials +FUNCTION {make.href.hyperref} +{ + "\href {" swap$ * "} {\path{" * swap$ * "}}" * +} +FUNCTION {make.href} +{ hrefform #2 = + 'make.href.hyperref % hrefform = 2 + { hrefform #1 = + 'make.href.hypertex % hrefform = 1 + 'make.href.null % hrefform = 0 (or anything else) + if$ + } + if$ +} + +% If inlinelinks is true, then format.url should be a no-op, since it's +% (a) redundant, and (b) could end up as a link-within-a-link. +FUNCTION {format.url} +{ inlinelinks #1 = url empty$ or + { "" } + { hrefform #1 = + { % special case -- add HyperTeX specials + urlintro "\url{" url * "}" * url make.href.hypertex * } + { urlintro "\url{" * url * "}" * } + if$ + } + if$ +} + +FUNCTION {format.eprint} +{ eprint empty$ + { "" } + { eprintprefix eprint * eprinturl eprint * make.href } + if$ +} + +FUNCTION {format.doi} +{ doi empty$ + { "" } + { doiprefix doi * doiurl doi * make.href } + if$ +} + +FUNCTION {format.pubmed} +{ pubmed empty$ + { "" } + { pubmedprefix pubmed * pubmedurl pubmed * make.href } + if$ +} + +% Output a URL. We can't use the more normal idiom (something like +% `format.url output'), because the `inbrackets' within +% format.lastchecked applies to everything between calls to `output', +% so that `format.url format.lastchecked * output' ends up with both +% the URL and the lastchecked in brackets. +FUNCTION {output.url} +{ url empty$ + 'skip$ + { new.block + format.url output + format.lastchecked output + } + if$ +} + +FUNCTION {output.web.refs} +{ + new.block + inlinelinks + 'skip$ % links were inline -- don't repeat them + { + output.url + addeprints eprint empty$ not and + { format.eprint output.nonnull } + 'skip$ + if$ + adddoiresolver doi empty$ not and + { format.doi output.nonnull } + 'skip$ + if$ + addpubmedresolver pubmed empty$ not and + { format.pubmed output.nonnull } + 'skip$ + if$ + } + if$ +} + +% Wrapper for output.bibitem.original. +% If the URL field is not empty, set makeinlinelink to be true, +% so that an inline link will be started at the next opportunity +FUNCTION {output.bibitem} +{ outside.brackets 'bracket.state := + output.bibitem.original + inlinelinks url empty$ not doi empty$ not or pubmed empty$ not or eprint empty$ not or and + { #1 'makeinlinelink := } + { #0 'makeinlinelink := } + if$ +} + +% Wrapper for fin.entry.original +FUNCTION {fin.entry} +{ output.web.refs % urlbst + makeinlinelink % ooops, it appears we didn't have a title for inlinelink + { possibly.setup.inlinelink % add some artificial link text here, as a fallback + linktextstring output.nonnull } + 'skip$ + if$ + bracket.state close.brackets = % urlbst + { "]" * } + 'skip$ + if$ + fin.entry.original +} + +% Webpage entry type. +% Title and url fields required; +% author, note, year, month, and lastchecked fields optional +% See references +% ISO 690-2 http://www.nlc-bnc.ca/iso/tc46sc9/standard/690-2e.htm +% http://www.classroom.net/classroom/CitingNetResources.html +% http://neal.ctstateu.edu/history/cite.html +% http://www.cas.usf.edu/english/walker/mla.html +% for citation formats for web pages. +FUNCTION {webpage} +{ output.bibitem + author empty$ + { editor empty$ + 'skip$ % author and editor both optional + { format.editors output.nonnull } + if$ + } + { editor empty$ + { format.authors output.nonnull } + { "can't use both author and editor fields in " cite$ * warning$ } + if$ + } + if$ + new.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ + format.title "title" output.check + inbrackets onlinestring output + new.block + year empty$ + 'skip$ + { format.date "year" output.check } + if$ + % We don't need to output the URL details ('lastchecked' and 'url'), + % because fin.entry does that for us, using output.web.refs. The only + % reason we would want to put them here is if we were to decide that + % they should go in front of the rather miscellaneous information in 'note'. + new.block + note output + fin.entry +} +% ...urlbst to here + + +FUNCTION {article} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title "title" output.check + new.block + crossref missing$ + { + journal + "journal" bibinfo.check + emphasize + "journal" output.check + possibly.setup.inlinelink format.vol.num.pages output% urlbst + } + { format.article.crossref output.nonnull + format.pages output + } + if$ + new.block + format.note output + fin.entry +} +FUNCTION {book} +{ output.bibitem + author empty$ + { format.editors "author and editor" output.check + editor format.key output + } + { format.authors output.nonnull + crossref missing$ + { "author and editor" editor either.or.check } + 'skip$ + if$ + } + if$ + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.btitle "title" output.check + format.edition output + crossref missing$ + { format.bvolume output + new.block + format.number.series output + new.sentence + format.publisher.address output + } + { + new.block + format.book.crossref output.nonnull + } + if$ + new.block + format.note output + fin.entry +} +FUNCTION {booklet} +{ output.bibitem + format.authors output + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title "title" output.check + new.block + howpublished "howpublished" bibinfo.check output + address "address" bibinfo.check output + new.block + format.note output + fin.entry +} + +FUNCTION {inbook} +{ output.bibitem + author empty$ + { format.editors "author and editor" output.check + editor format.key output + } + { format.authors output.nonnull + crossref missing$ + { "author and editor" editor either.or.check } + 'skip$ + if$ + } + if$ + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.btitle "title" output.check + format.edition output + crossref missing$ + { + format.bvolume output + format.number.series output + format.chapter "chapter" output.check + new.sentence + format.publisher.address output + new.block + } + { + format.chapter "chapter" output.check + new.block + format.book.crossref output.nonnull + } + if$ + new.block + format.note output + fin.entry +} + +FUNCTION {incollection} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title "title" output.check + new.block + crossref missing$ + { format.in.ed.booktitle "booktitle" output.check + format.edition output + format.bvolume output + format.number.series output + format.chapter.pages output + new.sentence + format.publisher.address output + } + { format.incoll.inproc.crossref output.nonnull + format.chapter.pages output + } + if$ + new.block + format.note output + fin.entry +} +FUNCTION {inproceedings} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title "title" output.check + new.block + crossref missing$ + { format.in.booktitle "booktitle" output.check + format.bvolume output + format.number.series output + format.pages output + address "address" bibinfo.check output + new.sentence + organization "organization" bibinfo.check output + publisher "publisher" bibinfo.check output + } + { format.incoll.inproc.crossref output.nonnull + format.pages output + } + if$ + new.block + format.note output + fin.entry +} +FUNCTION {conference} { inproceedings } +FUNCTION {manual} +{ output.bibitem + format.authors output + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.btitle "title" output.check + format.edition output + organization address new.block.checkb + organization "organization" bibinfo.check output + address "address" bibinfo.check output + new.block + format.note output + fin.entry +} + +FUNCTION {mastersthesis} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title + "title" output.check + new.block + bbl.mthesis format.thesis.type output.nonnull + school "school" bibinfo.warn output + address "address" bibinfo.check output + month "month" bibinfo.check output + new.block + format.note output + fin.entry +} + +FUNCTION {misc} +{ output.bibitem + format.authors output + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title output + new.block + howpublished "howpublished" bibinfo.check output + new.block + format.note output + fin.entry +} +FUNCTION {phdthesis} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.btitle + "title" output.check + new.block + bbl.phdthesis format.thesis.type output.nonnull + school "school" bibinfo.warn output + address "address" bibinfo.check output + new.block + format.note output + fin.entry +} + +FUNCTION {proceedings} +{ output.bibitem + format.editors output + editor format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.btitle "title" output.check + format.bvolume output + format.number.series output + new.sentence + publisher empty$ + { format.organization.address output } + { organization "organization" bibinfo.check output + new.sentence + format.publisher.address output + } + if$ + new.block + format.note output + fin.entry +} + +FUNCTION {techreport} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title + "title" output.check + new.block + format.tr.number output.nonnull + institution "institution" bibinfo.warn output + address "address" bibinfo.check output + new.block + format.note output + fin.entry +} + +FUNCTION {unpublished} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title "title" output.check + new.block + format.note "note" output.check + fin.entry +} + +FUNCTION {default.type} { misc } +READ +FUNCTION {sortify} +{ purify$ + "l" change.case$ +} +INTEGERS { len } +FUNCTION {chop.word} +{ 's := + 'len := + s #1 len substring$ = + { s len #1 + global.max$ substring$ } + 's + if$ +} +FUNCTION {format.lab.names} +{ 's := + "" 't := + s #1 "{vv~}{ll}" format.name$ + s num.names$ duplicate$ + #2 > + { pop$ + " " * bbl.etal * + } + { #2 < + 'skip$ + { s #2 "{ff }{vv }{ll}{ jj}" format.name$ "others" = + { + " " * bbl.etal * + } + { bbl.and space.word * s #2 "{vv~}{ll}" format.name$ + * } + if$ + } + if$ + } + if$ +} + +FUNCTION {author.key.label} +{ author empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { author format.lab.names } + if$ +} + +FUNCTION {author.editor.key.label} +{ author empty$ + { editor empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { editor format.lab.names } + if$ + } + { author format.lab.names } + if$ +} + +FUNCTION {editor.key.label} +{ editor empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { editor format.lab.names } + if$ +} + +FUNCTION {calc.short.authors} +{ type$ "book" = + type$ "inbook" = + or + 'author.editor.key.label + { type$ "proceedings" = + 'editor.key.label + 'author.key.label + if$ + } + if$ + 'short.list := +} + +FUNCTION {calc.label} +{ calc.short.authors + short.list + "(" + * + year duplicate$ empty$ + short.list key field.or.null = or + { pop$ "" } + 'skip$ + if$ + * + 'label := +} + +FUNCTION {sort.format.names} +{ 's := + #1 'nameptr := + "" + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + "{ll{ }}{ ff{ }}{ jj{ }}" + format.name$ 't := + nameptr #1 > + { + " " * + namesleft #1 = t "others" = and + { "zzzzz" * } + { t sortify * } + if$ + } + { t sortify * } + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ +} + +FUNCTION {sort.format.title} +{ 't := + "A " #2 + "An " #3 + "The " #4 t chop.word + chop.word + chop.word + sortify + #1 global.max$ substring$ +} +FUNCTION {author.sort} +{ author empty$ + { key empty$ + { "to sort, need author or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { author sort.format.names } + if$ +} +FUNCTION {author.editor.sort} +{ author empty$ + { editor empty$ + { key empty$ + { "to sort, need author, editor, or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { editor sort.format.names } + if$ + } + { author sort.format.names } + if$ +} +FUNCTION {editor.sort} +{ editor empty$ + { key empty$ + { "to sort, need editor or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { editor sort.format.names } + if$ +} +FUNCTION {presort} +{ calc.label + label sortify + " " + * + type$ "book" = + type$ "inbook" = + or + 'author.editor.sort + { type$ "proceedings" = + 'editor.sort + 'author.sort + if$ + } + if$ + #1 entry.max$ substring$ + 'sort.label := + sort.label + * + " " + * + title field.or.null + sort.format.title + * + #1 entry.max$ substring$ + 'sort.key$ := +} + +ITERATE {presort} +SORT +STRINGS { last.label next.extra } +INTEGERS { last.extra.num number.label } +FUNCTION {initialize.extra.label.stuff} +{ #0 int.to.chr$ 'last.label := + "" 'next.extra := + #0 'last.extra.num := + #0 'number.label := +} +FUNCTION {forward.pass} +{ last.label label = + { last.extra.num #1 + 'last.extra.num := + last.extra.num int.to.chr$ 'extra.label := + } + { "a" chr.to.int$ 'last.extra.num := + "" 'extra.label := + label 'last.label := + } + if$ + number.label #1 + 'number.label := +} +FUNCTION {reverse.pass} +{ next.extra "b" = + { "a" 'extra.label := } + 'skip$ + if$ + extra.label 'next.extra := + extra.label + duplicate$ empty$ + 'skip$ + { year field.or.null #-1 #1 substring$ chr.to.int$ #65 < + { "{\natexlab{" swap$ * "}}" * } + { "{(\natexlab{" swap$ * "})}" * } + if$ } + if$ + 'extra.label := + label extra.label * 'label := +} +EXECUTE {initialize.extra.label.stuff} +ITERATE {forward.pass} +REVERSE {reverse.pass} +FUNCTION {bib.sort.order} +{ sort.label + " " + * + year field.or.null sortify + * + " " + * + title field.or.null + sort.format.title + * + #1 entry.max$ substring$ + 'sort.key$ := +} +ITERATE {bib.sort.order} +SORT +FUNCTION {begin.bib} +{ preamble$ empty$ + 'skip$ + { preamble$ write$ newline$ } + if$ + "\begin{thebibliography}{" number.label int.to.str$ * "}" * + write$ newline$ + "\expandafter\ifx\csname natexlab\endcsname\relax\def\natexlab#1{#1}\fi" + write$ newline$ +} +EXECUTE {begin.bib} +EXECUTE {init.urlbst.variables} % urlbst +EXECUTE {init.state.consts} +ITERATE {call.type$} +FUNCTION {end.bib} +{ newline$ + "\end{thebibliography}" write$ newline$ +} +EXECUTE {end.bib} +%% End of customized bst file +%% +%% End of file `compling.bst'. diff --git a/references/2019.arxiv.liu/source/main.bbl b/references/2019.arxiv.liu/source/main.bbl new file mode 100644 index 0000000000000000000000000000000000000000..31ed25dce5fca271d3093d63c1ed5f11c5c1f286 --- /dev/null +++ b/references/2019.arxiv.liu/source/main.bbl @@ -0,0 +1,346 @@ +\begin{thebibliography}{51} +\expandafter\ifx\csname natexlab\endcsname\relax\def\natexlab#1{#1}\fi + +\bibitem[{Agirre et~al.(2007)Agirre, M`arquez, and + Wicentowski}]{agirre2007semantic} +Eneko Agirre, Llu'{i}s M`arquez, and Richard Wicentowski, editors. 2007. +\newblock \emph{Proceedings of the Fourth International Workshop on Semantic + Evaluations (SemEval-2007)}. + +\bibitem[{Baevski et~al.(2019)Baevski, Edunov, Liu, Zettlemoyer, and + Auli}]{baevski2019cloze} +Alexei Baevski, Sergey Edunov, Yinhan Liu, Luke Zettlemoyer, and Michael Auli. + 2019. +\newblock Cloze-driven pretraining of self-attention networks. +\newblock \emph{arXiv preprint arXiv:1903.07785}. + +\bibitem[{Bar-Haim et~al.(2006)Bar-Haim, Dagan, Dolan, Ferro, Giampiccolo, + Magnini, and Szpektor}]{bar2006second} +Roy Bar-Haim, Ido Dagan, Bill Dolan, Lisa Ferro, Danilo Giampiccolo, Bernardo + Magnini, and Idan Szpektor. 2006. +\newblock The second {PASCAL} recognising textual entailment challenge. +\newblock In \emph{Proceedings of the second PASCAL challenges workshop on + recognising textual entailment}. + +\bibitem[{Bentivogli et~al.(2009)Bentivogli, Dagan, Dang, Giampiccolo, and + Magnini}]{bentivogli2009fifth} +Luisa Bentivogli, Ido Dagan, Hoa~Trang Dang, Danilo Giampiccolo, and Bernardo + Magnini. 2009. +\newblock The fifth {PASCAL} recognizing textual entailment challenge. + +\bibitem[{Bowman et~al.(2015)Bowman, Angeli, Potts, and + Manning}]{bowman2015large} +Samuel~R Bowman, Gabor Angeli, Christopher Potts, and Christopher~D Manning. + 2015. +\newblock A large annotated corpus for learning natural language inference. +\newblock In \emph{Empirical Methods in Natural Language Processing (EMNLP)}. + +\bibitem[{Chan et~al.(2019)Chan, Kitaev, Guu, Stern, and + Uszkoreit}]{chan2019kermit} +William Chan, Nikita Kitaev, Kelvin Guu, Mitchell Stern, and Jakob Uszkoreit. + 2019. +\newblock {KERMIT}: Generative insertion-based modeling for sequences. +\newblock \emph{arXiv preprint arXiv:1906.01604}. + +\bibitem[{Dagan et~al.(2006)Dagan, Glickman, and Magnini}]{dagan2006pascal} +Ido Dagan, Oren Glickman, and Bernardo Magnini. 2006. +\newblock The {PASCAL} recognising textual entailment challenge. +\newblock In \emph{Machine learning challenges. evaluating predictive + uncertainty, visual object classification, and recognising tectual + entailment}. + +\bibitem[{Dai and Le(2015)}]{dai2015semi} +Andrew~M Dai and Quoc~V Le. 2015. +\newblock Semi-supervised sequence learning. +\newblock In \emph{Advances in Neural Information Processing Systems (NIPS)}. + +\bibitem[{Devlin et~al.(2019)Devlin, Chang, Lee, and + Toutanova}]{devlin2018bert} +Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. +\newblock {BERT}: Pre-training of deep bidirectional transformers for language + understanding. +\newblock In \emph{North American Association for Computational Linguistics + (NAACL)}. + +\bibitem[{Dolan and Brockett(2005)}]{dolan2005automatically} +William~B Dolan and Chris Brockett. 2005. +\newblock Automatically constructing a corpus of sentential paraphrases. +\newblock In \emph{Proceedings of the International Workshop on Paraphrasing}. + +\bibitem[{Dong et~al.(2019)Dong, Yang, Wang, Wei, Liu, Wang, Gao, Zhou, and + Hon}]{dong2019unified} +Li~Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu~Wang, Jianfeng Gao, + Ming Zhou, and Hsiao-Wuen Hon. 2019. +\newblock Unified language model pre-training for natural language + understanding and generation. +\newblock \emph{arXiv preprint arXiv:1905.03197}. + +\bibitem[{Giampiccolo et~al.(2007)Giampiccolo, Magnini, Dagan, and + Dolan}]{giampiccolo2007third} +Danilo Giampiccolo, Bernardo Magnini, Ido Dagan, and Bill Dolan. 2007. +\newblock The third {PASCAL} recognizing textual entailment challenge. +\newblock In \emph{Proceedings of the ACL-PASCAL workshop on textual entailment + and paraphrasing}. + +\bibitem[{Gokaslan and 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Allen School of Computer Science \& Engineering, \\ University of Washington, Seattle, WA \\ +{\tt \{mandar90,lsz\}@cs.washington.edu}\\[4pt] +$^{\mathsection}$ Facebook AI \\ +{\tt \{yinhanliu,myleott,naman,jingfeidu,}\\ +{\tt \quad\quad\quad\quad danqi,omerlevy,mikelewis,lsz,ves\}@fb.com} +} +\date{} + +\begin{document} + +\maketitle + +\input{00-abstract.tex} +\input{01-intro.tex} +\input{02-background.tex} +\input{03-exp_setup.tex} +\input{04-design.tex} +\input{05-roberta.tex} +\input{06-related_work.tex} +\input{07-conclusion.tex} + +\bibliography{main} +\bibliographystyle{acl_natbib} + +\input{08-appendix.tex} + +\end{document} diff --git a/references/2019.arxiv.liu/source/tables/ablation.tex b/references/2019.arxiv.liu/source/tables/ablation.tex new file mode 100644 index 0000000000000000000000000000000000000000..66b4e7c858721a6fb07b90c1f7d04f2e588ab6a9 --- /dev/null +++ b/references/2019.arxiv.liu/source/tables/ablation.tex @@ -0,0 +1,29 @@ +\begin{table*}[t] +\begin{center} +\begin{tabular}{lcccccc} +\toprule +\multirow{2}{*}{\bf Model} & \bf \multirow{2}{*}{data} & \bf \multirow{2}{*}{bsz} & \bf \multirow{2}{*}{steps} & \bf SQuAD & \bf \multirow{2}{*}{MNLI-m} & \bf \multirow{2}{*}{SST-2} \\ +& & & & (v1.1/2.0) & & \\ +\midrule +\multicolumn{4}{l}{\ourmodel{}} \\ +\quad with \textsc{Books} + \textsc{Wiki} & 16GB & 8K & 100K & 93.6/87.3 & 89.0 & 95.3 \\ +\quad + additional data (\textsection\ref{sec:data}) & 160GB & 8K & 100K & 94.0/87.7 & 89.3 & 95.6 \\ +\quad + pretrain longer & 160GB & 8K & 300K & 94.4/88.7 & 90.0 & 96.1 \\ +\quad + pretrain even longer & 160GB & 8K & 500K & \textbf{94.6}/\textbf{89.4} & \textbf{90.2} & \textbf{96.4} \\ +\midrule +\multicolumn{4}{l}{\bertlarge{}} \\ +\quad with \textsc{Books} + \textsc{Wiki} & 13GB & 256 & 1M & 90.9/81.8 & 86.6 & 93.7 \\ +\multicolumn{4}{l}{\xlnetlarge{}} \\ +\quad with \textsc{Books} + \textsc{Wiki} & 13GB & 256 & 1M & 94.0/87.8 & 88.4 & 94.4 \\ +\quad + additional data & 126GB & 2K & 500K & 94.5/88.8 & 89.8 & 95.6 \\ +\bottomrule +\end{tabular} +\end{center} +\caption{Development set results for \ourmodel{} as we pretrain over more data (16GB $\rightarrow$ 160GB of text) and pretrain for longer (100K $\rightarrow$ 300K $\rightarrow$ 500K steps). +Each row accumulates improvements from the rows above. +\ourmodel{} matches the architecture and training objective of \bertlarge{}. +Results for \bertlarge{} and \xlnetlarge{} are from \newcite{devlin2018bert} and \newcite{yang2019xlnet}, respectively. +Complete results on all GLUE tasks can be found in the Appendix. +} +\label{tab:ablation} +\end{table*} \ No newline at end of file diff --git a/references/2019.arxiv.liu/source/tables/base_apples_to_apples.tex b/references/2019.arxiv.liu/source/tables/base_apples_to_apples.tex new file mode 100644 index 0000000000000000000000000000000000000000..e8827d810f3894cda2454887434b4a6a3e1aca19 --- /dev/null +++ b/references/2019.arxiv.liu/source/tables/base_apples_to_apples.tex @@ -0,0 +1,32 @@ +\begin{table*}[t] +\begin{center} +\begin{tabular}{lcccc} +\toprule +\bf Model & \bf SQuAD 1.1/2.0 & \bf MNLI-m & \bf SST-2 & \bf RACE \\ +\midrule +\multicolumn{5}{l}{\emph{Our reimplementation (with NSP loss):}} \\ +\textsc{segment-pair} & 90.4/78.7 & 84.0 & 92.9 & 64.2 \\ +\textsc{sentence-pair} & 88.7/76.2 & 82.9 & 92.1 & 63.0 \\ +\midrule +\multicolumn{5}{l}{\emph{Our reimplementation (without NSP loss):}} \\ +\textsc{full-sentences} & 90.4/79.1 & 84.7 & 92.5 & 64.8 \\ +\textsc{doc-sentences} & 90.6/79.7 & 84.7 & 92.7 & 65.6 \\ +\midrule +\bertbase{} & 88.5/76.3 & 84.3 & 92.8 & 64.3 \\ +\xlnetbase{} (K = 7) & --/81.3 & 85.8 & 92.7 & 66.1 \\ +\xlnetbase{} (K = 6) & --/81.0 & 85.6 & 93.4 & 66.7 \\ +\bottomrule + + + +\end{tabular} +\end{center} +\caption{ +Development set results for base models pretrained over \textsc{BookCorpus} and \textsc{Wikipedia}. +All models are trained for 1M steps with a batch size of 256 sequences. +We report F1 for SQuAD and accuracy for MNLI-m, SST-2 and RACE. +Reported results are medians over five random initializations (seeds). +Results for \bertbase{} and \xlnetbase{} are from \newcite{yang2019xlnet}. +} +\label{tab:base_apples_to_apples} +\end{table*} \ No newline at end of file diff --git a/references/2019.arxiv.liu/source/tables/large_batches.tex b/references/2019.arxiv.liu/source/tables/large_batches.tex new file mode 100644 index 0000000000000000000000000000000000000000..8220d7d5c70387e67340e767969188c6db2696d3 --- /dev/null +++ b/references/2019.arxiv.liu/source/tables/large_batches.tex @@ -0,0 +1,24 @@ +\begin{table}[t] + +\begin{center} +\begin{tabular}{cccccc} +\toprule + + +\bf bsz & \bf steps & \bf lr & \bf ppl & \bf MNLI-m & \bf SST-2 \\ +\midrule +256 & 1M & 1e-4 & 3.99 & 84.7 & 92.7 \\ +2K & 125K & 7e-4 & \textbf{3.68} & \textbf{85.2} & \textbf{92.9} \\ +8K & 31K & 1e-3 & 3.77 & 84.6 & 92.8 \\ +\bottomrule +\end{tabular} +\end{center} +\caption{ +Perplexity on held-out training data (\emph{ppl}) and development set accuracy for base models trained over \textsc{BookCorpus} and \textsc{Wikipedia} with varying batch sizes (\emph{bsz}). +We tune the learning rate (\emph{lr}) for each setting. +Models make the same number of passes over the data (epochs) and have the same computational cost. +} + +\label{tab:large_batches} + +\end{table} \ No newline at end of file diff --git a/references/2019.arxiv.liu/source/tables/pretraining_hyperparams.tex b/references/2019.arxiv.liu/source/tables/pretraining_hyperparams.tex new file mode 100644 index 0000000000000000000000000000000000000000..e93d0a58c294b4a56117a8d0d4751d52ebc91b1c --- /dev/null +++ b/references/2019.arxiv.liu/source/tables/pretraining_hyperparams.tex @@ -0,0 +1,31 @@ +\begin{table*}[t] +\begin{center} +\begin{tabular}{lccc} +\toprule +\bf Hyperparam & \bf \ourmodellarge{} & \bf \ourmodelbase{} \\ +\midrule +Number of Layers & 24 & 12 \\ +Hidden size & 1024 & 768 \\ +FFN inner hidden size & 4096 & 3072 \\ +Attention heads & 16 & 12 \\ +Attention head size & 64 & 64 \\ +Dropout & 0.1 & 0.1 \\ +Attention Dropout & 0.1 & 0.1 \\ +Warmup Steps & 30k & 24k \\ +Peak Learning Rate & 4e-4 & 6e-4 \\ +Batch Size & 8k & 8k\\ +Weight Decay & 0.01 & 0.01 \\ +Max Steps & 500k & 500k\\ +Learning Rate Decay & Linear & Linear \\ +Adam $\epsilon$ & 1e-6 & 1e-6 \\ +Adam $\beta_1$ & 0.9 & 0.9 \\ +Adam $\beta_2$ & 0.98 & 0.98 \\ +Gradient Clipping & 0.0 & 0.0 \\ +\bottomrule +\end{tabular} +\end{center} +\caption{ +Hyperparameters for pretraining \ourmodellarge{} and \ourmodelbase{}. +} +\label{tab:pretraining_hyperparams} +\end{table*} \ No newline at end of file diff --git a/references/2019.arxiv.liu/source/tables/roberta_all_large_glue.tex b/references/2019.arxiv.liu/source/tables/roberta_all_large_glue.tex new file mode 100644 index 0000000000000000000000000000000000000000..4e3978e4bf185c0004fb8fc2a8b6ab5793ee95d8 --- /dev/null +++ b/references/2019.arxiv.liu/source/tables/roberta_all_large_glue.tex @@ -0,0 +1,22 @@ +\begin{table*}[t] +\begin{center} +\begin{tabular}{lcccccccc} +\toprule +\bf & \bf MNLI & \bf QNLI & \bf QQP & \bf RTE & \bf SST & \bf MRPC & \bf CoLA & \bf STS \\ +\midrule +\multicolumn{4}{l}{\ourmodelbase{}} \\ +\quad + all data + 500k steps & 87.6 & 92.8 & 91.9 & 78.7 & 94.8 & 90.2 & 63.6 & 91.2 \\ +\midrule +\multicolumn{4}{l}{\ourmodellarge{}} \\ +\quad with \textsc{Books} + \textsc{Wiki} & 89.0 & 93.9 & 91.9 & 84.5 & 95.3 & 90.2 & 66.3 & 91.6 \\ +\quad + additional data (\textsection\ref{sec:data}) & 89.3 & 94.0 & 92.0 & 82.7 & 95.6 & \textbf{91.4} & 66.1 & 92.2 \\ +\quad + pretrain longer 300k & 90.0 & 94.5 & \textbf{92.2} & 83.3 & 96.1 & 91.1 & 67.4 & 92.3 \\ +\quad + pretrain longer 500k & \textbf{90.2} & \textbf{94.7} & \textbf{92.2} & \textbf{86.6} & \textbf{96.4} & 90.9 & \textbf{68.0} & \textbf{92.4} \\ +\bottomrule +\end{tabular} +\end{center} +\caption{ +Development set results on GLUE tasks for various configurations of \ourmodel{}. +} +\label{tab:roberta_all_large_glue} +\end{table*} \ No newline at end of file diff --git a/references/2019.arxiv.liu/source/tables/roberta_glue.tex b/references/2019.arxiv.liu/source/tables/roberta_glue.tex new file mode 100644 index 0000000000000000000000000000000000000000..779b3ae32f569a7f519056d57923e3a0a457bc38 --- /dev/null +++ b/references/2019.arxiv.liu/source/tables/roberta_glue.tex @@ -0,0 +1,29 @@ +\begin{table*}[t] +\begin{center} +\begin{tabular}{lcccccccccc} +\toprule +& \bf MNLI & \bf QNLI & \bf QQP & \bf RTE & \bf SST & \bf MRPC & \bf CoLA & \bf STS & \bf WNLI & \bf Avg \\ +\midrule +\multicolumn{10}{l}{\textit{Single-task single models on dev}}\\ +\bertlarge{} & 86.6/- & 92.3 & 91.3 & 70.4 & 93.2 & 88.0 & 60.6 & 90.0 & - & -\\ +\xlnetlarge{} & 89.8/- & 93.9 & 91.8 & 83.8 & 95.6 & 89.2 & 63.6 & 91.8 & - & -\\ +\ourmodel{} & \textbf{90.2}/\textbf{90.2} & \textbf{94.7} & \textbf{92.2} & \textbf{86.6} & \textbf{96.4} & \textbf{90.9} & \textbf{68.0} & \textbf{92.4} & \textbf{91.3} & - \\ +\midrule +\multicolumn{10}{l}{\textit{Ensembles on test (from leaderboard as of July 25, 2019)}} \\ +ALICE & 88.2/87.9 & 95.7 & \textbf{90.7} & 83.5 & 95.2 & 92.6 & \textbf{68.6} & 91.1 & 80.8 & 86.3 \\ +MT-DNN & 87.9/87.4 & 96.0 & 89.9 & 86.3 & 96.5 & 92.7 & 68.4 & 91.1 & 89.0 & 87.6 \\ +XLNet & 90.2/89.8 & 98.6 & 90.3 & 86.3 & \textbf{96.8} & \textbf{93.0} & 67.8 & 91.6 & \textbf{90.4} & 88.4 \\ +\ourmodel{} & \textbf{90.8/90.2} & \textbf{98.9} & 90.2 & \textbf{88.2} & 96.7 & 92.3 & 67.8 & \textbf{92.2} & 89.0 & \bf 88.5 \\ +\bottomrule +\end{tabular} +\end{center} +\caption{ +Results on GLUE. All results are based on a 24-layer architecture. +\bertlarge{} and \xlnetlarge{} results are from \newcite{devlin2018bert} and \newcite{yang2019xlnet}, respectively. +\ourmodel{} results on the development set are a median over five runs. +\ourmodel{} results on the test set are ensembles of \emph{single-task} models. +For RTE, STS and MRPC we finetune starting from the MNLI model instead of the baseline pretrained model. +Averages are obtained from the GLUE leaderboard. +} +\label{tab:roberta_glue} +\end{table*} \ No newline at end of file diff --git a/references/2019.arxiv.liu/source/tables/roberta_glue_finetune_hyperparams.tex b/references/2019.arxiv.liu/source/tables/roberta_glue_finetune_hyperparams.tex new file mode 100644 index 0000000000000000000000000000000000000000..0387e795386bca261ec4638b2b93de53b376ed50 --- /dev/null +++ b/references/2019.arxiv.liu/source/tables/roberta_glue_finetune_hyperparams.tex @@ -0,0 +1,20 @@ +\begin{table*}[t] +\begin{center} +\begin{tabular}{lccc} +\toprule +\bf Hyperparam & \bf RACE & \bf SQuAD & \bf GLUE \\ +\midrule +Learning Rate & 1e-5 & 1.5e-5 & \{1e-5, 2e-5, 3e-5\}\\ +Batch Size & 16 & 48 & \{16, 32\}\\ +Weight Decay & 0.1 & 0.01 & 0.1 \\ +Max Epochs & 4 & 2 & 10 \\ +Learning Rate Decay & Linear &Linear & Linear \\ +Warmup ratio & 0.06 & 0.06 & 0.06 \\ +\bottomrule +\end{tabular} +\end{center} +\caption{ +Hyperparameters for finetuning \ourmodellarge{} on RACE, SQuAD and GLUE. +} +\label{tab:roberta_glue_finetune_hyperparams} +\end{table*} \ No newline at end of file diff --git a/references/2019.arxiv.liu/source/tables/roberta_race.tex b/references/2019.arxiv.liu/source/tables/roberta_race.tex new file mode 100644 index 0000000000000000000000000000000000000000..63f765bf34e169169090ad89c41359745d9c1df5 --- /dev/null +++ b/references/2019.arxiv.liu/source/tables/roberta_race.tex @@ -0,0 +1,17 @@ +\begin{table}[t] +\begin{center} +\begin{tabular}{lccc} +\toprule +\bf Model & \bf Accuracy & \bf Middle & \bf High \\ +\midrule +\multicolumn{4}{l}{\textit{Single models on test (as of July 25, 2019)}}\\ +\bertlarge{} & 72.0 & 76.6 & 70.1 \\ +\xlnetlarge{} & 81.7 & 85.4 & 80.2 \\ +\midrule +\ourmodel{} & \bf{83.2} & \bf{86.5} & \bf{81.3}\\ +\bottomrule +\end{tabular} +\end{center} +\caption{Results on the RACE test set. BERT$_{\textsc{large}}$ and XLNet$_{\textsc{large}}$ results are from \newcite{yang2019xlnet}.} +\label{tab:roberta_race} +\end{table} diff --git a/references/2019.arxiv.liu/source/tables/roberta_squad.tex b/references/2019.arxiv.liu/source/tables/roberta_squad.tex new file mode 100644 index 0000000000000000000000000000000000000000..b439882ea8d229f2c5684b8438ccdfbc7e4c4560 --- /dev/null +++ b/references/2019.arxiv.liu/source/tables/roberta_squad.tex @@ -0,0 +1,27 @@ +\begin{table}[t] +\begin{center} +\begin{tabular}{lcccc} +\toprule +\multirow{2}{*}{\bf Model} & \multicolumn{2}{c}{\bf SQuAD 1.1} &\multicolumn{2}{c}{\bf SQuAD 2.0} \\ +& EM & F1 & EM & F1 \\ +\midrule +\multicolumn{5}{l}{\textit{Single models on dev, w/o data augmentation}}\\ +\bertlarge{} & 84.1&90.9&79.0&81.8\\ +\xlnetlarge{} &\bf{89.0}& 94.5&86.1&88.8\\ +\ourmodel{} & 88.9 & \bf{94.6} & \bf{86.5} &\bf{89.4}\\ +\midrule +\multicolumn{5}{l}{\textit{Single models on test (as of July 25, 2019)}}\\ +\multicolumn{3}{l}{\xlnetlarge{}} & 86.3$^{\dag}$ & 89.1$^{\dag}$ \\ +\multicolumn{3}{l}{\ourmodel{}} & 86.8 & 89.8 \\ +\multicolumn{3}{l}{XLNet + SG-Net Verifier} & \textbf{87.0}$^{\dag}$ & \textbf{89.9}$^{\dag}$ \\ +\bottomrule +\end{tabular} +\end{center} +\caption{ +Results on SQuAD. +$\dag$ indicates results that depend on additional external training data. +\ourmodel{} uses only the provided SQuAD data in both dev and test settings. +BERT$_{\textsc{large}}$ and XLNet$_{\textsc{large}}$ results are from \newcite{devlin2018bert} and \newcite{yang2019xlnet}, respectively. +} +\label{tab:roberta_squad} +\end{table} \ No newline at end of file diff --git a/references/2019.arxiv.liu/source/tables/roberta_swag.tex b/references/2019.arxiv.liu/source/tables/roberta_swag.tex new file mode 100644 index 0000000000000000000000000000000000000000..666b49ac29ae7bbb4195752c3a4b4bd69fa41cc9 --- /dev/null +++ b/references/2019.arxiv.liu/source/tables/roberta_swag.tex @@ -0,0 +1,17 @@ +\begin{table}[t] +\begin{center} +\begin{tabular}{lccc} +\toprule +\bf Model & \bf Accuracy \\ +\midrule +\bertlarge{} & 86.6 \\ +\ourmodel{} & \bf{89.9}\\ +\midrule +Human (expert) & 85.0\\ +Human (5 annotations) & 88.0 \\ +\bottomrule +\end{tabular} +\end{center} +\caption{Results on the SWAG test set. \bertlarge{} results are from \newcite{devlin2018bert}. Human performance is from \newcite{zellers2018swag}.} +\label{tab:roberta_swag} +\end{table} \ No newline at end of file diff --git a/references/2019.arxiv.liu/source/tables/static_vs_dynamic_masking.tex b/references/2019.arxiv.liu/source/tables/static_vs_dynamic_masking.tex new file mode 100644 index 0000000000000000000000000000000000000000..51ca4d6e7c656da7a83dddc89605d991fece5e00 --- /dev/null +++ b/references/2019.arxiv.liu/source/tables/static_vs_dynamic_masking.tex @@ -0,0 +1,20 @@ +\begin{table}[t] +\begin{center} +\begin{tabular}{lccc} +\toprule +\bf Masking & \bf SQuAD 2.0 & \bf MNLI-m & \bf SST-2 \\ +\midrule +reference & 76.3 & 84.3 & 92.8 \\ +\midrule +\multicolumn{4}{l}{\emph{Our reimplementation:}} \\ +static & 78.3 & 84.3 & 92.5 \\ +dynamic & 78.7 & 84.0 & 92.9 \\ +\bottomrule +\end{tabular} +\end{center} +\caption{Comparison between static and dynamic masking for \bertbase{}. +We report F1 for SQuAD and accuracy for MNLI-m and SST-2. +Reported results are medians over 5 random initializations (seeds). +Reference results are from \newcite{yang2019xlnet}.} +\label{tab:static_vs_dynamic_masking} +\end{table} diff --git a/references/2020.arxiv.nguyen/paper.md b/references/2020.arxiv.nguyen/paper.md new file mode 100644 index 0000000000000000000000000000000000000000..54ac79f629abcd76b0fc556dba8da507056c9423 --- /dev/null +++ b/references/2020.arxiv.nguyen/paper.md @@ -0,0 +1,229 @@ +--- +title: "PhoBERT: Pre-trained language models for Vietnamese" +authors: + - "Dat Quoc Nguyen" + - "Anh Tuan Nguyen" +year: 2020 +venue: "arXiv" +url: "https://arxiv.org/abs/2003.00744" +arxiv: "2003.00744" +--- + +\maketitle +\begin{abstract} +We present **PhoBERT** with two versions---PhoBERT\textsubscript{base} and PhoBERT\textsubscript{large}---the *first* public large-scale monolingual language models pre-trained for Vietnamese. Experimental results show that PhoBERT consistently outperforms the recent best pre-trained multilingual model XLM-R [conneau2019unsupervised] and improves the state-of-the-art in multiple Vietnamese-specific NLP tasks including Part-of-speech tagging, Dependency parsing, Named-entity recognition and Natural language inference. We release PhoBERT to facilitate future research and downstream applications for Vietnamese NLP. Our PhoBERT models are available at: https://github.com/VinAIResearch/PhoBERT. +\end{abstract} + +# Introduction\label{sec:intro} + +Pre-trained language models, especially BERT [devlin-etal-2019-bert]---the Bidirectional Encoder Representations from Transformers [NIPS2017_7181], have recently become extremely popular and helped to produce significant improvement gains for various NLP tasks. The success of pre-trained BERT and its variants has largely been limited to the English language. For other languages, one could retrain a language-specific model using the BERT architecture [abs-1906-08101,vries2019bertje,vu-xuan-etal-2019-etnlp,2019arXiv191103894M] or employ existing pre-trained multilingual BERT-based models [devlin-etal-2019-bert,NIPS2019_8928,conneau2019unsupervised]. + +In terms of Vietnamese language modeling, to the best of our knowledge, there are two main concerns as follows: + +[leftmargin=*] +\setlength- sep{-1pt} + - The Vietnamese Wikipedia corpus is the only data used to train monolingual language models [vu-xuan-etal-2019-etnlp], and it also is the only Vietnamese dataset which is included in the pre-training data used by all multilingual language models except XLM-R. It is worth noting that Wikipedia data is not representative of a general language use, and the Vietnamese Wikipedia data is relatively small (1GB in size uncompressed), while pre-trained language models can be significantly improved by using more pre-training data [RoBERTa]. + + - All publicly released monolingual and multilingual BERT-based language models are not aware of the difference between Vietnamese syllables and word tokens. This ambiguity comes from the fact that the white space is also used to separate syllables that constitute words when written in Vietnamese.\footnote{\newcite{DinhQuangThang2008} show that 85\ + For example, a 6-syllable written text ``Tôi là một nghiên cứu viên'' (I am a researcher) forms 4 words ``Tôi\textsubscript{I} là\textsubscript{am} một\textsubscript{a} nghiên\_cứu\_viên\textsubscript{researcher}''. \\ +Without doing a pre-process step of Vietnamese word segmentation, those models directly apply Byte-Pair encoding (BPE) methods [sennrich-etal-2016-neural,kudo-richardson-2018-sentencepiece] to the syllable-level Vietnamese pre-training data.\footnote{Although performing word segmentation before applying BPE on the Vietnamese Wikipedia corpus, ETNLP [vu-xuan-etal-2019-etnlp] in fact {does not publicly release} any pre-trained BERT-based language model (https://github.com/vietnlp/etnlp). In particular, \newcite{vu-xuan-etal-2019-etnlp} release a set of 15K BERT-based word embeddings specialized only for the Vietnamese NER task.} +Intuitively, for word-level Vietnamese NLP tasks, those models pre-trained on syllable-level data might not perform as good as language models pre-trained on word-level data. + +To handle the two concerns above, we train the {first} large-scale monolingual BERT-based ``base'' and ``large'' models using a 20GB *word-level* Vietnamese corpus. +We evaluate our models on four downstream Vietnamese NLP tasks: the common word-level ones of Part-of-speech (POS) tagging, Dependency parsing and Named-entity recognition (NER), and a language understanding task of Natural language inference (NLI) which can be formulated as either a syllable- or word-level task. Experimental results show that our models obtain state-of-the-art (SOTA) results on all these tasks. +Our contributions are summarized as follows: + +[leftmargin=*] +\setlength- sep{-1pt} + - We present the *first* large-scale monolingual language models pre-trained for Vietnamese. + + - Our models help produce SOTA performances on four downstream tasks of POS tagging, Dependency parsing, NER and NLI, thus showing the effectiveness of large-scale BERT-based monolingual language models for Vietnamese. + + - To the best of our knowledge, we also perform the *first* set of experiments to compare monolingual language models with the recent best multilingual model XLM-R in multiple (i.e. four) different language-specific tasks. The experiments show that our models outperform XLM-R on all these tasks, thus convincingly confirming that dedicated language-specific models still outperform multilingual ones. + + - We publicly release our models under the name PhoBERT which can be used with `fairseq` [ott2019fairseq] and `transformers` [Wolf2019HuggingFacesTS]. We hope that PhoBERT can serve as a strong baseline for future Vietnamese NLP research and applications. + + + + + + +# PhoBERT + +This section outlines the architecture and describes the pre-training data and optimization setup that we use for PhoBERT. + +\vspace{3pt} + +\noindent**Architecture:**\ Our PhoBERT has two versions, PhoBERT\textsubscript{base} and PhoBERT\textsubscript{large}, using the same architectures of BERT\textsubscript{base} and BERT\textsubscript{large}, respectively. PhoBERT pre-training approach is based on RoBERTa [RoBERTa] which optimizes the BERT pre-training procedure for more robust performance. + +\vspace{3pt} + +\noindent**Pre-training data:**\ To handle the first concern mentioned in Section \ref{sec:intro}, we use a 20GB pre-training dataset of uncompressed texts. This dataset is a concatenation of two corpora: (i) the first one is the Vietnamese Wikipedia corpus ($\sim$1GB), and (ii) the second corpus ($\sim$19GB) is generated by removing similar articles and duplication from a 50GB Vietnamese news corpus.\footnote{https://github.com/binhvq/news-corpus, crawled from a wide range of news websites and topics.} To solve the second concern, +we employ RDRSegmenter [nguyen-etal-2018-fast] from VnCoreNLP [vu-etal-2018-vncorenlp] to perform word and sentence segmentation on the pre-training dataset, resulting in $\sim$145M word-segmented sentences ($\sim$3B word tokens). Different from RoBERTa, we then apply `fastBPE` [sennrich-etal-2016-neural] to segment these sentences with subword units, using a vocabulary of 64K subword types. On average there are 24.4 subword tokens per sentence. + +\vspace{3pt} + +\noindent**Optimization:**\ We employ the RoBERTa implementation in `fairseq` [ott2019fairseq]. We set a maximum length at 256 subword tokens, thus generating 145M $\times$ 24.4 / 256 $\approx$ 13.8M sentence blocks. Following \newcite{RoBERTa}, we optimize the models using Adam [KingmaB14]. We use a batch size of 1024 across 4 V100 GPUs (16GB each) and a peak learning rate of 0.0004 for PhoBERT\textsubscript{base}, and a batch size of 512 and a peak learning rate of 0.0002 for PhoBERT\textsubscript{large}. We run for 40 epochs (here, the learning rate is warmed up for 2 epochs), thus resulting in 13.8M $\times$ 40 / 1024 $\approx$ 540K training steps for PhoBERT\textsubscript{base} and 1.08M training steps for PhoBERT\textsubscript{large}. We pre-train PhoBERT\textsubscript{base} during 3 weeks, and then PhoBERT\textsubscript{large} during 5 weeks. + +\begin{table}[!t] + \centering + \begin{tabular}{l|l|l|l} + \hline + **Task** & **\#training** & **\#valid** & **\#test** \\ + \hline + + POS tagging$^\dagger$ & 27,000 & 870 & 2,120 \\ + Dep. parsing$^\dagger$ & 8,977 & 200 & 1,020 \\ + NER$^\dagger$ & 14,861 & 2,000 & 2,831\\ + NLI$^\ddagger$ & 392,702 & 2,490 & 5,010\\ + \hline + \end{tabular} + \caption{Statistics of the downstream task datasets. ``\#training'', ``\#valid'' and ``\#test'' denote the size of the training, validation and test sets, respectively. $\dagger$ and $\ddagger$ refer to the dataset size as the numbers of sentences and sentence pairs, respectively.} + \label{tab:data} +\end{table} + + + \begin{table*}[!ht] + \centering + \resizebox{15.5cm}{!}{ + + \begin{tabular}{l|l|l|l} + \hline + \multicolumn{2}{c|}{**POS tagging** (word-level)} & \multicolumn{2}{c}{**Dependency parsing** (word-level)}\\ + \hline + Model & Acc. & Model & LAS / UAS \\ + \hline + RDRPOSTagger [nguyen-etal-2014-rdrpostagger] [$\clubsuit$] & 95.1 & \_ & \_ \\ + + BiLSTM-CNN-CRF [ma-hovy-2016-end] [$\clubsuit$] & 95.4 & VnCoreNLP-DEP [vu-etal-2018-vncorenlp] [$\bigstar$] & 71.38 / 77.35 \\ + + + VnCoreNLP-POS [nguyen-etal-2017-word] [$\clubsuit$] & 95.9 &jPTDP-v2 [$\bigstar$] & 73.12 / 79.63 \\ + + jPTDP-v2 [nguyen-verspoor-2018-improved] [$\bigstar$] & 95.7 &jointWPD [$\bigstar$] & 73.90 / 80.12 \\ + + jointWPD [nguyen-2019-neural] [$\bigstar$] & 96.0 & Biaffine [DozatM17] [$\bigstar$] & 74.99 / 81.19 \\ + + XLM-R\textsubscript{base} (our result) & 96.2 & Biaffine w/ XLM-R\textsubscript{base} (our result) & 76.46 / 83.10 \\ + + XLM-R\textsubscript{large} (our result) & 96.3 & Biaffine w/ XLM-R\textsubscript{large} (our result) & 75.87 / 82.70 \\ + + \hline + PhoBERT\textsubscript{base} & \underline{96.7} & Biaffine w/ PhoBERT\textsubscript{base} & **78.77** / **85.22** \\ + + PhoBERT\textsubscript{large} & **96.8** & Biaffine w/ PhoBERT\textsubscript{large} & \underline{77.85} / \underline{84.32} \\ + \hline + \end{tabular} + } + \caption{Performance scores (in \ + [$\clubsuit$] and [$\bigstar$] denote + results reported by \newcite{nguyen-etal-2017-word} and \newcite{nguyen-2019-neural}, respectively.} + \label{tab:posdep} + \end{table*} + + + +# Experimental setup + + We evaluate the performance of PhoBERT on four downstream Vietnamese NLP tasks: POS tagging, Dependency parsing, NER and NLI. + + +### Downstream task datasets + +Table \ref{tab:data} presents the statistics of the experimental datasets that we employ for downstream task evaluation. +For POS tagging, Dependency parsing and NER, we follow the VnCoreNLP setup [vu-etal-2018-vncorenlp], using standard benchmarks of the VLSP 2013 POS tagging dataset,\footnote{https://vlsp.org.vn/vlsp2013/eval} the VnDT dependency treebank v1.1 [Nguyen2014NLDB] with POS tags predicted by VnCoreNLP and the VLSP 2016 NER dataset [JCC13161]. + +For NLI, we use the manually-constructed Vietnamese validation and test sets from the cross-lingual NLI (XNLI) corpus v1.0 [conneau-etal-2018-xnli] where the Vietnamese training set is released as a machine-translated version of the corresponding English training set [N18-1101]. +Unlike the POS tagging, Dependency parsing and NER datasets which provide the gold word segmentation, for NLI, we employ RDRSegmenter to segment the text into words before applying BPE to produce subwords from word tokens. + +### Fine-tuning + +Following \newcite{devlin-etal-2019-bert}, for POS tagging and NER, we append a linear prediction layer on top of the PhoBERT architecture (i.e. to the last Transformer layer of PhoBERT) w.r.t. the first subword of each word token.\footnote{In our preliminary experiments, using the average of contextualized embeddings of subword tokens of each word to represent the word produces slightly lower performance than using the contextualized embedding of the first subword.} +For dependency parsing, following \newcite{nguyen-2019-neural}, we employ a reimplementation of the state-of-the-art Biaffine dependency parser [DozatM17] from \newcite{ma-etal-2018-stack} with default optimal hyper-parameters. +We then extend this parser by replacing the pre-trained word embedding of each word in an input sentence by the corresponding contextualized embedding (from the last layer) computed for the first subword token of the word. + +For POS tagging, NER and NLI, we employ `transformers` [Wolf2019HuggingFacesTS] to fine-tune PhoBERT for each task and each dataset independently. We use AdamW [loshchilov2018decoupled] with a fixed learning rate of 1.e-5 and a batch size of 32 [RoBERTa]. We fine-tune in 30 training epochs, evaluate the task performance after each epoch on the validation set (here, early stopping is applied when there is no improvement after 5 continuous epochs), and then select the best model checkpoint to report the final result on the test set (note that each of our scores is an average over 5 runs with different random seeds). + + + \begin{table*}[!ht] + \centering + \resizebox{15.5cm}{!}{ + + \begin{tabular}{l|l|l|l} + \hline + \multicolumn{2}{c|}{**NER** (word-level)} & \multicolumn{2}{c}{**NLI** (syllable- or word-level)} \\ + + \hline + Model & F\textsubscript{1} & Model & Acc. \\ + \hline + BiLSTM-CNN-CRF [$\blacklozenge$] & 88.3 & \_ & \_\\ + + VnCoreNLP-NER [vu-etal-2018-vncorenlp] [$\blacklozenge$] & 88.6 & BiLSTM-max [conneau-etal-2018-xnli] & 66.4 \\ + + + VNER [8713740] & 89.6 & mBiLSTM [ArtetxeS19] & 72.0 \\ + + BiLSTM-CNN-CRF + ETNLP [$\spadesuit$] & 91.1 & multilingual BERT [devlin-etal-2019-bert] [$\blacksquare$] & 69.5 \\ + + VnCoreNLP-NER + ETNLP [$\spadesuit$] & 91.3 & XLM\textsubscript{MLM+TLM} [NIPS2019_8928] & 76.6 \\ + + XLM-R\textsubscript{base} (our result) & 92.0 & XLM-R\textsubscript{base} [conneau2019unsupervised] & {75.4} \\ + + XLM-R\textsubscript{large} (our result) & 92.8 & XLM-R\textsubscript{large} [conneau2019unsupervised] & \underline{79.7} \\ + + \hline + PhoBERT\textsubscript{base}& \underline{93.6} & PhoBERT\textsubscript{base}& {78.5} \\ + + PhoBERT\textsubscript{large}& **94.7** & PhoBERT\textsubscript{large}& **80.0** \\ + \hline + + + \end{tabular} + } + \caption{Performance scores (in \ + [$\blacklozenge$], [$\spadesuit$] and [$\blacksquare$] denote + results reported by \newcite{vu-etal-2018-vncorenlp}, \newcite{vu-xuan-etal-2019-etnlp} and \newcite{wu-dredze-2019-beto}, respectively. + + Note that there are higher Vietnamese NLI results reported for XLM-R when fine-tuning on the concatenation of all 15 training datasets from the XNLI corpus (i.e. TRANSLATE-TRAIN-ALL: 79.5\ + \label{tab:nernli} + \end{table*} + +# Experimental results\label{sec:results} + +### Main results + +Tables \ref{tab:posdep} and \ref{tab:nernli} compare PhoBERT scores with the previous highest reported results, using the same experimental setup. It is clear that our PhoBERT helps produce new SOTA performance results for all four downstream tasks. + +For \underline{POS tagging}, the neural model jointWPD for joint POS tagging and dependency parsing [nguyen-2019-neural] and the feature-based model VnCoreNLP-POS [nguyen-etal-2017-word] are the two previous SOTA models, obtaining accuracies at about 96.0\ + +For \underline{Dependency parsing}, the previous highest parsing scores LAS and UAS are obtained by the Biaffine parser at 75.0\ + +For \underline{NER}, PhoBERT\textsubscript{large} produces 1.1 points higher F\textsubscript{1} than PhoBERT\textsubscript{base}. In addition, PhoBERT\textsubscript{base} obtains 2+ points higher than the previous SOTA feature- and neural network-based models VnCoreNLP-NER [vu-etal-2018-vncorenlp] and BiLSTM-CNN-CRF [ma-hovy-2016-end] which are trained with the set of 15K BERT-based ETNLP word embeddings [vu-xuan-etal-2019-etnlp]. + + For \underline{NLI}, +PhoBERT outperforms the multilingual BERT [devlin-etal-2019-bert] and the BERT-based cross-lingual model with a new translation language modeling objective XLM\textsubscript{MLM+TLM} [NIPS2019_8928] by large margins. PhoBERT also performs better than the recent best pre-trained multilingual model XLM-R but using far fewer parameters than XLM-R: 135M (PhoBERT\textsubscript{base}) vs. 250M (XLM-R\textsubscript{base}); 370M (PhoBERT\textsubscript{large}) vs. 560M (XLM-R\textsubscript{large}). + + +### Discussion + +We find that PhoBERT\textsubscript{large} achieves 0.9\ + +Using more pre-training data can significantly improve the quality of the pre-trained language models [RoBERTa]. Thus it is not surprising that PhoBERT helps produce better performance than ETNLP on NER, and the multilingual BERT and XLM\textsubscript{MLM+TLM} on NLI (here, PhoBERT uses 20GB of Vietnamese texts while those models employ the 1GB Vietnamese Wikipedia corpus). + +Following the fine-tuning approach that we use for PhoBERT, we carefully fine-tune XLM-R for the remaining Vietnamese POS tagging, Dependency parsing and NER tasks (here, it is applied to the first sub-syllable token of the first syllable of each word).\footnote{For fine-tuning XLM-R, we use a grid search on the validation set to select the AdamW learning rate from \{5e-6, 1e-5, 2e-5, 4e-5\} and the batch size from \{16, 32\}.} +Tables \ref{tab:posdep} and \ref{tab:nernli} show that our PhoBERT also does better than XLM-R on these three word-level tasks. +It is worth noting that XLM-R uses a 2.5TB pre-training corpus which contains 137GB of Vietnamese texts (i.e. about 137\ /\ 20 $\approx$ 7 times bigger than our pre-training corpus). +Recall that PhoBERT performs Vietnamese word segmentation to segment syllable-level sentences into word tokens before applying BPE to segment the word-segmented sentences into subword units, while XLM-R directly applies BPE to the syllable-level Vietnamese pre-training sentences. + This reconfirms that the dedicated language-specific models still outperform the multilingual ones [2019arXiv191103894M].\footnote{Note that \newcite{2019arXiv191103894M} only compare their model CamemBERT with XLM-R on the French NLI task.} + + + # Conclusion + +In this paper, we have presented the first large-scale monolingual PhoBERT language models pre-trained for Vietnamese. We demonstrate the usefulness of PhoBERT by showing that PhoBERT performs better than the recent best multilingual model XLM-R and helps produce the SOTA performances for four downstream Vietnamese NLP tasks of POS tagging, Dependency parsing, NER and NLI. +By publicly releasing PhoBERT models, +we hope that they can foster future research and applications in Vietnamese NLP. + +{ +\bibliographystyle{acl_natbib} +\bibliography{REFs} +} \ No newline at end of file diff --git a/references/2020.arxiv.nguyen/paper.pdf b/references/2020.arxiv.nguyen/paper.pdf new file mode 100644 index 0000000000000000000000000000000000000000..3d808d7fae959ae5c4583cab62e406ab30234ca6 --- /dev/null +++ b/references/2020.arxiv.nguyen/paper.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a125bee31d51587e147067accde30da98815a185d5e0741080b72c99680d7a48 +size 216294 diff --git a/references/2020.arxiv.nguyen/paper.tex b/references/2020.arxiv.nguyen/paper.tex new file mode 100644 index 0000000000000000000000000000000000000000..56780c089d7eee7ee5ef9e23be4d4ac72eea3db9 --- /dev/null +++ b/references/2020.arxiv.nguyen/paper.tex @@ -0,0 +1,301 @@ +\documentclass[11pt,a4paper]{article} +\usepackage[hyperref]{emnlp2020} +\pdfoutput=1 +\usepackage{times} +\usepackage{latexsym} +%\renewcommand{\UrlFont}{\ttfamily\small} + +\usepackage{times} +\usepackage{latexsym} +\usepackage{amsmath} +\usepackage{url} +\usepackage{amssymb} +\usepackage{amsfonts} +\usepackage{graphicx} +\usepackage{tabularx} +\usepackage{multirow} +\usepackage{arydshln} +\usepackage{mathtools,nccmath} + +\usepackage[utf8]{inputenc} +\usepackage[utf8]{vietnam} +\usepackage{enumitem} +% This is not strictly necessary, and may be commented out, +% but it will improve the layout of the manuscript, +% and will typically save some space. +%\usepackage{microtype} + + +\setlength{\textfloatsep}{15pt plus 5.0pt minus 5.0pt} +\setlength{\floatsep}{15pt plus 5.0pt minus 5.0pt} +%\setlength{\dbltextfloatsep }{15pt plus 2.0pt minus 3.0pt} +%\setlength{\dblfloatsep}{15pt plus 2.0pt minus 3.0pt} +%\setlength{\intextsep}{15pt plus 2.0pt minus 3.0pt} +\setlength{\abovecaptionskip}{3pt plus 1pt minus 1pt} + +\aclfinalcopy % Uncomment this line for the final submission +%\def\aclpaperid{***} % Enter the acl Paper ID here + +\setlength\titlebox{5cm} +% You can expand the titlebox if you need extra space +% to show all the authors. Please do not make the titlebox +% smaller than 5cm (the original size); we will check this +% in the camera-ready version and ask you to change it back. + +\newcommand\BibTeX{B\textsc{ib}\TeX} + + +\title{PhoBERT: Pre-trained language models for Vietnamese} + +\author{Dat Quoc Nguyen$^1$ \and Anh Tuan Nguyen$^{2,}$\thanks{\ \ Work done during internship at VinAI Research.} \\ + $^1$VinAI Research, Vietnam; $^2$NVIDIA, USA\\ + \tt{\normalsize v.datnq9@vinai.io, tuananhn@nvidia.com}} + +\date{} + +\begin{document} +\maketitle +\begin{abstract} +We present \textbf{PhoBERT} with two versions---PhoBERT\textsubscript{base} and PhoBERT\textsubscript{large}---the \emph{first} public large-scale monolingual language models pre-trained for Vietnamese. Experimental results show that PhoBERT consistently outperforms the recent best pre-trained multilingual model XLM-R \citep{conneau2019unsupervised} and improves the state-of-the-art in multiple Vietnamese-specific NLP tasks including Part-of-speech tagging, Dependency parsing, Named-entity recognition and Natural language inference. We release PhoBERT to facilitate future research and downstream applications for Vietnamese NLP. Our PhoBERT models are available at: \url{https://github.com/VinAIResearch/PhoBERT}. +\end{abstract} + +\section{Introduction}\label{sec:intro} + + +Pre-trained language models, especially BERT \citep{devlin-etal-2019-bert}---the Bidirectional Encoder Representations from Transformers \citep{NIPS2017_7181}, have recently become extremely popular and helped to produce significant improvement gains for various NLP tasks. The success of pre-trained BERT and its variants has largely been limited to the English language. For other languages, one could retrain a language-specific model using the BERT architecture \citep{abs-1906-08101,vries2019bertje,vu-xuan-etal-2019-etnlp,2019arXiv191103894M} or employ existing pre-trained multilingual BERT-based models \citep{devlin-etal-2019-bert,NIPS2019_8928,conneau2019unsupervised}. + +In terms of Vietnamese language modeling, to the best of our knowledge, there are two main concerns as follows: + +\begin{itemize}[leftmargin=*] +\setlength\itemsep{-1pt} + \item The Vietnamese Wikipedia corpus is the only data used to train monolingual language models \citep{vu-xuan-etal-2019-etnlp}, and it also is the only Vietnamese dataset which is included in the pre-training data used by all multilingual language models except XLM-R. It is worth noting that Wikipedia data is not representative of a general language use, and the Vietnamese Wikipedia data is relatively small (1GB in size uncompressed), while pre-trained language models can be significantly improved by using more pre-training data \cite{RoBERTa}. + + \item All publicly released monolingual and multilingual BERT-based language models are not aware of the difference between Vietnamese syllables and word tokens. This ambiguity comes from the fact that the white space is also used to separate syllables that constitute words when written in Vietnamese.\footnote{\newcite{DinhQuangThang2008} show that 85\% of Vietnamese word types are composed of at least two syllables.} + For example, a 6-syllable written text ``Tôi là một nghiên cứu viên'' (I am a researcher) forms 4 words ``Tôi\textsubscript{I} là\textsubscript{am} một\textsubscript{a} nghiên\_cứu\_viên\textsubscript{researcher}''. \\ +Without doing a pre-process step of Vietnamese word segmentation, those models directly apply Byte-Pair encoding (BPE) methods \citep{sennrich-etal-2016-neural,kudo-richardson-2018-sentencepiece} to the syllable-level Vietnamese pre-training data.\footnote{Although performing word segmentation before applying BPE on the Vietnamese Wikipedia corpus, ETNLP \citep{vu-xuan-etal-2019-etnlp} in fact {does not publicly release} any pre-trained BERT-based language model (\url{https://github.com/vietnlp/etnlp}). In particular, \newcite{vu-xuan-etal-2019-etnlp} release a set of 15K BERT-based word embeddings specialized only for the Vietnamese NER task.} +Intuitively, for word-level Vietnamese NLP tasks, those models pre-trained on syllable-level data might not perform as good as language models pre-trained on word-level data. + +\end{itemize} + + +To handle the two concerns above, we train the {first} large-scale monolingual BERT-based ``base'' and ``large'' models using a 20GB \textit{word-level} Vietnamese corpus. +We evaluate our models on four downstream Vietnamese NLP tasks: the common word-level ones of Part-of-speech (POS) tagging, Dependency parsing and Named-entity recognition (NER), and a language understanding task of Natural language inference (NLI) which can be formulated as either a syllable- or word-level task. Experimental results show that our models obtain state-of-the-art (SOTA) results on all these tasks. +Our contributions are summarized as follows: + +\begin{itemize}[leftmargin=*] +\setlength\itemsep{-1pt} + \item We present the \textit{first} large-scale monolingual language models pre-trained for Vietnamese. + + \item Our models help produce SOTA performances on four downstream tasks of POS tagging, Dependency parsing, NER and NLI, thus showing the effectiveness of large-scale BERT-based monolingual language models for Vietnamese. + + \item To the best of our knowledge, we also perform the \textit{first} set of experiments to compare monolingual language models with the recent best multilingual model XLM-R in multiple (i.e. four) different language-specific tasks. The experiments show that our models outperform XLM-R on all these tasks, thus convincingly confirming that dedicated language-specific models still outperform multilingual ones. + + \item We publicly release our models under the name PhoBERT which can be used with \texttt{fairseq} \citep{ott2019fairseq} and \texttt{transformers} \cite{Wolf2019HuggingFacesTS}. We hope that PhoBERT can serve as a strong baseline for future Vietnamese NLP research and applications. +\end{itemize} + + + + + + + + +\section{PhoBERT} + +This section outlines the architecture and describes the pre-training data and optimization setup that we use for PhoBERT. + +\vspace{3pt} + +\noindent\textbf{Architecture:}\ Our PhoBERT has two versions, PhoBERT\textsubscript{base} and PhoBERT\textsubscript{large}, using the same architectures of BERT\textsubscript{base} and BERT\textsubscript{large}, respectively. PhoBERT pre-training approach is based on RoBERTa \citep{RoBERTa} which optimizes the BERT pre-training procedure for more robust performance. + +\vspace{3pt} + +\noindent\textbf{Pre-training data:}\ To handle the first concern mentioned in Section \ref{sec:intro}, we use a 20GB pre-training dataset of uncompressed texts. This dataset is a concatenation of two corpora: (i) the first one is the Vietnamese Wikipedia corpus ($\sim$1GB), and (ii) the second corpus ($\sim$19GB) is generated by removing similar articles and duplication from a 50GB Vietnamese news corpus.\footnote{\url{https://github.com/binhvq/news-corpus}, crawled from a wide range of news websites and topics.} To solve the second concern, +we employ RDRSegmenter \citep{nguyen-etal-2018-fast} from VnCoreNLP \citep{vu-etal-2018-vncorenlp} to perform word and sentence segmentation on the pre-training dataset, resulting in $\sim$145M word-segmented sentences ($\sim$3B word tokens). Different from RoBERTa, we then apply \texttt{fastBPE} \citep{sennrich-etal-2016-neural} to segment these sentences with subword units, using a vocabulary of 64K subword types. On average there are 24.4 subword tokens per sentence. + +\vspace{3pt} + +\noindent\textbf{Optimization:}\ We employ the RoBERTa implementation in \texttt{fairseq} \citep{ott2019fairseq}. We set a maximum length at 256 subword tokens, thus generating 145M $\times$ 24.4 / 256 $\approx$ 13.8M sentence blocks. Following \newcite{RoBERTa}, we optimize the models using Adam \citep{KingmaB14}. We use a batch size of 1024 across 4 V100 GPUs (16GB each) and a peak learning rate of 0.0004 for PhoBERT\textsubscript{base}, and a batch size of 512 and a peak learning rate of 0.0002 for PhoBERT\textsubscript{large}. We run for 40 epochs (here, the learning rate is warmed up for 2 epochs), thus resulting in 13.8M $\times$ 40 / 1024 $\approx$ 540K training steps for PhoBERT\textsubscript{base} and 1.08M training steps for PhoBERT\textsubscript{large}. We pre-train PhoBERT\textsubscript{base} during 3 weeks, and then PhoBERT\textsubscript{large} during 5 weeks. + + +\begin{table}[!t] + \centering + \begin{tabular}{l|l|l|l} + \hline + \textbf{Task} & \textbf{\#training} & \textbf{\#valid} & \textbf{\#test} \\ + \hline + + POS tagging$^\dagger$ & 27,000 & 870 & 2,120 \\ + Dep. parsing$^\dagger$ & 8,977 & 200 & 1,020 \\ + NER$^\dagger$ & 14,861 & 2,000 & 2,831\\ + NLI$^\ddagger$ & 392,702 & 2,490 & 5,010\\ + \hline + \end{tabular} + \caption{Statistics of the downstream task datasets. ``\#training'', ``\#valid'' and ``\#test'' denote the size of the training, validation and test sets, respectively. $\dagger$ and $\ddagger$ refer to the dataset size as the numbers of sentences and sentence pairs, respectively.} + \label{tab:data} +\end{table} + + + \begin{table*}[!ht] + \centering + \resizebox{15.5cm}{!}{ + %\setlength{\tabcolsep}{0.3em} + \begin{tabular}{l|l|l|l} + \hline + \multicolumn{2}{c|}{\textbf{POS tagging} (word-level)} & \multicolumn{2}{c}{\textbf{Dependency parsing} (word-level)}\\ + \hline + Model & Acc. & Model & LAS / UAS \\ + \hline + RDRPOSTagger \citep{nguyen-etal-2014-rdrpostagger} [$\clubsuit$] & 95.1 & \_ & \_ \\ + + BiLSTM-CNN-CRF \citep{ma-hovy-2016-end} [$\clubsuit$] & 95.4 & VnCoreNLP-DEP \citep{vu-etal-2018-vncorenlp} [$\bigstar$] & 71.38 / 77.35 \\ + + + VnCoreNLP-POS \citep{nguyen-etal-2017-word} [$\clubsuit$] & 95.9 &jPTDP-v2 [$\bigstar$] & 73.12 / 79.63 \\ + + jPTDP-v2 \citep{nguyen-verspoor-2018-improved} [$\bigstar$] & 95.7 &jointWPD [$\bigstar$] & 73.90 / 80.12 \\ + + jointWPD \citep{nguyen-2019-neural} [$\bigstar$] & 96.0 & Biaffine \citep{DozatM17} [$\bigstar$] & 74.99 / 81.19 \\ + + XLM-R\textsubscript{base} (our result) & 96.2 & Biaffine w/ XLM-R\textsubscript{base} (our result) & 76.46 / 83.10 \\ + + XLM-R\textsubscript{large} (our result) & 96.3 & Biaffine w/ XLM-R\textsubscript{large} (our result) & 75.87 / 82.70 \\ + + \hline + PhoBERT\textsubscript{base} & \underline{96.7} & Biaffine w/ PhoBERT\textsubscript{base} & \textbf{78.77} / \textbf{85.22} \\ + + PhoBERT\textsubscript{large} & \textbf{96.8} & Biaffine w/ PhoBERT\textsubscript{large} & \underline{77.85} / \underline{84.32} \\ + \hline + \end{tabular} + } + \caption{Performance scores (in \%) on the POS tagging and Dependency parsing test sets. ``Acc.'', ``LAS'' and ``UAS'' abbreviate the Accuracy, the Labeled Attachment Score and the Unlabeled Attachment Score, respectively (here, all these evaluation metrics are computed on all word tokens, including punctuation). + [$\clubsuit$] and [$\bigstar$] denote + results reported by \newcite{nguyen-etal-2017-word} and \newcite{nguyen-2019-neural}, respectively.} + \label{tab:posdep} + \end{table*} + + + +\section{Experimental setup} + + We evaluate the performance of PhoBERT on four downstream Vietnamese NLP tasks: POS tagging, Dependency parsing, NER and NLI. + + +\subsubsection*{Downstream task datasets} + +Table \ref{tab:data} presents the statistics of the experimental datasets that we employ for downstream task evaluation. +For POS tagging, Dependency parsing and NER, we follow the VnCoreNLP setup \citep{vu-etal-2018-vncorenlp}, using standard benchmarks of the VLSP 2013 POS tagging dataset,\footnote{\url{https://vlsp.org.vn/vlsp2013/eval}} the VnDT dependency treebank v1.1 \cite{Nguyen2014NLDB} with POS tags predicted by VnCoreNLP and the VLSP 2016 NER dataset \citep{JCC13161}. + +For NLI, we use the manually-constructed Vietnamese validation and test sets from the cross-lingual NLI (XNLI) corpus v1.0 \citep{conneau-etal-2018-xnli} where the Vietnamese training set is released as a machine-translated version of the corresponding English training set \citep{N18-1101}. +Unlike the POS tagging, Dependency parsing and NER datasets which provide the gold word segmentation, for NLI, we employ RDRSegmenter to segment the text into words before applying BPE to produce subwords from word tokens. + +\subsubsection*{Fine-tuning} + +Following \newcite{devlin-etal-2019-bert}, for POS tagging and NER, we append a linear prediction layer on top of the PhoBERT architecture (i.e. to the last Transformer layer of PhoBERT) w.r.t. the first subword of each word token.\footnote{In our preliminary experiments, using the average of contextualized embeddings of subword tokens of each word to represent the word produces slightly lower performance than using the contextualized embedding of the first subword.} +For dependency parsing, following \newcite{nguyen-2019-neural}, we employ a reimplementation of the state-of-the-art Biaffine dependency parser \citep{DozatM17} from \newcite{ma-etal-2018-stack} with default optimal hyper-parameters. %\footnote{\url{https://github.com/XuezheMax/NeuroNLP2}} +We then extend this parser by replacing the pre-trained word embedding of each word in an input sentence by the corresponding contextualized embedding (from the last layer) computed for the first subword token of the word. + +For POS tagging, NER and NLI, we employ \texttt{transformers} \cite{Wolf2019HuggingFacesTS} to fine-tune PhoBERT for each task and each dataset independently. We use AdamW \citep{loshchilov2018decoupled} with a fixed learning rate of 1.e-5 and a batch size of 32 \citep{RoBERTa}. We fine-tune in 30 training epochs, evaluate the task performance after each epoch on the validation set (here, early stopping is applied when there is no improvement after 5 continuous epochs), and then select the best model checkpoint to report the final result on the test set (note that each of our scores is an average over 5 runs with different random seeds). %Section \ref{sec:results} shows that using this relatively straightforward fine-tuning manner can lead to SOTA results. %Note that we might boost our downstream task performances even further by doing a more careful hyper-parameter tuning. + + + + + \begin{table*}[!ht] + \centering + \resizebox{15.5cm}{!}{ + %\setlength{\tabcolsep}{0.3em} + \begin{tabular}{l|l|l|l} + \hline + \multicolumn{2}{c|}{\textbf{NER} (word-level)} & \multicolumn{2}{c}{\textbf{NLI} (syllable- or word-level)} \\ + + \hline + Model & F\textsubscript{1} & Model & Acc. \\ + \hline + BiLSTM-CNN-CRF [$\blacklozenge$] & 88.3 & \_ & \_\\ + + VnCoreNLP-NER \citep{vu-etal-2018-vncorenlp} [$\blacklozenge$] & 88.6 & BiLSTM-max \citep{conneau-etal-2018-xnli} & 66.4 \\ + + + VNER \citep{8713740} & 89.6 & mBiLSTM \citep{ArtetxeS19} & 72.0 \\ + + BiLSTM-CNN-CRF + ETNLP [$\spadesuit$] & 91.1 & multilingual BERT \citep{devlin-etal-2019-bert} [$\blacksquare$] & 69.5 \\ + + VnCoreNLP-NER + ETNLP [$\spadesuit$] & 91.3 & XLM\textsubscript{MLM+TLM} \citep{NIPS2019_8928} & 76.6 \\ + + XLM-R\textsubscript{base} (our result) & 92.0 & XLM-R\textsubscript{base} \citep{conneau2019unsupervised} & {75.4} \\ + + XLM-R\textsubscript{large} (our result) & 92.8 & XLM-R\textsubscript{large} \citep{conneau2019unsupervised} & \underline{79.7} \\ + + \hline + PhoBERT\textsubscript{base}& \underline{93.6} & PhoBERT\textsubscript{base}& {78.5} \\ + + PhoBERT\textsubscript{large}& \textbf{94.7} & PhoBERT\textsubscript{large}& \textbf{80.0} \\ + \hline + + + \end{tabular} + } + \caption{Performance scores (in \%) on the NER and NLI test sets. + [$\blacklozenge$], [$\spadesuit$] and [$\blacksquare$] denote + results reported by \newcite{vu-etal-2018-vncorenlp}, \newcite{vu-xuan-etal-2019-etnlp} and \newcite{wu-dredze-2019-beto}, respectively. + %``mBiLSTM'' denotes a BiLSTM-based multilingual embedding model. + Note that there are higher Vietnamese NLI results reported for XLM-R when fine-tuning on the concatenation of all 15 training datasets from the XNLI corpus (i.e. TRANSLATE-TRAIN-ALL: 79.5\% for XLM-R\textsubscript{base} and 83.4\% XLM-R\textsubscript{large}). However, those results might not be comparable as we only use the monolingual Vietnamese training data for fine-tuning. } + \label{tab:nernli} + \end{table*} + +\section{Experimental results}\label{sec:results} + +\subsubsection*{Main results} + +Tables \ref{tab:posdep} and \ref{tab:nernli} compare PhoBERT scores with the previous highest reported results, using the same experimental setup. It is clear that our PhoBERT helps produce new SOTA performance results for all four downstream tasks. + +For \underline{POS tagging}, the neural model jointWPD for joint POS tagging and dependency parsing \citep{nguyen-2019-neural} and the feature-based model VnCoreNLP-POS \citep{nguyen-etal-2017-word} are the two previous SOTA models, obtaining accuracies at about 96.0\%. PhoBERT obtains 0.8\% absolute higher accuracy than these two models. + +For \underline{Dependency parsing}, the previous highest parsing scores LAS and UAS are obtained by the Biaffine parser at 75.0\% and 81.2\%, respectively. PhoBERT helps boost the Biaffine parser with about 4\% absolute improvement, achieving a LAS at 78.8\% and a UAS at 85.2\%. + + +For \underline{NER}, PhoBERT\textsubscript{large} produces 1.1 points higher F\textsubscript{1} than PhoBERT\textsubscript{base}. In addition, PhoBERT\textsubscript{base} obtains 2+ points higher than the previous SOTA feature- and neural network-based models VnCoreNLP-NER \citep{vu-etal-2018-vncorenlp} and BiLSTM-CNN-CRF \citep{ma-hovy-2016-end} which are trained with the set of 15K BERT-based ETNLP word embeddings \citep{vu-xuan-etal-2019-etnlp}. + + For \underline{NLI}, +PhoBERT outperforms the multilingual BERT \citep{devlin-etal-2019-bert} and the BERT-based cross-lingual model with a new translation language modeling objective XLM\textsubscript{MLM+TLM} \citep{NIPS2019_8928} by large margins. PhoBERT also performs better than the recent best pre-trained multilingual model XLM-R but using far fewer parameters than XLM-R: 135M (PhoBERT\textsubscript{base}) vs. 250M (XLM-R\textsubscript{base}); 370M (PhoBERT\textsubscript{large}) vs. 560M (XLM-R\textsubscript{large}). + + + + + + + +\subsubsection*{Discussion} + +We find that PhoBERT\textsubscript{large} achieves 0.9\% lower dependency parsing scores than PhoBERT\textsubscript{base}. One possible reason is that the last Transformer layer in the BERT architecture might not be the optimal one which encodes the richest information of syntactic structures \cite{hewitt-manning-2019-structural,jawahar-etal-2019-bert}. Future work will study which PhoBERT's Transformer layer contains richer syntactic information by evaluating the Vietnamese parsing performance from each layer. + +Using more pre-training data can significantly improve the quality of the pre-trained language models \cite{RoBERTa}. Thus it is not surprising that PhoBERT helps produce better performance than ETNLP on NER, and the multilingual BERT and XLM\textsubscript{MLM+TLM} on NLI (here, PhoBERT uses 20GB of Vietnamese texts while those models employ the 1GB Vietnamese Wikipedia corpus). + +Following the fine-tuning approach that we use for PhoBERT, we carefully fine-tune XLM-R for the remaining Vietnamese POS tagging, Dependency parsing and NER tasks (here, it is applied to the first sub-syllable token of the first syllable of each word).\footnote{For fine-tuning XLM-R, we use a grid search on the validation set to select the AdamW learning rate from \{5e-6, 1e-5, 2e-5, 4e-5\} and the batch size from \{16, 32\}.} +Tables \ref{tab:posdep} and \ref{tab:nernli} show that our PhoBERT also does better than XLM-R on these three word-level tasks. +It is worth noting that XLM-R uses a 2.5TB pre-training corpus which contains 137GB of Vietnamese texts (i.e. about 137\ /\ 20 $\approx$ 7 times bigger than our pre-training corpus). +Recall that PhoBERT performs Vietnamese word segmentation to segment syllable-level sentences into word tokens before applying BPE to segment the word-segmented sentences into subword units, while XLM-R directly applies BPE to the syllable-level Vietnamese pre-training sentences. + This reconfirms that the dedicated language-specific models still outperform the multilingual ones \citep{2019arXiv191103894M}.\footnote{Note that \newcite{2019arXiv191103894M} only compare their model CamemBERT with XLM-R on the French NLI task.} + + + + + + + + \section{Conclusion} + +In this paper, we have presented the first large-scale monolingual PhoBERT language models pre-trained for Vietnamese. We demonstrate the usefulness of PhoBERT by showing that PhoBERT performs better than the recent best multilingual model XLM-R and helps produce the SOTA performances for four downstream Vietnamese NLP tasks of POS tagging, Dependency parsing, NER and NLI. +By publicly releasing PhoBERT models, %\footnote{\url{https://github.com/VinAIResearch/PhoBERT}} +we hope that they can foster future research and applications in Vietnamese NLP. %Our PhoBERT and its usage are available at: \url{https://github.com/VinAIResearch/PhoBERT}. + +{%\footnotesize +\bibliographystyle{acl_natbib} +\bibliography{REFs} +} + + + + + +\end{document} diff --git a/references/2020.arxiv.nguyen/source/acl_natbib.bst b/references/2020.arxiv.nguyen/source/acl_natbib.bst new file mode 100644 index 0000000000000000000000000000000000000000..821195d8bbb77f882afb308a31e5f9da81720f6b --- /dev/null +++ b/references/2020.arxiv.nguyen/source/acl_natbib.bst @@ -0,0 +1,1975 @@ +%%% acl_natbib.bst +%%% Modification of BibTeX style file acl_natbib_nourl.bst +%%% ... by urlbst, version 0.7 (marked with "% urlbst") +%%% See +%%% Added webpage entry type, and url and lastchecked fields. +%%% Added eprint support. +%%% Added DOI support. +%%% Added PUBMED support. +%%% Added hyperref support. +%%% Original headers follow... + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% BibTeX style file acl_natbib_nourl.bst +% +% intended as input to urlbst script +% $ ./urlbst --hyperref --inlinelinks acl_natbib_nourl.bst > acl_natbib.bst +% +% adapted from compling.bst +% in order to mimic the style files for ACL conferences prior to 2017 +% by making the following three changes: +% - for @incollection, page numbers now follow volume title. +% - for @inproceedings, address now follows conference name. +% (address is intended as location of conference, +% not address of publisher.) +% - for papers with three authors, use et al. in citation +% Dan Gildea 2017/06/08 +% - fixed a bug with format.chapter - error given if chapter is empty +% with inbook. +% Shay Cohen 2018/02/16 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% BibTeX style file compling.bst +% +% Intended for the journal Computational Linguistics (ACL/MIT Press) +% Created by Ron Artstein on 2005/08/22 +% For use with for author-year citations. +% +% I created this file in order to allow submissions to the journal +% Computational Linguistics using the package for author-year +% citations, which offers a lot more flexibility than , CL's +% official citation package. This file adheres strictly to the official +% style guide available from the MIT Press: +% +% http://mitpress.mit.edu/journals/coli/compling_style.pdf +% +% This includes all the various quirks of the style guide, for example: +% - a chapter from a monograph (@inbook) has no page numbers. +% - an article from an edited volume (@incollection) has page numbers +% after the publisher and address. +% - an article from a proceedings volume (@inproceedings) has page +% numbers before the publisher and address. +% +% Where the style guide was inconsistent or not specific enough I +% looked at actual published articles and exercised my own judgment. +% I noticed two inconsistencies in the style guide: +% +% - The style guide gives one example of an article from an edited +% volume with the editor's name spelled out in full, and another +% with the editors' names abbreviated. I chose to accept the first +% one as correct, since the style guide generally shuns abbreviations, +% and editors' names are also spelled out in some recently published +% articles. +% +% - The style guide gives one example of a reference where the word +% "and" between two authors is preceded by a comma. This is most +% likely a typo, since in all other cases with just two authors or +% editors there is no comma before the word "and". +% +% One case where the style guide is not being specific is the placement +% of the edition number, for which no example is given. I chose to put +% it immediately after the title, which I (subjectively) find natural, +% and is also the place of the edition in a few recently published +% articles. +% +% This file correctly reproduces all of the examples in the official +% style guide, except for the two inconsistencies noted above. I even +% managed to get it to correctly format the proceedings example which +% has an organization, a publisher, and two addresses (the conference +% location and the publisher's address), though I cheated a bit by +% putting the conference location and month as part of the title field; +% I feel that in this case the conference location and month can be +% considered as part of the title, and that adding a location field +% is not justified. Note also that a location field is not standard, +% so entries made with this field would not port nicely to other styles. +% However, if authors feel that there's a need for a location field +% then tell me and I'll see what I can do. +% +% The file also produces to my satisfaction all the bibliographical +% entries in my recent (joint) submission to CL (this was the original +% motivation for creating the file). I also tested it by running it +% on a larger set of entries and eyeballing the results. There may of +% course still be errors, especially with combinations of fields that +% are not that common, or with cross-references (which I seldom use). +% If you find such errors please write to me. +% +% I hope people find this file useful. Please email me with comments +% and suggestions. +% +% Ron Artstein +% artstein [at] essex.ac.uk +% August 22, 2005. +% +% Some technical notes. +% +% This file is based on a file generated with the package +% by Patrick W. Daly (see selected options below), which was then +% manually customized to conform with certain CL requirements which +% cannot be met by . Departures from the generated file +% include: +% +% Function inbook: moved publisher and address to the end; moved +% edition after title; replaced function format.chapter.pages by +% new function format.chapter to output chapter without pages. +% +% Function inproceedings: moved publisher and address to the end; +% replaced function format.in.ed.booktitle by new function +% format.in.booktitle to output the proceedings title without +% the editor. +% +% Functions book, incollection, manual: moved edition after title. +% +% Function mastersthesis: formatted title as for articles (unlike +% phdthesis which is formatted as book) and added month. +% +% Function proceedings: added new.sentence between organization and +% publisher when both are present. +% +% Function format.lab.names: modified so that it gives all the +% authors' surnames for in-text citations for one, two and three +% authors and only uses "et. al" for works with four authors or more +% (thanks to Ken Shan for convincing me to go through the trouble of +% modifying this function rather than using unreliable hacks). +% +% Changes: +% +% 2006-10-27: Changed function reverse.pass so that the extra label is +% enclosed in parentheses when the year field ends in an uppercase or +% lowercase letter (change modeled after Uli Sauerland's modification +% of nals.bst). RA. +% +% +% The preamble of the generated file begins below: +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% +%% This is file `compling.bst', +%% generated with the docstrip utility. +%% +%% The original source files were: +%% +%% merlin.mbs (with options: `ay,nat,vonx,nm-revv1,jnrlst,keyxyr,blkyear,dt-beg,yr-per,note-yr,num-xser,pre-pub,xedn,nfss') +%% ---------------------------------------- +%% *** Intended for the journal Computational Linguistics *** +%% +%% Copyright 1994-2002 Patrick W Daly + % =============================================================== + % IMPORTANT NOTICE: + % This bibliographic style (bst) file has been generated from one or + % more master bibliographic style (mbs) files, listed above. + % + % This generated file can be redistributed and/or modified under the terms + % of the LaTeX Project Public License Distributed from CTAN + % archives in directory macros/latex/base/lppl.txt; either + % version 1 of the License, or any later version. + % =============================================================== + % Name and version information of the main mbs file: + % \ProvidesFile{merlin.mbs}[2002/10/21 4.05 (PWD, AO, DPC)] + % For use with BibTeX version 0.99a or later + %------------------------------------------------------------------- + % This bibliography style file is intended for texts in ENGLISH + % This is an author-year citation style bibliography. As such, it is + % non-standard LaTeX, and requires a special package file to function properly. + % Such a package is natbib.sty by Patrick W. Daly + % The form of the \bibitem entries is + % \bibitem[Jones et al.(1990)]{key}... + % \bibitem[Jones et al.(1990)Jones, Baker, and Smith]{key}... + % The essential feature is that the label (the part in brackets) consists + % of the author names, as they should appear in the citation, with the year + % in parentheses following. There must be no space before the opening + % parenthesis! + % With natbib v5.3, a full list of authors may also follow the year. + % In natbib.sty, it is possible to define the type of enclosures that is + % really wanted (brackets or parentheses), but in either case, there must + % be parentheses in the label. + % The \cite command functions as follows: + % \citet{key} ==>> Jones et al. (1990) + % \citet*{key} ==>> Jones, Baker, and Smith (1990) + % \citep{key} ==>> (Jones et al., 1990) + % \citep*{key} ==>> (Jones, Baker, and Smith, 1990) + % \citep[chap. 2]{key} ==>> (Jones et al., 1990, chap. 2) + % \citep[e.g.][]{key} ==>> (e.g. Jones et al., 1990) + % \citep[e.g.][p. 32]{key} ==>> (e.g. Jones et al., p. 32) + % \citeauthor{key} ==>> Jones et al. + % \citeauthor*{key} ==>> Jones, Baker, and Smith + % \citeyear{key} ==>> 1990 + %--------------------------------------------------------------------- + +ENTRY + { address + author + booktitle + chapter + edition + editor + howpublished + institution + journal + key + month + note + number + organization + pages + publisher + school + series + title + type + volume + year + eprint % urlbst + doi % urlbst + pubmed % urlbst + url % urlbst + lastchecked % urlbst + } + {} + { label extra.label sort.label short.list } +INTEGERS { output.state before.all mid.sentence after.sentence after.block } +% urlbst... +% urlbst constants and state variables +STRINGS { urlintro + eprinturl eprintprefix doiprefix doiurl pubmedprefix pubmedurl + citedstring onlinestring linktextstring + openinlinelink closeinlinelink } +INTEGERS { hrefform inlinelinks makeinlinelink + addeprints adddoiresolver addpubmedresolver } +FUNCTION {init.urlbst.variables} +{ + % The following constants may be adjusted by hand, if desired + + % The first set allow you to enable or disable certain functionality. + #1 'addeprints := % 0=no eprints; 1=include eprints + #1 'adddoiresolver := % 0=no DOI resolver; 1=include it + #1 'addpubmedresolver := % 0=no PUBMED resolver; 1=include it + #2 'hrefform := % 0=no crossrefs; 1=hypertex xrefs; 2=hyperref refs + #1 'inlinelinks := % 0=URLs explicit; 1=URLs attached to titles + + % String constants, which you _might_ want to tweak. + "URL: " 'urlintro := % prefix before URL; typically "Available from:" or "URL": + "online" 'onlinestring := % indication that resource is online; typically "online" + "cited " 'citedstring := % indicator of citation date; typically "cited " + "[link]" 'linktextstring := % dummy link text; typically "[link]" + "http://arxiv.org/abs/" 'eprinturl := % prefix to make URL from eprint ref + "arXiv:" 'eprintprefix := % text prefix printed before eprint ref; typically "arXiv:" + "https://doi.org/" 'doiurl := % prefix to make URL from DOI + "doi:" 'doiprefix := % text prefix printed before DOI ref; typically "doi:" + "http://www.ncbi.nlm.nih.gov/pubmed/" 'pubmedurl := % prefix to make URL from PUBMED + "PMID:" 'pubmedprefix := % text prefix printed before PUBMED ref; typically "PMID:" + + % The following are internal state variables, not configuration constants, + % so they shouldn't be fiddled with. + #0 'makeinlinelink := % state variable managed by possibly.setup.inlinelink + "" 'openinlinelink := % ditto + "" 'closeinlinelink := % ditto +} +INTEGERS { + bracket.state + outside.brackets + open.brackets + within.brackets + close.brackets +} +% ...urlbst to here +FUNCTION {init.state.consts} +{ #0 'outside.brackets := % urlbst... + #1 'open.brackets := + #2 'within.brackets := + #3 'close.brackets := % ...urlbst to here + + #0 'before.all := + #1 'mid.sentence := + #2 'after.sentence := + #3 'after.block := +} +STRINGS { s t} +% urlbst +FUNCTION {output.nonnull.original} +{ 's := + output.state mid.sentence = + { ", " * write$ } + { output.state after.block = + { add.period$ write$ + newline$ + "\newblock " write$ + } + { output.state before.all = + 'write$ + { add.period$ " " * write$ } + if$ + } + if$ + mid.sentence 'output.state := + } + if$ + s +} + +% urlbst... +% The following three functions are for handling inlinelink. They wrap +% a block of text which is potentially output with write$ by multiple +% other functions, so we don't know the content a priori. +% They communicate between each other using the variables makeinlinelink +% (which is true if a link should be made), and closeinlinelink (which holds +% the string which should close any current link. They can be called +% at any time, but start.inlinelink will be a no-op unless something has +% previously set makeinlinelink true, and the two ...end.inlinelink functions +% will only do their stuff if start.inlinelink has previously set +% closeinlinelink to be non-empty. +% (thanks to 'ijvm' for suggested code here) +FUNCTION {uand} +{ 'skip$ { pop$ #0 } if$ } % 'and' (which isn't defined at this point in the file) +FUNCTION {possibly.setup.inlinelink} +{ makeinlinelink hrefform #0 > uand + { doi empty$ adddoiresolver uand + { pubmed empty$ addpubmedresolver uand + { eprint empty$ addeprints uand + { url empty$ + { "" } + { url } + if$ } + { eprinturl eprint * } + if$ } + { pubmedurl pubmed * } + if$ } + { doiurl doi * } + if$ + % an appropriately-formatted URL is now on the stack + hrefform #1 = % hypertex + { "\special {html: }{" * 'openinlinelink := + "\special {html:}" 'closeinlinelink := } + { "\href {" swap$ * "} {" * 'openinlinelink := % hrefform=#2 -- hyperref + % the space between "} {" matters: a URL of just the right length can cause "\% newline em" + "}" 'closeinlinelink := } + if$ + #0 'makeinlinelink := + } + 'skip$ + if$ % makeinlinelink +} +FUNCTION {add.inlinelink} +{ openinlinelink empty$ + 'skip$ + { openinlinelink swap$ * closeinlinelink * + "" 'openinlinelink := + } + if$ +} +FUNCTION {output.nonnull} +{ % Save the thing we've been asked to output + 's := + % If the bracket-state is close.brackets, then add a close-bracket to + % what is currently at the top of the stack, and set bracket.state + % to outside.brackets + bracket.state close.brackets = + { "]" * + outside.brackets 'bracket.state := + } + 'skip$ + if$ + bracket.state outside.brackets = + { % We're outside all brackets -- this is the normal situation. + % Write out what's currently at the top of the stack, using the + % original output.nonnull function. + s + add.inlinelink + output.nonnull.original % invoke the original output.nonnull + } + { % Still in brackets. Add open-bracket or (continuation) comma, add the + % new text (in s) to the top of the stack, and move to the close-brackets + % state, ready for next time (unless inbrackets resets it). If we come + % into this branch, then output.state is carefully undisturbed. + bracket.state open.brackets = + { " [" * } + { ", " * } % bracket.state will be within.brackets + if$ + s * + close.brackets 'bracket.state := + } + if$ +} + +% Call this function just before adding something which should be presented in +% brackets. bracket.state is handled specially within output.nonnull. +FUNCTION {inbrackets} +{ bracket.state close.brackets = + { within.brackets 'bracket.state := } % reset the state: not open nor closed + { open.brackets 'bracket.state := } + if$ +} + +FUNCTION {format.lastchecked} +{ lastchecked empty$ + { "" } + { inbrackets citedstring lastchecked * } + if$ +} +% ...urlbst to here +FUNCTION {output} +{ duplicate$ empty$ + 'pop$ + 'output.nonnull + if$ +} +FUNCTION {output.check} +{ 't := + duplicate$ empty$ + { pop$ "empty " t * " in " * cite$ * warning$ } + 'output.nonnull + if$ +} +FUNCTION {fin.entry.original} % urlbst (renamed from fin.entry, so it can be wrapped below) +{ add.period$ + write$ + newline$ +} + +FUNCTION {new.block} +{ output.state before.all = + 'skip$ + { after.block 'output.state := } + if$ +} +FUNCTION {new.sentence} +{ output.state after.block = + 'skip$ + { output.state before.all = + 'skip$ + { after.sentence 'output.state := } + if$ + } + if$ +} +FUNCTION {add.blank} +{ " " * before.all 'output.state := +} + +FUNCTION {date.block} +{ + new.block +} + +FUNCTION {not} +{ { #0 } + { #1 } + if$ +} +FUNCTION {and} +{ 'skip$ + { pop$ #0 } + if$ +} +FUNCTION {or} +{ { pop$ #1 } + 'skip$ + if$ +} +FUNCTION {new.block.checkb} +{ empty$ + swap$ empty$ + and + 'skip$ + 'new.block + if$ +} +FUNCTION {field.or.null} +{ duplicate$ empty$ + { pop$ "" } + 'skip$ + if$ +} +FUNCTION {emphasize} +{ duplicate$ empty$ + { pop$ "" } + { "\emph{" swap$ * "}" * } + if$ +} +FUNCTION {tie.or.space.prefix} +{ duplicate$ text.length$ #3 < + { "~" } + { " " } + if$ + swap$ +} + +FUNCTION {capitalize} +{ "u" change.case$ "t" change.case$ } + +FUNCTION {space.word} +{ " " swap$ * " " * } + % Here are the language-specific definitions for explicit words. + % Each function has a name bbl.xxx where xxx is the English word. + % The language selected here is ENGLISH +FUNCTION {bbl.and} +{ "and"} + +FUNCTION {bbl.etal} +{ "et~al." } + +FUNCTION {bbl.editors} +{ "editors" } + +FUNCTION {bbl.editor} +{ "editor" } + +FUNCTION {bbl.edby} +{ "edited by" } + +FUNCTION {bbl.edition} +{ "edition" } + +FUNCTION {bbl.volume} +{ "volume" } + +FUNCTION {bbl.of} +{ "of" } + +FUNCTION {bbl.number} +{ "number" } + +FUNCTION {bbl.nr} +{ "no." } + +FUNCTION {bbl.in} +{ "in" } + +FUNCTION {bbl.pages} +{ "pages" } + +FUNCTION {bbl.page} +{ "page" } + +FUNCTION {bbl.chapter} +{ "chapter" } + +FUNCTION {bbl.techrep} +{ "Technical Report" } + +FUNCTION {bbl.mthesis} +{ "Master's thesis" } + +FUNCTION {bbl.phdthesis} +{ "Ph.D. thesis" } + +MACRO {jan} {"January"} + +MACRO {feb} {"February"} + +MACRO {mar} {"March"} + +MACRO {apr} {"April"} + +MACRO {may} {"May"} + +MACRO {jun} {"June"} + +MACRO {jul} {"July"} + +MACRO {aug} {"August"} + +MACRO {sep} {"September"} + +MACRO {oct} {"October"} + +MACRO {nov} {"November"} + +MACRO {dec} {"December"} + +MACRO {acmcs} {"ACM Computing Surveys"} + +MACRO {acta} {"Acta Informatica"} + +MACRO {cacm} {"Communications of the ACM"} + +MACRO {ibmjrd} {"IBM Journal of Research and Development"} + +MACRO {ibmsj} {"IBM Systems Journal"} + +MACRO {ieeese} {"IEEE Transactions on Software Engineering"} + +MACRO {ieeetc} {"IEEE Transactions on Computers"} + +MACRO {ieeetcad} + {"IEEE Transactions on Computer-Aided Design of Integrated Circuits"} + +MACRO {ipl} {"Information Processing Letters"} + +MACRO {jacm} {"Journal of the ACM"} + +MACRO {jcss} {"Journal of Computer and System Sciences"} + +MACRO {scp} {"Science of Computer Programming"} + +MACRO {sicomp} {"SIAM Journal on Computing"} + +MACRO {tocs} {"ACM Transactions on Computer Systems"} + +MACRO {tods} {"ACM Transactions on Database Systems"} + +MACRO {tog} {"ACM Transactions on Graphics"} + +MACRO {toms} {"ACM Transactions on Mathematical Software"} + +MACRO {toois} {"ACM Transactions on Office Information Systems"} + +MACRO {toplas} {"ACM Transactions on Programming Languages and Systems"} + +MACRO {tcs} {"Theoretical Computer Science"} +FUNCTION {bibinfo.check} +{ swap$ + duplicate$ missing$ + { + pop$ pop$ + "" + } + { duplicate$ empty$ + { + swap$ pop$ + } + { swap$ + pop$ + } + if$ + } + if$ +} +FUNCTION {bibinfo.warn} +{ swap$ + duplicate$ missing$ + { + swap$ "missing " swap$ * " in " * cite$ * warning$ pop$ + "" + } + { duplicate$ empty$ + { + swap$ "empty " swap$ * " in " * cite$ * warning$ + } + { swap$ + pop$ + } + if$ + } + if$ +} +STRINGS { bibinfo} +INTEGERS { nameptr namesleft numnames } + +FUNCTION {format.names} +{ 'bibinfo := + duplicate$ empty$ 'skip$ { + 's := + "" 't := + #1 'nameptr := + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + duplicate$ #1 > + { "{ff~}{vv~}{ll}{, jj}" } + { "{ff~}{vv~}{ll}{, jj}" } % first name first for first author +% { "{vv~}{ll}{, ff}{, jj}" } % last name first for first author + if$ + format.name$ + bibinfo bibinfo.check + 't := + nameptr #1 > + { + namesleft #1 > + { ", " * t * } + { + numnames #2 > + { "," * } + 'skip$ + if$ + s nameptr "{ll}" format.name$ duplicate$ "others" = + { 't := } + { pop$ } + if$ + t "others" = + { + " " * bbl.etal * + } + { + bbl.and + space.word * t * + } + if$ + } + if$ + } + 't + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ + } if$ +} +FUNCTION {format.names.ed} +{ + 'bibinfo := + duplicate$ empty$ 'skip$ { + 's := + "" 't := + #1 'nameptr := + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + "{ff~}{vv~}{ll}{, jj}" + format.name$ + bibinfo bibinfo.check + 't := + nameptr #1 > + { + namesleft #1 > + { ", " * t * } + { + numnames #2 > + { "," * } + 'skip$ + if$ + s nameptr "{ll}" format.name$ duplicate$ "others" = + { 't := } + { pop$ } + if$ + t "others" = + { + + " " * bbl.etal * + } + { + bbl.and + space.word * t * + } + if$ + } + if$ + } + 't + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ + } if$ +} +FUNCTION {format.key} +{ empty$ + { key field.or.null } + { "" } + if$ +} + +FUNCTION {format.authors} +{ author "author" format.names +} +FUNCTION {get.bbl.editor} +{ editor num.names$ #1 > 'bbl.editors 'bbl.editor if$ } + +FUNCTION {format.editors} +{ editor "editor" format.names duplicate$ empty$ 'skip$ + { + "," * + " " * + get.bbl.editor + * + } + if$ +} +FUNCTION {format.note} +{ + note empty$ + { "" } + { note #1 #1 substring$ + duplicate$ "{" = + 'skip$ + { output.state mid.sentence = + { "l" } + { "u" } + if$ + change.case$ + } + if$ + note #2 global.max$ substring$ * "note" bibinfo.check + } + if$ +} + +FUNCTION {format.title} +{ title + duplicate$ empty$ 'skip$ + { "t" change.case$ } + if$ + "title" bibinfo.check +} +FUNCTION {format.full.names} +{'s := + "" 't := + #1 'nameptr := + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + "{vv~}{ll}" format.name$ + 't := + nameptr #1 > + { + namesleft #1 > + { ", " * t * } + { + s nameptr "{ll}" format.name$ duplicate$ "others" = + { 't := } + { pop$ } + if$ + t "others" = + { + " " * bbl.etal * + } + { + numnames #2 > + { "," * } + 'skip$ + if$ + bbl.and + space.word * t * + } + if$ + } + if$ + } + 't + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ +} + +FUNCTION {author.editor.key.full} +{ author empty$ + { editor empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { editor format.full.names } + if$ + } + { author format.full.names } + if$ +} + +FUNCTION {author.key.full} +{ author empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { author format.full.names } + if$ +} + +FUNCTION {editor.key.full} +{ editor empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { editor format.full.names } + if$ +} + +FUNCTION {make.full.names} +{ type$ "book" = + type$ "inbook" = + or + 'author.editor.key.full + { type$ "proceedings" = + 'editor.key.full + 'author.key.full + if$ + } + if$ +} + +FUNCTION {output.bibitem.original} % urlbst (renamed from output.bibitem, so it can be wrapped below) +{ newline$ + "\bibitem[{" write$ + label write$ + ")" make.full.names duplicate$ short.list = + { pop$ } + { * } + if$ + "}]{" * write$ + cite$ write$ + "}" write$ + newline$ + "" + before.all 'output.state := +} + +FUNCTION {n.dashify} +{ + 't := + "" + { t empty$ not } + { t #1 #1 substring$ "-" = + { t #1 #2 substring$ "--" = not + { "--" * + t #2 global.max$ substring$ 't := + } + { { t #1 #1 substring$ "-" = } + { "-" * + t #2 global.max$ substring$ 't := + } + while$ + } + if$ + } + { t #1 #1 substring$ * + t #2 global.max$ substring$ 't := + } + if$ + } + while$ +} + +FUNCTION {word.in} +{ bbl.in capitalize + " " * } + +FUNCTION {format.date} +{ year "year" bibinfo.check duplicate$ empty$ + { + } + 'skip$ + if$ + extra.label * + before.all 'output.state := + after.sentence 'output.state := +} +FUNCTION {format.btitle} +{ title "title" bibinfo.check + duplicate$ empty$ 'skip$ + { + emphasize + } + if$ +} +FUNCTION {either.or.check} +{ empty$ + 'pop$ + { "can't use both " swap$ * " fields in " * cite$ * warning$ } + if$ +} +FUNCTION {format.bvolume} +{ volume empty$ + { "" } + { bbl.volume volume tie.or.space.prefix + "volume" bibinfo.check * * + series "series" bibinfo.check + duplicate$ empty$ 'pop$ + { swap$ bbl.of space.word * swap$ + emphasize * } + if$ + "volume and number" number either.or.check + } + if$ +} +FUNCTION {format.number.series} +{ volume empty$ + { number empty$ + { series field.or.null } + { series empty$ + { number "number" bibinfo.check } + { output.state mid.sentence = + { bbl.number } + { bbl.number capitalize } + if$ + number tie.or.space.prefix "number" bibinfo.check * * + bbl.in space.word * + series "series" bibinfo.check * + } + if$ + } + if$ + } + { "" } + if$ +} + +FUNCTION {format.edition} +{ edition duplicate$ empty$ 'skip$ + { + output.state mid.sentence = + { "l" } + { "t" } + if$ change.case$ + "edition" bibinfo.check + " " * bbl.edition * + } + if$ +} +INTEGERS { multiresult } +FUNCTION {multi.page.check} +{ 't := + #0 'multiresult := + { multiresult not + t empty$ not + and + } + { t #1 #1 substring$ + duplicate$ "-" = + swap$ duplicate$ "," = + swap$ "+" = + or or + { #1 'multiresult := } + { t #2 global.max$ substring$ 't := } + if$ + } + while$ + multiresult +} +FUNCTION {format.pages} +{ pages duplicate$ empty$ 'skip$ + { duplicate$ multi.page.check + { + bbl.pages swap$ + n.dashify + } + { + bbl.page swap$ + } + if$ + tie.or.space.prefix + "pages" bibinfo.check + * * + } + if$ +} +FUNCTION {format.journal.pages} +{ pages duplicate$ empty$ 'pop$ + { swap$ duplicate$ empty$ + { pop$ pop$ format.pages } + { + ":" * + swap$ + n.dashify + "pages" bibinfo.check + * + } + if$ + } + if$ +} +FUNCTION {format.vol.num.pages} +{ volume field.or.null + duplicate$ empty$ 'skip$ + { + "volume" bibinfo.check + } + if$ + number "number" bibinfo.check duplicate$ empty$ 'skip$ + { + swap$ duplicate$ empty$ + { "there's a number but no volume in " cite$ * warning$ } + 'skip$ + if$ + swap$ + "(" swap$ * ")" * + } + if$ * + format.journal.pages +} + +FUNCTION {format.chapter} +{ chapter empty$ + 'format.pages + { type empty$ + { bbl.chapter } + { type "l" change.case$ + "type" bibinfo.check + } + if$ + chapter tie.or.space.prefix + "chapter" bibinfo.check + * * + } + if$ +} + +FUNCTION {format.chapter.pages} +{ chapter empty$ + 'format.pages + { type empty$ + { bbl.chapter } + { type "l" change.case$ + "type" bibinfo.check + } + if$ + chapter tie.or.space.prefix + "chapter" bibinfo.check + * * + pages empty$ + 'skip$ + { ", " * format.pages * } + if$ + } + if$ +} + +FUNCTION {format.booktitle} +{ + booktitle "booktitle" bibinfo.check + emphasize +} +FUNCTION {format.in.booktitle} +{ format.booktitle duplicate$ empty$ 'skip$ + { + word.in swap$ * + } + if$ +} +FUNCTION {format.in.ed.booktitle} +{ format.booktitle duplicate$ empty$ 'skip$ + { + editor "editor" format.names.ed duplicate$ empty$ 'pop$ + { + "," * + " " * + get.bbl.editor + ", " * + * swap$ + * } + if$ + word.in swap$ * + } + if$ +} +FUNCTION {format.thesis.type} +{ type duplicate$ empty$ + 'pop$ + { swap$ pop$ + "t" change.case$ "type" bibinfo.check + } + if$ +} +FUNCTION {format.tr.number} +{ number "number" bibinfo.check + type duplicate$ empty$ + { pop$ bbl.techrep } + 'skip$ + if$ + "type" bibinfo.check + swap$ duplicate$ empty$ + { pop$ "t" change.case$ } + { tie.or.space.prefix * * } + if$ +} +FUNCTION {format.article.crossref} +{ + word.in + " \cite{" * crossref * "}" * +} +FUNCTION {format.book.crossref} +{ volume duplicate$ empty$ + { "empty volume in " cite$ * "'s crossref of " * crossref * warning$ + pop$ word.in + } + { bbl.volume + capitalize + swap$ tie.or.space.prefix "volume" bibinfo.check * * bbl.of space.word * + } + if$ + " \cite{" * crossref * "}" * +} +FUNCTION {format.incoll.inproc.crossref} +{ + word.in + " \cite{" * crossref * "}" * +} +FUNCTION {format.org.or.pub} +{ 't := + "" + address empty$ t empty$ and + 'skip$ + { + t empty$ + { address "address" bibinfo.check * + } + { t * + address empty$ + 'skip$ + { ", " * address "address" bibinfo.check * } + if$ + } + if$ + } + if$ +} +FUNCTION {format.publisher.address} +{ publisher "publisher" bibinfo.warn format.org.or.pub +} + +FUNCTION {format.organization.address} +{ organization "organization" bibinfo.check format.org.or.pub +} + +% urlbst... +% Functions for making hypertext links. +% In all cases, the stack has (link-text href-url) +% +% make 'null' specials +FUNCTION {make.href.null} +{ + pop$ +} +% make hypertex specials +FUNCTION {make.href.hypertex} +{ + "\special {html: }" * swap$ * + "\special {html:}" * +} +% make hyperref specials +FUNCTION {make.href.hyperref} +{ + "\href {" swap$ * "} {\path{" * swap$ * "}}" * +} +FUNCTION {make.href} +{ hrefform #2 = + 'make.href.hyperref % hrefform = 2 + { hrefform #1 = + 'make.href.hypertex % hrefform = 1 + 'make.href.null % hrefform = 0 (or anything else) + if$ + } + if$ +} + +% If inlinelinks is true, then format.url should be a no-op, since it's +% (a) redundant, and (b) could end up as a link-within-a-link. +FUNCTION {format.url} +{ inlinelinks #1 = url empty$ or + { "" } + { hrefform #1 = + { % special case -- add HyperTeX specials + urlintro "\url{" url * "}" * url make.href.hypertex * } + { urlintro "\url{" * url * "}" * } + if$ + } + if$ +} + +FUNCTION {format.eprint} +{ eprint empty$ + { "" } + { eprintprefix eprint * eprinturl eprint * make.href } + if$ +} + +FUNCTION {format.doi} +{ doi empty$ + { "" } + { doiprefix doi * doiurl doi * make.href } + if$ +} + +FUNCTION {format.pubmed} +{ pubmed empty$ + { "" } + { pubmedprefix pubmed * pubmedurl pubmed * make.href } + if$ +} + +% Output a URL. We can't use the more normal idiom (something like +% `format.url output'), because the `inbrackets' within +% format.lastchecked applies to everything between calls to `output', +% so that `format.url format.lastchecked * output' ends up with both +% the URL and the lastchecked in brackets. +FUNCTION {output.url} +{ url empty$ + 'skip$ + { new.block + format.url output + format.lastchecked output + } + if$ +} + +FUNCTION {output.web.refs} +{ + new.block + inlinelinks + 'skip$ % links were inline -- don't repeat them + { + output.url + addeprints eprint empty$ not and + { format.eprint output.nonnull } + 'skip$ + if$ + adddoiresolver doi empty$ not and + { format.doi output.nonnull } + 'skip$ + if$ + addpubmedresolver pubmed empty$ not and + { format.pubmed output.nonnull } + 'skip$ + if$ + } + if$ +} + +% Wrapper for output.bibitem.original. +% If the URL field is not empty, set makeinlinelink to be true, +% so that an inline link will be started at the next opportunity +FUNCTION {output.bibitem} +{ outside.brackets 'bracket.state := + output.bibitem.original + inlinelinks url empty$ not doi empty$ not or pubmed empty$ not or eprint empty$ not or and + { #1 'makeinlinelink := } + { #0 'makeinlinelink := } + if$ +} + +% Wrapper for fin.entry.original +FUNCTION {fin.entry} +{ output.web.refs % urlbst + makeinlinelink % ooops, it appears we didn't have a title for inlinelink + { possibly.setup.inlinelink % add some artificial link text here, as a fallback + linktextstring output.nonnull } + 'skip$ + if$ + bracket.state close.brackets = % urlbst + { "]" * } + 'skip$ + if$ + fin.entry.original +} + +% Webpage entry type. +% Title and url fields required; +% author, note, year, month, and lastchecked fields optional +% See references +% ISO 690-2 http://www.nlc-bnc.ca/iso/tc46sc9/standard/690-2e.htm +% http://www.classroom.net/classroom/CitingNetResources.html +% http://neal.ctstateu.edu/history/cite.html +% http://www.cas.usf.edu/english/walker/mla.html +% for citation formats for web pages. +FUNCTION {webpage} +{ output.bibitem + author empty$ + { editor empty$ + 'skip$ % author and editor both optional + { format.editors output.nonnull } + if$ + } + { editor empty$ + { format.authors output.nonnull } + { "can't use both author and editor fields in " cite$ * warning$ } + if$ + } + if$ + new.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ + format.title "title" output.check + inbrackets onlinestring output + new.block + year empty$ + 'skip$ + { format.date "year" output.check } + if$ + % We don't need to output the URL details ('lastchecked' and 'url'), + % because fin.entry does that for us, using output.web.refs. The only + % reason we would want to put them here is if we were to decide that + % they should go in front of the rather miscellaneous information in 'note'. + new.block + note output + fin.entry +} +% ...urlbst to here + + +FUNCTION {article} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title "title" output.check + new.block + crossref missing$ + { + journal + "journal" bibinfo.check + emphasize + "journal" output.check + possibly.setup.inlinelink format.vol.num.pages output% urlbst + } + { format.article.crossref output.nonnull + format.pages output + } + if$ + new.block + format.note output + fin.entry +} +FUNCTION {book} +{ output.bibitem + author empty$ + { format.editors "author and editor" output.check + editor format.key output + } + { format.authors output.nonnull + crossref missing$ + { "author and editor" editor either.or.check } + 'skip$ + if$ + } + if$ + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.btitle "title" output.check + format.edition output + crossref missing$ + { format.bvolume output + new.block + format.number.series output + new.sentence + format.publisher.address output + } + { + new.block + format.book.crossref output.nonnull + } + if$ + new.block + format.note output + fin.entry +} +FUNCTION {booklet} +{ output.bibitem + format.authors output + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title "title" output.check + new.block + howpublished "howpublished" bibinfo.check output + address "address" bibinfo.check output + new.block + format.note output + fin.entry +} + +FUNCTION {inbook} +{ output.bibitem + author empty$ + { format.editors "author and editor" output.check + editor format.key output + } + { format.authors output.nonnull + crossref missing$ + { "author and editor" editor either.or.check } + 'skip$ + if$ + } + if$ + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.btitle "title" output.check + format.edition output + crossref missing$ + { + format.bvolume output + format.number.series output + format.chapter "chapter" output.check + new.sentence + format.publisher.address output + new.block + } + { + format.chapter "chapter" output.check + new.block + format.book.crossref output.nonnull + } + if$ + new.block + format.note output + fin.entry +} + +FUNCTION {incollection} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title "title" output.check + new.block + crossref missing$ + { format.in.ed.booktitle "booktitle" output.check + format.edition output + format.bvolume output + format.number.series output + format.chapter.pages output + new.sentence + format.publisher.address output + } + { format.incoll.inproc.crossref output.nonnull + format.chapter.pages output + } + if$ + new.block + format.note output + fin.entry +} +FUNCTION {inproceedings} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title "title" output.check + new.block + crossref missing$ + { format.in.booktitle "booktitle" output.check + format.bvolume output + format.number.series output + format.pages output + address "address" bibinfo.check output + new.sentence + organization "organization" bibinfo.check output + publisher "publisher" bibinfo.check output + } + { format.incoll.inproc.crossref output.nonnull + format.pages output + } + if$ + new.block + format.note output + fin.entry +} +FUNCTION {conference} { inproceedings } +FUNCTION {manual} +{ output.bibitem + format.authors output + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.btitle "title" output.check + format.edition output + organization address new.block.checkb + organization "organization" bibinfo.check output + address "address" bibinfo.check output + new.block + format.note output + fin.entry +} + +FUNCTION {mastersthesis} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title + "title" output.check + new.block + bbl.mthesis format.thesis.type output.nonnull + school "school" bibinfo.warn output + address "address" bibinfo.check output + month "month" bibinfo.check output + new.block + format.note output + fin.entry +} + +FUNCTION {misc} +{ output.bibitem + format.authors output + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title output + new.block + howpublished "howpublished" bibinfo.check output + new.block + format.note output + fin.entry +} +FUNCTION {phdthesis} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.btitle + "title" output.check + new.block + bbl.phdthesis format.thesis.type output.nonnull + school "school" bibinfo.warn output + address "address" bibinfo.check output + new.block + format.note output + fin.entry +} + +FUNCTION {proceedings} +{ output.bibitem + format.editors output + editor format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.btitle "title" output.check + format.bvolume output + format.number.series output + new.sentence + publisher empty$ + { format.organization.address output } + { organization "organization" bibinfo.check output + new.sentence + format.publisher.address output + } + if$ + new.block + format.note output + fin.entry +} + +FUNCTION {techreport} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title + "title" output.check + new.block + format.tr.number output.nonnull + institution "institution" bibinfo.warn output + address "address" bibinfo.check output + new.block + format.note output + fin.entry +} + +FUNCTION {unpublished} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title "title" output.check + new.block + format.note "note" output.check + fin.entry +} + +FUNCTION {default.type} { misc } +READ +FUNCTION {sortify} +{ purify$ + "l" change.case$ +} +INTEGERS { len } +FUNCTION {chop.word} +{ 's := + 'len := + s #1 len substring$ = + { s len #1 + global.max$ substring$ } + 's + if$ +} +FUNCTION {format.lab.names} +{ 's := + "" 't := + s #1 "{vv~}{ll}" format.name$ + s num.names$ duplicate$ + #2 > + { pop$ + " " * bbl.etal * + } + { #2 < + 'skip$ + { s #2 "{ff }{vv }{ll}{ jj}" format.name$ "others" = + { + " " * bbl.etal * + } + { bbl.and space.word * s #2 "{vv~}{ll}" format.name$ + * } + if$ + } + if$ + } + if$ +} + +FUNCTION {author.key.label} +{ author empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { author format.lab.names } + if$ +} + +FUNCTION {author.editor.key.label} +{ author empty$ + { editor empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { editor format.lab.names } + if$ + } + { author format.lab.names } + if$ +} + +FUNCTION {editor.key.label} +{ editor empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { editor format.lab.names } + if$ +} + +FUNCTION {calc.short.authors} +{ type$ "book" = + type$ "inbook" = + or + 'author.editor.key.label + { type$ "proceedings" = + 'editor.key.label + 'author.key.label + if$ + } + if$ + 'short.list := +} + +FUNCTION {calc.label} +{ calc.short.authors + short.list + "(" + * + year duplicate$ empty$ + short.list key field.or.null = or + { pop$ "" } + 'skip$ + if$ + * + 'label := +} + +FUNCTION {sort.format.names} +{ 's := + #1 'nameptr := + "" + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + "{ll{ }}{ ff{ }}{ jj{ }}" + format.name$ 't := + nameptr #1 > + { + " " * + namesleft #1 = t "others" = and + { "zzzzz" * } + { t sortify * } + if$ + } + { t sortify * } + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ +} + +FUNCTION {sort.format.title} +{ 't := + "A " #2 + "An " #3 + "The " #4 t chop.word + chop.word + chop.word + sortify + #1 global.max$ substring$ +} +FUNCTION {author.sort} +{ author empty$ + { key empty$ + { "to sort, need author or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { author sort.format.names } + if$ +} +FUNCTION {author.editor.sort} +{ author empty$ + { editor empty$ + { key empty$ + { "to sort, need author, editor, or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { editor sort.format.names } + if$ + } + { author sort.format.names } + if$ +} +FUNCTION {editor.sort} +{ editor empty$ + { key empty$ + { "to sort, need editor or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { editor sort.format.names } + if$ +} +FUNCTION {presort} +{ calc.label + label sortify + " " + * + type$ "book" = + type$ "inbook" = + or + 'author.editor.sort + { type$ "proceedings" = + 'editor.sort + 'author.sort + if$ + } + if$ + #1 entry.max$ substring$ + 'sort.label := + sort.label + * + " " + * + title field.or.null + sort.format.title + * + #1 entry.max$ substring$ + 'sort.key$ := +} + +ITERATE {presort} +SORT +STRINGS { last.label next.extra } +INTEGERS { last.extra.num number.label } +FUNCTION {initialize.extra.label.stuff} +{ #0 int.to.chr$ 'last.label := + "" 'next.extra := + #0 'last.extra.num := + #0 'number.label := +} +FUNCTION {forward.pass} +{ last.label label = + { last.extra.num #1 + 'last.extra.num := + last.extra.num int.to.chr$ 'extra.label := + } + { "a" chr.to.int$ 'last.extra.num := + "" 'extra.label := + label 'last.label := + } + if$ + number.label #1 + 'number.label := +} +FUNCTION {reverse.pass} +{ next.extra "b" = + { "a" 'extra.label := } + 'skip$ + if$ + extra.label 'next.extra := + extra.label + duplicate$ empty$ + 'skip$ + { year field.or.null #-1 #1 substring$ chr.to.int$ #65 < + { "{\natexlab{" swap$ * "}}" * } + { "{(\natexlab{" swap$ * "})}" * } + if$ } + if$ + 'extra.label := + label extra.label * 'label := +} +EXECUTE {initialize.extra.label.stuff} +ITERATE {forward.pass} +REVERSE {reverse.pass} +FUNCTION {bib.sort.order} +{ sort.label + " " + * + year field.or.null sortify + * + " " + * + title field.or.null + sort.format.title + * + #1 entry.max$ substring$ + 'sort.key$ := +} +ITERATE {bib.sort.order} +SORT +FUNCTION {begin.bib} +{ preamble$ empty$ + 'skip$ + { preamble$ write$ newline$ } + if$ + "\begin{thebibliography}{" number.label int.to.str$ * "}" * + write$ newline$ + "\expandafter\ifx\csname natexlab\endcsname\relax\def\natexlab#1{#1}\fi" + write$ newline$ +} +EXECUTE {begin.bib} +EXECUTE {init.urlbst.variables} % urlbst +EXECUTE {init.state.consts} +ITERATE {call.type$} +FUNCTION {end.bib} +{ newline$ + "\end{thebibliography}" write$ newline$ +} +EXECUTE {end.bib} +%% End of customized bst file +%% +%% End of file `compling.bst'. diff --git a/references/2020.arxiv.nguyen/source/emnlp2020.sty b/references/2020.arxiv.nguyen/source/emnlp2020.sty new file mode 100644 index 0000000000000000000000000000000000000000..491e9b53ad03850f885eb62282342181332fc34f --- /dev/null +++ b/references/2020.arxiv.nguyen/source/emnlp2020.sty @@ -0,0 +1,560 @@ +% This is the LaTex style file for EMNLP 2020, based off of ACL 2020. + +% Addressing bibtex issues mentioned in https://github.com/acl-org/acl-pub/issues/2 +% Other major modifications include +% changing the color of the line numbers to a light gray; changing font size of abstract to be 10pt; changing caption font size to be 10pt. +% -- M Mitchell and Stephanie Lukin + +% 2017: modified to support DOI links in bibliography. Now uses +% natbib package rather than defining citation commands in this file. +% Use with acl_natbib.bst bib style. -- Dan Gildea + +% This is the LaTeX style for ACL 2016. It contains Margaret Mitchell's +% line number adaptations (ported by Hai Zhao and Yannick Versley). + +% It is nearly identical to the style files for ACL 2015, +% ACL 2014, EACL 2006, ACL2005, ACL 2002, ACL 2001, ACL 2000, +% EACL 95 and EACL 99. +% +% Changes made include: adapt layout to A4 and centimeters, widen abstract + +% This is the LaTeX style file for ACL 2000. It is nearly identical to the +% style files for EACL 95 and EACL 99. Minor changes include editing the +% instructions to reflect use of \documentclass rather than \documentstyle +% and removing the white space before the title on the first page +% -- John Chen, June 29, 2000 + +% This is the LaTeX style file for EACL-95. It is identical to the +% style file for ANLP '94 except that the margins are adjusted for A4 +% paper. -- abney 13 Dec 94 + +% The ANLP '94 style file is a slightly modified +% version of the style used for AAAI and IJCAI, using some changes +% prepared by Fernando Pereira and others and some minor changes +% by Paul Jacobs. + +% Papers prepared using the aclsub.sty file and acl.bst bibtex style +% should be easily converted to final format using this style. +% (1) Submission information (\wordcount, \subject, and \makeidpage) +% should be removed. +% (2) \summary should be removed. The summary material should come +% after \maketitle and should be in the ``abstract'' environment +% (between \begin{abstract} and \end{abstract}). +% (3) Check all citations. This style should handle citations correctly +% and also allows multiple citations separated by semicolons. +% (4) Check figures and examples. Because the final format is double- +% column, some adjustments may have to be made to fit text in the column +% or to choose full-width (\figure*} figures. + +% Place this in a file called aclap.sty in the TeX search path. +% (Placing it in the same directory as the paper should also work.) + +% Prepared by Peter F. Patel-Schneider, liberally using the ideas of +% other style hackers, including Barbara Beeton. +% This style is NOT guaranteed to work. It is provided in the hope +% that it will make the preparation of papers easier. +% +% There are undoubtably bugs in this style. If you make bug fixes, +% improvements, etc. please let me know. My e-mail address is: +% pfps@research.att.com + +% Papers are to be prepared using the ``acl_natbib'' bibliography style, +% as follows: +% \documentclass[11pt]{article} +% \usepackage{acl2000} +% \title{Title} +% \author{Author 1 \and Author 2 \\ Address line \\ Address line \And +% Author 3 \\ Address line \\ Address line} +% \begin{document} +% ... +% \bibliography{bibliography-file} +% \bibliographystyle{acl_natbib} +% \end{document} + +% Author information can be set in various styles: +% For several authors from the same institution: +% \author{Author 1 \and ... \and Author n \\ +% Address line \\ ... \\ Address line} +% if the names do not fit well on one line use +% Author 1 \\ {\bf Author 2} \\ ... \\ {\bf Author n} \\ +% For authors from different institutions: +% \author{Author 1 \\ Address line \\ ... \\ Address line +% \And ... \And +% Author n \\ Address line \\ ... \\ Address line} +% To start a seperate ``row'' of authors use \AND, as in +% \author{Author 1 \\ Address line \\ ... \\ Address line +% \AND +% Author 2 \\ Address line \\ ... \\ Address line \And +% Author 3 \\ Address line \\ ... \\ Address line} + +% If the title and author information does not fit in the area allocated, +% place \setlength\titlebox{} right after +% \usepackage{acl2015} +% where can be something larger than 5cm + +% include hyperref, unless user specifies nohyperref option like this: +% \usepackage[nohyperref]{naaclhlt2018} +\newif\ifacl@hyperref +\DeclareOption{hyperref}{\acl@hyperreftrue} +\DeclareOption{nohyperref}{\acl@hyperreffalse} +\ExecuteOptions{hyperref} % default is to use hyperref +\ProcessOptions\relax +\ifacl@hyperref + \RequirePackage{hyperref} + \usepackage{xcolor} % make links dark blue + \definecolor{darkblue}{rgb}{0, 0, 0.5} + \hypersetup{colorlinks=true,citecolor=darkblue, linkcolor=darkblue, urlcolor=darkblue} +\else + % This definition is used if the hyperref package is not loaded. + % It provides a backup, no-op definiton of \href. + % This is necessary because \href command is used in the acl_natbib.bst file. + \def\href#1#2{{#2}} + % We still need to load xcolor in this case because the lighter line numbers require it. (SC/KG/WL) + \usepackage{xcolor} +\fi + +\typeout{Conference Style for EMNLP 2020} + +% NOTE: Some laser printers have a serious problem printing TeX output. +% These printing devices, commonly known as ``write-white'' laser +% printers, tend to make characters too light. To get around this +% problem, a darker set of fonts must be created for these devices. +% + +\newcommand{\Thanks}[1]{\thanks{\ #1}} + +% A4 modified by Eneko; again modified by Alexander for 5cm titlebox +\setlength{\paperwidth}{21cm} % A4 +\setlength{\paperheight}{29.7cm}% A4 +\setlength\topmargin{-0.5cm} +\setlength\oddsidemargin{0cm} +\setlength\textheight{24.7cm} +\setlength\textwidth{16.0cm} +\setlength\columnsep{0.6cm} +\newlength\titlebox +\setlength\titlebox{5cm} +\setlength\headheight{5pt} +\setlength\headsep{0pt} +\thispagestyle{empty} +\pagestyle{empty} + + +\flushbottom \twocolumn \sloppy + +% We're never going to need a table of contents, so just flush it to +% save space --- suggested by drstrip@sandia-2 +\def\addcontentsline#1#2#3{} + +\newif\ifaclfinal +\aclfinalfalse +\def\aclfinalcopy{\global\aclfinaltrue} + +%% ----- Set up hooks to repeat content on every page of the output doc, +%% necessary for the line numbers in the submitted version. --MM +%% +%% Copied from CVPR 2015's cvpr_eso.sty, which appears to be largely copied from everyshi.sty. +%% +%% Original cvpr_eso.sty available at: http://www.pamitc.org/cvpr15/author_guidelines.php +%% Original evershi.sty available at: https://www.ctan.org/pkg/everyshi +%% +%% Copyright (C) 2001 Martin Schr\"oder: +%% +%% Martin Schr"oder +%% Cr"usemannallee 3 +%% D-28213 Bremen +%% Martin.Schroeder@ACM.org +%% +%% This program may be redistributed and/or modified under the terms +%% of the LaTeX Project Public License, either version 1.0 of this +%% license, or (at your option) any later version. +%% The latest version of this license is in +%% CTAN:macros/latex/base/lppl.txt. +%% +%% Happy users are requested to send [Martin] a postcard. :-) +%% +\newcommand{\@EveryShipoutACL@Hook}{} +\newcommand{\@EveryShipoutACL@AtNextHook}{} +\newcommand*{\EveryShipoutACL}[1] + {\g@addto@macro\@EveryShipoutACL@Hook{#1}} +\newcommand*{\AtNextShipoutACL@}[1] + {\g@addto@macro\@EveryShipoutACL@AtNextHook{#1}} +\newcommand{\@EveryShipoutACL@Shipout}{% + \afterassignment\@EveryShipoutACL@Test + \global\setbox\@cclv= % + } +\newcommand{\@EveryShipoutACL@Test}{% + \ifvoid\@cclv\relax + \aftergroup\@EveryShipoutACL@Output + \else + \@EveryShipoutACL@Output + \fi% + } +\newcommand{\@EveryShipoutACL@Output}{% + \@EveryShipoutACL@Hook% + \@EveryShipoutACL@AtNextHook% + \gdef\@EveryShipoutACL@AtNextHook{}% + \@EveryShipoutACL@Org@Shipout\box\@cclv% + } +\newcommand{\@EveryShipoutACL@Org@Shipout}{} +\newcommand*{\@EveryShipoutACL@Init}{% + \message{ABD: EveryShipout initializing macros}% + \let\@EveryShipoutACL@Org@Shipout\shipout + \let\shipout\@EveryShipoutACL@Shipout + } +\AtBeginDocument{\@EveryShipoutACL@Init} + +%% ----- Set up for placing additional items into the submitted version --MM +%% +%% Based on eso-pic.sty +%% +%% Original available at: https://www.ctan.org/tex-archive/macros/latex/contrib/eso-pic +%% Copyright (C) 1998-2002 by Rolf Niepraschk +%% +%% Which may be distributed and/or modified under the conditions of +%% the LaTeX Project Public License, either version 1.2 of this license +%% or (at your option) any later version. The latest version of this +%% license is in: +%% +%% http://www.latex-project.org/lppl.txt +%% +%% and version 1.2 or later is part of all distributions of LaTeX version +%% 1999/12/01 or later. +%% +%% In contrast to the original, we do not include the definitions for/using: +%% gridpicture, div[2], isMEMOIR[1], gridSetup[6][], subgridstyle{dotted}, labelfactor{}, gap{}, gridunitname{}, gridunit{}, gridlines{\thinlines}, subgridlines{\thinlines}, the {keyval} package, evenside margin, nor any definitions with 'color'. +%% +%% These are beyond what is needed for the NAACL/ACL style. +%% +\newcommand\LenToUnit[1]{#1\@gobble} +\newcommand\AtPageUpperLeft[1]{% + \begingroup + \@tempdima=0pt\relax\@tempdimb=\ESO@yoffsetI\relax + \put(\LenToUnit{\@tempdima},\LenToUnit{\@tempdimb}){#1}% + \endgroup +} +\newcommand\AtPageLowerLeft[1]{\AtPageUpperLeft{% + \put(0,\LenToUnit{-\paperheight}){#1}}} +\newcommand\AtPageCenter[1]{\AtPageUpperLeft{% + \put(\LenToUnit{.5\paperwidth},\LenToUnit{-.5\paperheight}){#1}}} +\newcommand\AtPageLowerCenter[1]{\AtPageUpperLeft{% + \put(\LenToUnit{.5\paperwidth},\LenToUnit{-\paperheight}){#1}}}% +\newcommand\AtPageLowishCenter[1]{\AtPageUpperLeft{% + \put(\LenToUnit{.5\paperwidth},\LenToUnit{-.96\paperheight}){#1}}} +\newcommand\AtTextUpperLeft[1]{% + \begingroup + \setlength\@tempdima{1in}% + \advance\@tempdima\oddsidemargin% + \@tempdimb=\ESO@yoffsetI\relax\advance\@tempdimb-1in\relax% + \advance\@tempdimb-\topmargin% + \advance\@tempdimb-\headheight\advance\@tempdimb-\headsep% + \put(\LenToUnit{\@tempdima},\LenToUnit{\@tempdimb}){#1}% + \endgroup +} +\newcommand\AtTextLowerLeft[1]{\AtTextUpperLeft{% + \put(0,\LenToUnit{-\textheight}){#1}}} +\newcommand\AtTextCenter[1]{\AtTextUpperLeft{% + \put(\LenToUnit{.5\textwidth},\LenToUnit{-.5\textheight}){#1}}} +\newcommand{\ESO@HookI}{} \newcommand{\ESO@HookII}{} +\newcommand{\ESO@HookIII}{} +\newcommand{\AddToShipoutPicture}{% + \@ifstar{\g@addto@macro\ESO@HookII}{\g@addto@macro\ESO@HookI}} +\newcommand{\ClearShipoutPicture}{\global\let\ESO@HookI\@empty} +\newcommand{\@ShipoutPicture}{% + \bgroup + \@tempswafalse% + \ifx\ESO@HookI\@empty\else\@tempswatrue\fi% + \ifx\ESO@HookII\@empty\else\@tempswatrue\fi% + \ifx\ESO@HookIII\@empty\else\@tempswatrue\fi% + \if@tempswa% + \@tempdima=1in\@tempdimb=-\@tempdima% + \advance\@tempdimb\ESO@yoffsetI% + \unitlength=1pt% + \global\setbox\@cclv\vbox{% + \vbox{\let\protect\relax + \pictur@(0,0)(\strip@pt\@tempdima,\strip@pt\@tempdimb)% + \ESO@HookIII\ESO@HookI\ESO@HookII% + \global\let\ESO@HookII\@empty% + \endpicture}% + \nointerlineskip% + \box\@cclv}% + \fi + \egroup +} +\EveryShipoutACL{\@ShipoutPicture} +\newif\ifESO@dvips\ESO@dvipsfalse +\newif\ifESO@grid\ESO@gridfalse +\newif\ifESO@texcoord\ESO@texcoordfalse +\newcommand*\ESO@griddelta{}\newcommand*\ESO@griddeltaY{} +\newcommand*\ESO@gridDelta{}\newcommand*\ESO@gridDeltaY{} +\newcommand*\ESO@yoffsetI{}\newcommand*\ESO@yoffsetII{} +\ifESO@texcoord + \def\ESO@yoffsetI{0pt}\def\ESO@yoffsetII{-\paperheight} + \edef\ESO@griddeltaY{-\ESO@griddelta}\edef\ESO@gridDeltaY{-\ESO@gridDelta} +\else + \def\ESO@yoffsetI{\paperheight}\def\ESO@yoffsetII{0pt} + \edef\ESO@griddeltaY{\ESO@griddelta}\edef\ESO@gridDeltaY{\ESO@gridDelta} +\fi + + +%% ----- Submitted version markup: Page numbers, ruler, and confidentiality. Using ideas/code from cvpr.sty 2015. --MM + +\font\aclhv = phvb at 8pt + +%% Define vruler %% + +%\makeatletter +\newbox\aclrulerbox +\newcount\aclrulercount +\newdimen\aclruleroffset +\newdimen\cv@lineheight +\newdimen\cv@boxheight +\newbox\cv@tmpbox +\newcount\cv@refno +\newcount\cv@tot +% NUMBER with left flushed zeros \fillzeros[] +\newcount\cv@tmpc@ \newcount\cv@tmpc +\def\fillzeros[#1]#2{\cv@tmpc@=#2\relax\ifnum\cv@tmpc@<0\cv@tmpc@=-\cv@tmpc@\fi +\cv@tmpc=1 % +\loop\ifnum\cv@tmpc@<10 \else \divide\cv@tmpc@ by 10 \advance\cv@tmpc by 1 \fi + \ifnum\cv@tmpc@=10\relax\cv@tmpc@=11\relax\fi \ifnum\cv@tmpc@>10 \repeat +\ifnum#2<0\advance\cv@tmpc1\relax-\fi +\loop\ifnum\cv@tmpc<#1\relax0\advance\cv@tmpc1\relax\fi \ifnum\cv@tmpc<#1 \repeat +\cv@tmpc@=#2\relax\ifnum\cv@tmpc@<0\cv@tmpc@=-\cv@tmpc@\fi \relax\the\cv@tmpc@}% +% \makevruler[][][][][] +\def\makevruler[#1][#2][#3][#4][#5]{\begingroup\offinterlineskip +\textheight=#5\vbadness=10000\vfuzz=120ex\overfullrule=0pt% +\global\setbox\aclrulerbox=\vbox to \textheight{% +{\parskip=0pt\hfuzz=150em\cv@boxheight=\textheight +\color{gray} +\cv@lineheight=#1\global\aclrulercount=#2% +\cv@tot\cv@boxheight\divide\cv@tot\cv@lineheight\advance\cv@tot2% +\cv@refno1\vskip-\cv@lineheight\vskip1ex% +\loop\setbox\cv@tmpbox=\hbox to0cm{{\aclhv\hfil\fillzeros[#4]\aclrulercount}}% +\ht\cv@tmpbox\cv@lineheight\dp\cv@tmpbox0pt\box\cv@tmpbox\break +\advance\cv@refno1\global\advance\aclrulercount#3\relax +\ifnum\cv@refno<\cv@tot\repeat}}\endgroup}% +%\makeatother + + +\def\aclpaperid{***} +\def\confidential{\textcolor{black}{EMNLP 2020 Submission~\aclpaperid. Confidential Review Copy. DO NOT DISTRIBUTE.}} + +%% Page numbering, Vruler and Confidentiality %% +% \makevruler[][][][][] + +% SC/KG/WL - changed line numbering to gainsboro +\definecolor{gainsboro}{rgb}{0.8, 0.8, 0.8} +%\def\aclruler#1{\makevruler[14.17pt][#1][1][3][\textheight]\usebox{\aclrulerbox}} %% old line +\def\aclruler#1{\textcolor{gainsboro}{\makevruler[14.17pt][#1][1][3][\textheight]\usebox{\aclrulerbox}}} + +\def\leftoffset{-2.1cm} %original: -45pt +\def\rightoffset{17.5cm} %original: 500pt +\ifaclfinal\else\pagenumbering{arabic} +\AddToShipoutPicture{% +\ifaclfinal\else +\AtPageLowishCenter{\textcolor{black}{\thepage}} +\aclruleroffset=\textheight +\advance\aclruleroffset4pt + \AtTextUpperLeft{% + \put(\LenToUnit{\leftoffset},\LenToUnit{-\aclruleroffset}){%left ruler + \aclruler{\aclrulercount}} + \put(\LenToUnit{\rightoffset},\LenToUnit{-\aclruleroffset}){%right ruler + \aclruler{\aclrulercount}} + } + \AtTextUpperLeft{%confidential + \put(0,\LenToUnit{1cm}){\parbox{\textwidth}{\centering\aclhv\confidential}} + } +\fi +} + +%%%% ----- End settings for placing additional items into the submitted version --MM ----- %%%% + +%%%% ----- Begin settings for both submitted and camera-ready version ----- %%%% + +%% Title and Authors %% + +\newcommand\outauthor{ + \begin{tabular}[t]{c} + \ifaclfinal + \bf\@author + \else + % Avoiding common accidental de-anonymization issue. --MM + \bf Anonymous EMNLP submission + \fi + \end{tabular}} + +% Changing the expanded titlebox for submissions to 2.5 in (rather than 6.5cm) +% and moving it to the style sheet, rather than within the example tex file. --MM +\ifaclfinal +\else + \addtolength\titlebox{.25in} +\fi +% Mostly taken from deproc. +\def\maketitle{\par + \begingroup + \def\thefootnote{\fnsymbol{footnote}} + \def\@makefnmark{\hbox to 0pt{$^{\@thefnmark}$\hss}} + \twocolumn[\@maketitle] \@thanks + \endgroup + \setcounter{footnote}{0} + \let\maketitle\relax \let\@maketitle\relax + \gdef\@thanks{}\gdef\@author{}\gdef\@title{}\let\thanks\relax} +\def\@maketitle{\vbox to \titlebox{\hsize\textwidth + \linewidth\hsize \vskip 0.125in minus 0.125in \centering + {\Large\bf \@title \par} \vskip 0.2in plus 1fil minus 0.1in + {\def\and{\unskip\enspace{\rm and}\enspace}% + \def\And{\end{tabular}\hss \egroup \hskip 1in plus 2fil + \hbox to 0pt\bgroup\hss \begin{tabular}[t]{c}\bf}% + \def\AND{\end{tabular}\hss\egroup \hfil\hfil\egroup + \vskip 0.25in plus 1fil minus 0.125in + \hbox to \linewidth\bgroup\large \hfil\hfil + \hbox to 0pt\bgroup\hss \begin{tabular}[t]{c}\bf} + \hbox to \linewidth\bgroup\large \hfil\hfil + \hbox to 0pt\bgroup\hss + \outauthor + \hss\egroup + \hfil\hfil\egroup} + \vskip 0.3in plus 2fil minus 0.1in +}} + +% margins and font size for abstract +\renewenvironment{abstract}% + {\centerline{\large\bf Abstract}% + \begin{list}{}% + {\setlength{\rightmargin}{0.6cm}% + \setlength{\leftmargin}{0.6cm}}% + \item[]\ignorespaces% + \@setsize\normalsize{12pt}\xpt\@xpt + }% + {\unskip\end{list}} + +%\renewenvironment{abstract}{\centerline{\large\bf +% Abstract}\vspace{0.5ex}\begin{quote}}{\par\end{quote}\vskip 1ex} + +% Resizing figure and table captions - SL +\newcommand{\figcapfont}{\rm} +\newcommand{\tabcapfont}{\rm} +\renewcommand{\fnum@figure}{\figcapfont Figure \thefigure} +\renewcommand{\fnum@table}{\tabcapfont Table \thetable} +\renewcommand{\figcapfont}{\@setsize\normalsize{12pt}\xpt\@xpt} +\renewcommand{\tabcapfont}{\@setsize\normalsize{12pt}\xpt\@xpt} +% Support for interacting with the caption, subfigure, and subcaption packages - SL +\usepackage{caption} +\DeclareCaptionFont{10pt}{\fontsize{10pt}{12pt}\selectfont} +\captionsetup{font=10pt} + +\RequirePackage{natbib} +% for citation commands in the .tex, authors can use: +% \citep, \citet, and \citeyearpar for compatibility with natbib, or +% \cite, \newcite, and \shortcite for compatibility with older ACL .sty files +\renewcommand\cite{\citep} % to get "(Author Year)" with natbib +\newcommand\shortcite{\citeyearpar}% to get "(Year)" with natbib +\newcommand\newcite{\citet} % to get "Author (Year)" with natbib + +% DK/IV: Workaround for annoying hyperref pagewrap bug +%\RequirePackage{etoolbox} +%\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{\errmessage{\noexpand patch failed}} + +% bibliography + +\def\@up#1{\raise.2ex\hbox{#1}} + +% Don't put a label in the bibliography at all. Just use the unlabeled format +% instead. +\def\thebibliography#1{\vskip\parskip% +\vskip\baselineskip% +\def\baselinestretch{1}% +\ifx\@currsize\normalsize\@normalsize\else\@currsize\fi% +\vskip-\parskip% +\vskip-\baselineskip% +\section*{References\@mkboth + {References}{References}}\list + {}{\setlength{\labelwidth}{0pt}\setlength{\leftmargin}{\parindent} + \setlength{\itemindent}{-\parindent}} + \def\newblock{\hskip .11em plus .33em minus -.07em} + \sloppy\clubpenalty4000\widowpenalty4000 + \sfcode`\.=1000\relax} +\let\endthebibliography=\endlist + + +% Allow for a bibliography of sources of attested examples +\def\thesourcebibliography#1{\vskip\parskip% +\vskip\baselineskip% +\def\baselinestretch{1}% +\ifx\@currsize\normalsize\@normalsize\else\@currsize\fi% +\vskip-\parskip% +\vskip-\baselineskip% +\section*{Sources of Attested Examples\@mkboth + {Sources of Attested Examples}{Sources of Attested Examples}}\list + {}{\setlength{\labelwidth}{0pt}\setlength{\leftmargin}{\parindent} + \setlength{\itemindent}{-\parindent}} + \def\newblock{\hskip .11em plus .33em minus -.07em} + \sloppy\clubpenalty4000\widowpenalty4000 + \sfcode`\.=1000\relax} +\let\endthesourcebibliography=\endlist + +% sections with less space +\def\section{\@startsection {section}{1}{\z@}{-2.0ex plus + -0.5ex minus -.2ex}{1.5ex plus 0.3ex minus .2ex}{\large\bf\raggedright}} +\def\subsection{\@startsection{subsection}{2}{\z@}{-1.8ex plus + -0.5ex minus -.2ex}{0.8ex plus .2ex}{\normalsize\bf\raggedright}} +%% changed by KO to - 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a/references/2020.arxiv.nguyen/source/emnlp2020_PhoBERT.tex b/references/2020.arxiv.nguyen/source/emnlp2020_PhoBERT.tex new file mode 100644 index 0000000000000000000000000000000000000000..56780c089d7eee7ee5ef9e23be4d4ac72eea3db9 --- /dev/null +++ b/references/2020.arxiv.nguyen/source/emnlp2020_PhoBERT.tex @@ -0,0 +1,301 @@ +\documentclass[11pt,a4paper]{article} +\usepackage[hyperref]{emnlp2020} +\pdfoutput=1 +\usepackage{times} +\usepackage{latexsym} +%\renewcommand{\UrlFont}{\ttfamily\small} + +\usepackage{times} +\usepackage{latexsym} +\usepackage{amsmath} +\usepackage{url} +\usepackage{amssymb} +\usepackage{amsfonts} +\usepackage{graphicx} +\usepackage{tabularx} +\usepackage{multirow} +\usepackage{arydshln} +\usepackage{mathtools,nccmath} + +\usepackage[utf8]{inputenc} +\usepackage[utf8]{vietnam} +\usepackage{enumitem} +% This is not strictly necessary, and may be commented out, +% but it will improve the layout of the manuscript, +% and will typically save some space. +%\usepackage{microtype} + + +\setlength{\textfloatsep}{15pt plus 5.0pt minus 5.0pt} +\setlength{\floatsep}{15pt plus 5.0pt minus 5.0pt} +%\setlength{\dbltextfloatsep }{15pt plus 2.0pt minus 3.0pt} +%\setlength{\dblfloatsep}{15pt plus 2.0pt minus 3.0pt} +%\setlength{\intextsep}{15pt plus 2.0pt minus 3.0pt} +\setlength{\abovecaptionskip}{3pt plus 1pt minus 1pt} + +\aclfinalcopy % Uncomment this line for the final submission +%\def\aclpaperid{***} % Enter the acl Paper ID here + +\setlength\titlebox{5cm} +% You can expand the titlebox if you need extra space +% to show all the authors. Please do not make the titlebox +% smaller than 5cm (the original size); we will check this +% in the camera-ready version and ask you to change it back. + +\newcommand\BibTeX{B\textsc{ib}\TeX} + + +\title{PhoBERT: Pre-trained language models for Vietnamese} + +\author{Dat Quoc Nguyen$^1$ \and Anh Tuan Nguyen$^{2,}$\thanks{\ \ Work done during internship at VinAI Research.} \\ + $^1$VinAI Research, Vietnam; $^2$NVIDIA, USA\\ + \tt{\normalsize v.datnq9@vinai.io, tuananhn@nvidia.com}} + +\date{} + +\begin{document} +\maketitle +\begin{abstract} +We present \textbf{PhoBERT} with two versions---PhoBERT\textsubscript{base} and PhoBERT\textsubscript{large}---the \emph{first} public large-scale monolingual language models pre-trained for Vietnamese. Experimental results show that PhoBERT consistently outperforms the recent best pre-trained multilingual model XLM-R \citep{conneau2019unsupervised} and improves the state-of-the-art in multiple Vietnamese-specific NLP tasks including Part-of-speech tagging, Dependency parsing, Named-entity recognition and Natural language inference. We release PhoBERT to facilitate future research and downstream applications for Vietnamese NLP. Our PhoBERT models are available at: \url{https://github.com/VinAIResearch/PhoBERT}. +\end{abstract} + +\section{Introduction}\label{sec:intro} + + +Pre-trained language models, especially BERT \citep{devlin-etal-2019-bert}---the Bidirectional Encoder Representations from Transformers \citep{NIPS2017_7181}, have recently become extremely popular and helped to produce significant improvement gains for various NLP tasks. The success of pre-trained BERT and its variants has largely been limited to the English language. For other languages, one could retrain a language-specific model using the BERT architecture \citep{abs-1906-08101,vries2019bertje,vu-xuan-etal-2019-etnlp,2019arXiv191103894M} or employ existing pre-trained multilingual BERT-based models \citep{devlin-etal-2019-bert,NIPS2019_8928,conneau2019unsupervised}. + +In terms of Vietnamese language modeling, to the best of our knowledge, there are two main concerns as follows: + +\begin{itemize}[leftmargin=*] +\setlength\itemsep{-1pt} + \item The Vietnamese Wikipedia corpus is the only data used to train monolingual language models \citep{vu-xuan-etal-2019-etnlp}, and it also is the only Vietnamese dataset which is included in the pre-training data used by all multilingual language models except XLM-R. It is worth noting that Wikipedia data is not representative of a general language use, and the Vietnamese Wikipedia data is relatively small (1GB in size uncompressed), while pre-trained language models can be significantly improved by using more pre-training data \cite{RoBERTa}. + + \item All publicly released monolingual and multilingual BERT-based language models are not aware of the difference between Vietnamese syllables and word tokens. This ambiguity comes from the fact that the white space is also used to separate syllables that constitute words when written in Vietnamese.\footnote{\newcite{DinhQuangThang2008} show that 85\% of Vietnamese word types are composed of at least two syllables.} + For example, a 6-syllable written text ``Tôi là một nghiên cứu viên'' (I am a researcher) forms 4 words ``Tôi\textsubscript{I} là\textsubscript{am} một\textsubscript{a} nghiên\_cứu\_viên\textsubscript{researcher}''. \\ +Without doing a pre-process step of Vietnamese word segmentation, those models directly apply Byte-Pair encoding (BPE) methods \citep{sennrich-etal-2016-neural,kudo-richardson-2018-sentencepiece} to the syllable-level Vietnamese pre-training data.\footnote{Although performing word segmentation before applying BPE on the Vietnamese Wikipedia corpus, ETNLP \citep{vu-xuan-etal-2019-etnlp} in fact {does not publicly release} any pre-trained BERT-based language model (\url{https://github.com/vietnlp/etnlp}). In particular, \newcite{vu-xuan-etal-2019-etnlp} release a set of 15K BERT-based word embeddings specialized only for the Vietnamese NER task.} +Intuitively, for word-level Vietnamese NLP tasks, those models pre-trained on syllable-level data might not perform as good as language models pre-trained on word-level data. + +\end{itemize} + + +To handle the two concerns above, we train the {first} large-scale monolingual BERT-based ``base'' and ``large'' models using a 20GB \textit{word-level} Vietnamese corpus. +We evaluate our models on four downstream Vietnamese NLP tasks: the common word-level ones of Part-of-speech (POS) tagging, Dependency parsing and Named-entity recognition (NER), and a language understanding task of Natural language inference (NLI) which can be formulated as either a syllable- or word-level task. Experimental results show that our models obtain state-of-the-art (SOTA) results on all these tasks. +Our contributions are summarized as follows: + +\begin{itemize}[leftmargin=*] +\setlength\itemsep{-1pt} + \item We present the \textit{first} large-scale monolingual language models pre-trained for Vietnamese. + + \item Our models help produce SOTA performances on four downstream tasks of POS tagging, Dependency parsing, NER and NLI, thus showing the effectiveness of large-scale BERT-based monolingual language models for Vietnamese. + + \item To the best of our knowledge, we also perform the \textit{first} set of experiments to compare monolingual language models with the recent best multilingual model XLM-R in multiple (i.e. four) different language-specific tasks. The experiments show that our models outperform XLM-R on all these tasks, thus convincingly confirming that dedicated language-specific models still outperform multilingual ones. + + \item We publicly release our models under the name PhoBERT which can be used with \texttt{fairseq} \citep{ott2019fairseq} and \texttt{transformers} \cite{Wolf2019HuggingFacesTS}. We hope that PhoBERT can serve as a strong baseline for future Vietnamese NLP research and applications. +\end{itemize} + + + + + + + + +\section{PhoBERT} + +This section outlines the architecture and describes the pre-training data and optimization setup that we use for PhoBERT. + +\vspace{3pt} + +\noindent\textbf{Architecture:}\ Our PhoBERT has two versions, PhoBERT\textsubscript{base} and PhoBERT\textsubscript{large}, using the same architectures of BERT\textsubscript{base} and BERT\textsubscript{large}, respectively. PhoBERT pre-training approach is based on RoBERTa \citep{RoBERTa} which optimizes the BERT pre-training procedure for more robust performance. + +\vspace{3pt} + +\noindent\textbf{Pre-training data:}\ To handle the first concern mentioned in Section \ref{sec:intro}, we use a 20GB pre-training dataset of uncompressed texts. This dataset is a concatenation of two corpora: (i) the first one is the Vietnamese Wikipedia corpus ($\sim$1GB), and (ii) the second corpus ($\sim$19GB) is generated by removing similar articles and duplication from a 50GB Vietnamese news corpus.\footnote{\url{https://github.com/binhvq/news-corpus}, crawled from a wide range of news websites and topics.} To solve the second concern, +we employ RDRSegmenter \citep{nguyen-etal-2018-fast} from VnCoreNLP \citep{vu-etal-2018-vncorenlp} to perform word and sentence segmentation on the pre-training dataset, resulting in $\sim$145M word-segmented sentences ($\sim$3B word tokens). Different from RoBERTa, we then apply \texttt{fastBPE} \citep{sennrich-etal-2016-neural} to segment these sentences with subword units, using a vocabulary of 64K subword types. On average there are 24.4 subword tokens per sentence. + +\vspace{3pt} + +\noindent\textbf{Optimization:}\ We employ the RoBERTa implementation in \texttt{fairseq} \citep{ott2019fairseq}. We set a maximum length at 256 subword tokens, thus generating 145M $\times$ 24.4 / 256 $\approx$ 13.8M sentence blocks. Following \newcite{RoBERTa}, we optimize the models using Adam \citep{KingmaB14}. We use a batch size of 1024 across 4 V100 GPUs (16GB each) and a peak learning rate of 0.0004 for PhoBERT\textsubscript{base}, and a batch size of 512 and a peak learning rate of 0.0002 for PhoBERT\textsubscript{large}. We run for 40 epochs (here, the learning rate is warmed up for 2 epochs), thus resulting in 13.8M $\times$ 40 / 1024 $\approx$ 540K training steps for PhoBERT\textsubscript{base} and 1.08M training steps for PhoBERT\textsubscript{large}. We pre-train PhoBERT\textsubscript{base} during 3 weeks, and then PhoBERT\textsubscript{large} during 5 weeks. + + +\begin{table}[!t] + \centering + \begin{tabular}{l|l|l|l} + \hline + \textbf{Task} & \textbf{\#training} & \textbf{\#valid} & \textbf{\#test} \\ + \hline + + POS tagging$^\dagger$ & 27,000 & 870 & 2,120 \\ + Dep. parsing$^\dagger$ & 8,977 & 200 & 1,020 \\ + NER$^\dagger$ & 14,861 & 2,000 & 2,831\\ + NLI$^\ddagger$ & 392,702 & 2,490 & 5,010\\ + \hline + \end{tabular} + \caption{Statistics of the downstream task datasets. ``\#training'', ``\#valid'' and ``\#test'' denote the size of the training, validation and test sets, respectively. $\dagger$ and $\ddagger$ refer to the dataset size as the numbers of sentences and sentence pairs, respectively.} + \label{tab:data} +\end{table} + + + \begin{table*}[!ht] + \centering + \resizebox{15.5cm}{!}{ + %\setlength{\tabcolsep}{0.3em} + \begin{tabular}{l|l|l|l} + \hline + \multicolumn{2}{c|}{\textbf{POS tagging} (word-level)} & \multicolumn{2}{c}{\textbf{Dependency parsing} (word-level)}\\ + \hline + Model & Acc. & Model & LAS / UAS \\ + \hline + RDRPOSTagger \citep{nguyen-etal-2014-rdrpostagger} [$\clubsuit$] & 95.1 & \_ & \_ \\ + + BiLSTM-CNN-CRF \citep{ma-hovy-2016-end} [$\clubsuit$] & 95.4 & VnCoreNLP-DEP \citep{vu-etal-2018-vncorenlp} [$\bigstar$] & 71.38 / 77.35 \\ + + + VnCoreNLP-POS \citep{nguyen-etal-2017-word} [$\clubsuit$] & 95.9 &jPTDP-v2 [$\bigstar$] & 73.12 / 79.63 \\ + + jPTDP-v2 \citep{nguyen-verspoor-2018-improved} [$\bigstar$] & 95.7 &jointWPD [$\bigstar$] & 73.90 / 80.12 \\ + + jointWPD \citep{nguyen-2019-neural} [$\bigstar$] & 96.0 & Biaffine \citep{DozatM17} [$\bigstar$] & 74.99 / 81.19 \\ + + XLM-R\textsubscript{base} (our result) & 96.2 & Biaffine w/ XLM-R\textsubscript{base} (our result) & 76.46 / 83.10 \\ + + XLM-R\textsubscript{large} (our result) & 96.3 & Biaffine w/ XLM-R\textsubscript{large} (our result) & 75.87 / 82.70 \\ + + \hline + PhoBERT\textsubscript{base} & \underline{96.7} & Biaffine w/ PhoBERT\textsubscript{base} & \textbf{78.77} / \textbf{85.22} \\ + + PhoBERT\textsubscript{large} & \textbf{96.8} & Biaffine w/ PhoBERT\textsubscript{large} & \underline{77.85} / \underline{84.32} \\ + \hline + \end{tabular} + } + \caption{Performance scores (in \%) on the POS tagging and Dependency parsing test sets. ``Acc.'', ``LAS'' and ``UAS'' abbreviate the Accuracy, the Labeled Attachment Score and the Unlabeled Attachment Score, respectively (here, all these evaluation metrics are computed on all word tokens, including punctuation). + [$\clubsuit$] and [$\bigstar$] denote + results reported by \newcite{nguyen-etal-2017-word} and \newcite{nguyen-2019-neural}, respectively.} + \label{tab:posdep} + \end{table*} + + + +\section{Experimental setup} + + We evaluate the performance of PhoBERT on four downstream Vietnamese NLP tasks: POS tagging, Dependency parsing, NER and NLI. + + +\subsubsection*{Downstream task datasets} + +Table \ref{tab:data} presents the statistics of the experimental datasets that we employ for downstream task evaluation. +For POS tagging, Dependency parsing and NER, we follow the VnCoreNLP setup \citep{vu-etal-2018-vncorenlp}, using standard benchmarks of the VLSP 2013 POS tagging dataset,\footnote{\url{https://vlsp.org.vn/vlsp2013/eval}} the VnDT dependency treebank v1.1 \cite{Nguyen2014NLDB} with POS tags predicted by VnCoreNLP and the VLSP 2016 NER dataset \citep{JCC13161}. + +For NLI, we use the manually-constructed Vietnamese validation and test sets from the cross-lingual NLI (XNLI) corpus v1.0 \citep{conneau-etal-2018-xnli} where the Vietnamese training set is released as a machine-translated version of the corresponding English training set \citep{N18-1101}. +Unlike the POS tagging, Dependency parsing and NER datasets which provide the gold word segmentation, for NLI, we employ RDRSegmenter to segment the text into words before applying BPE to produce subwords from word tokens. + +\subsubsection*{Fine-tuning} + +Following \newcite{devlin-etal-2019-bert}, for POS tagging and NER, we append a linear prediction layer on top of the PhoBERT architecture (i.e. to the last Transformer layer of PhoBERT) w.r.t. the first subword of each word token.\footnote{In our preliminary experiments, using the average of contextualized embeddings of subword tokens of each word to represent the word produces slightly lower performance than using the contextualized embedding of the first subword.} +For dependency parsing, following \newcite{nguyen-2019-neural}, we employ a reimplementation of the state-of-the-art Biaffine dependency parser \citep{DozatM17} from \newcite{ma-etal-2018-stack} with default optimal hyper-parameters. %\footnote{\url{https://github.com/XuezheMax/NeuroNLP2}} +We then extend this parser by replacing the pre-trained word embedding of each word in an input sentence by the corresponding contextualized embedding (from the last layer) computed for the first subword token of the word. + +For POS tagging, NER and NLI, we employ \texttt{transformers} \cite{Wolf2019HuggingFacesTS} to fine-tune PhoBERT for each task and each dataset independently. We use AdamW \citep{loshchilov2018decoupled} with a fixed learning rate of 1.e-5 and a batch size of 32 \citep{RoBERTa}. We fine-tune in 30 training epochs, evaluate the task performance after each epoch on the validation set (here, early stopping is applied when there is no improvement after 5 continuous epochs), and then select the best model checkpoint to report the final result on the test set (note that each of our scores is an average over 5 runs with different random seeds). %Section \ref{sec:results} shows that using this relatively straightforward fine-tuning manner can lead to SOTA results. %Note that we might boost our downstream task performances even further by doing a more careful hyper-parameter tuning. + + + + + \begin{table*}[!ht] + \centering + \resizebox{15.5cm}{!}{ + %\setlength{\tabcolsep}{0.3em} + \begin{tabular}{l|l|l|l} + \hline + \multicolumn{2}{c|}{\textbf{NER} (word-level)} & \multicolumn{2}{c}{\textbf{NLI} (syllable- or word-level)} \\ + + \hline + Model & F\textsubscript{1} & Model & Acc. \\ + \hline + BiLSTM-CNN-CRF [$\blacklozenge$] & 88.3 & \_ & \_\\ + + VnCoreNLP-NER \citep{vu-etal-2018-vncorenlp} [$\blacklozenge$] & 88.6 & BiLSTM-max \citep{conneau-etal-2018-xnli} & 66.4 \\ + + + VNER \citep{8713740} & 89.6 & mBiLSTM \citep{ArtetxeS19} & 72.0 \\ + + BiLSTM-CNN-CRF + ETNLP [$\spadesuit$] & 91.1 & multilingual BERT \citep{devlin-etal-2019-bert} [$\blacksquare$] & 69.5 \\ + + VnCoreNLP-NER + ETNLP [$\spadesuit$] & 91.3 & XLM\textsubscript{MLM+TLM} \citep{NIPS2019_8928} & 76.6 \\ + + XLM-R\textsubscript{base} (our result) & 92.0 & XLM-R\textsubscript{base} \citep{conneau2019unsupervised} & {75.4} \\ + + XLM-R\textsubscript{large} (our result) & 92.8 & XLM-R\textsubscript{large} \citep{conneau2019unsupervised} & \underline{79.7} \\ + + \hline + PhoBERT\textsubscript{base}& \underline{93.6} & PhoBERT\textsubscript{base}& {78.5} \\ + + PhoBERT\textsubscript{large}& \textbf{94.7} & PhoBERT\textsubscript{large}& \textbf{80.0} \\ + \hline + + + \end{tabular} + } + \caption{Performance scores (in \%) on the NER and NLI test sets. + [$\blacklozenge$], [$\spadesuit$] and [$\blacksquare$] denote + results reported by \newcite{vu-etal-2018-vncorenlp}, \newcite{vu-xuan-etal-2019-etnlp} and \newcite{wu-dredze-2019-beto}, respectively. + %``mBiLSTM'' denotes a BiLSTM-based multilingual embedding model. + Note that there are higher Vietnamese NLI results reported for XLM-R when fine-tuning on the concatenation of all 15 training datasets from the XNLI corpus (i.e. TRANSLATE-TRAIN-ALL: 79.5\% for XLM-R\textsubscript{base} and 83.4\% XLM-R\textsubscript{large}). However, those results might not be comparable as we only use the monolingual Vietnamese training data for fine-tuning. } + \label{tab:nernli} + \end{table*} + +\section{Experimental results}\label{sec:results} + +\subsubsection*{Main results} + +Tables \ref{tab:posdep} and \ref{tab:nernli} compare PhoBERT scores with the previous highest reported results, using the same experimental setup. It is clear that our PhoBERT helps produce new SOTA performance results for all four downstream tasks. + +For \underline{POS tagging}, the neural model jointWPD for joint POS tagging and dependency parsing \citep{nguyen-2019-neural} and the feature-based model VnCoreNLP-POS \citep{nguyen-etal-2017-word} are the two previous SOTA models, obtaining accuracies at about 96.0\%. PhoBERT obtains 0.8\% absolute higher accuracy than these two models. + +For \underline{Dependency parsing}, the previous highest parsing scores LAS and UAS are obtained by the Biaffine parser at 75.0\% and 81.2\%, respectively. PhoBERT helps boost the Biaffine parser with about 4\% absolute improvement, achieving a LAS at 78.8\% and a UAS at 85.2\%. + + +For \underline{NER}, PhoBERT\textsubscript{large} produces 1.1 points higher F\textsubscript{1} than PhoBERT\textsubscript{base}. In addition, PhoBERT\textsubscript{base} obtains 2+ points higher than the previous SOTA feature- and neural network-based models VnCoreNLP-NER \citep{vu-etal-2018-vncorenlp} and BiLSTM-CNN-CRF \citep{ma-hovy-2016-end} which are trained with the set of 15K BERT-based ETNLP word embeddings \citep{vu-xuan-etal-2019-etnlp}. + + For \underline{NLI}, +PhoBERT outperforms the multilingual BERT \citep{devlin-etal-2019-bert} and the BERT-based cross-lingual model with a new translation language modeling objective XLM\textsubscript{MLM+TLM} \citep{NIPS2019_8928} by large margins. PhoBERT also performs better than the recent best pre-trained multilingual model XLM-R but using far fewer parameters than XLM-R: 135M (PhoBERT\textsubscript{base}) vs. 250M (XLM-R\textsubscript{base}); 370M (PhoBERT\textsubscript{large}) vs. 560M (XLM-R\textsubscript{large}). + + + + + + + +\subsubsection*{Discussion} + +We find that PhoBERT\textsubscript{large} achieves 0.9\% lower dependency parsing scores than PhoBERT\textsubscript{base}. One possible reason is that the last Transformer layer in the BERT architecture might not be the optimal one which encodes the richest information of syntactic structures \cite{hewitt-manning-2019-structural,jawahar-etal-2019-bert}. Future work will study which PhoBERT's Transformer layer contains richer syntactic information by evaluating the Vietnamese parsing performance from each layer. + +Using more pre-training data can significantly improve the quality of the pre-trained language models \cite{RoBERTa}. Thus it is not surprising that PhoBERT helps produce better performance than ETNLP on NER, and the multilingual BERT and XLM\textsubscript{MLM+TLM} on NLI (here, PhoBERT uses 20GB of Vietnamese texts while those models employ the 1GB Vietnamese Wikipedia corpus). + +Following the fine-tuning approach that we use for PhoBERT, we carefully fine-tune XLM-R for the remaining Vietnamese POS tagging, Dependency parsing and NER tasks (here, it is applied to the first sub-syllable token of the first syllable of each word).\footnote{For fine-tuning XLM-R, we use a grid search on the validation set to select the AdamW learning rate from \{5e-6, 1e-5, 2e-5, 4e-5\} and the batch size from \{16, 32\}.} +Tables \ref{tab:posdep} and \ref{tab:nernli} show that our PhoBERT also does better than XLM-R on these three word-level tasks. +It is worth noting that XLM-R uses a 2.5TB pre-training corpus which contains 137GB of Vietnamese texts (i.e. about 137\ /\ 20 $\approx$ 7 times bigger than our pre-training corpus). +Recall that PhoBERT performs Vietnamese word segmentation to segment syllable-level sentences into word tokens before applying BPE to segment the word-segmented sentences into subword units, while XLM-R directly applies BPE to the syllable-level Vietnamese pre-training sentences. + This reconfirms that the dedicated language-specific models still outperform the multilingual ones \citep{2019arXiv191103894M}.\footnote{Note that \newcite{2019arXiv191103894M} only compare their model CamemBERT with XLM-R on the French NLI task.} + + + + + + + + \section{Conclusion} + +In this paper, we have presented the first large-scale monolingual PhoBERT language models pre-trained for Vietnamese. We demonstrate the usefulness of PhoBERT by showing that PhoBERT performs better than the recent best multilingual model XLM-R and helps produce the SOTA performances for four downstream Vietnamese NLP tasks of POS tagging, Dependency parsing, NER and NLI. +By publicly releasing PhoBERT models, %\footnote{\url{https://github.com/VinAIResearch/PhoBERT}} +we hope that they can foster future research and applications in Vietnamese NLP. %Our PhoBERT and its usage are available at: \url{https://github.com/VinAIResearch/PhoBERT}. + +{%\footnotesize +\bibliographystyle{acl_natbib} +\bibliography{REFs} +} + + + + + +\end{document} diff --git a/references/2020.arxiv.the/paper.md b/references/2020.arxiv.the/paper.md new file mode 100644 index 0000000000000000000000000000000000000000..b37299e6afd2d187ddbee24cdc46c98a40cb68fb --- /dev/null +++ b/references/2020.arxiv.the/paper.md @@ -0,0 +1,659 @@ +--- +title: "Improving Sequence Tagging for Vietnamese Text Using Transformer-based Neural Models" +authors: + - "Viet Bui The" + - "Oanh Tran Thi" + - "Phuong Le-Hong" +year: 2020 +venue: "arXiv" +url: "https://arxiv.org/abs/2006.15994" +arxiv: "2006.15994" +--- + +\captionsenglish + +\maketitle + +\begin{abstract} + This paper describes our study on using mutilingual BERT embeddings + and some new neural models for improving sequence tagging tasks for + the Vietnamese language. We propose new model architectures and + evaluate them extensively on two named entity recognition datasets + of VLSP 2016 and VLSP 2018, and on two part-of-speech tagging + datasets of VLSP 2010 and VLSP 2013. Our proposed models outperform + existing methods and achieve new state-of-the-art results. In + particular, we have pushed the accuracy of part-of-speech + tagging to 95.40\ + 2013 corpus; and the $F_1$ score of named entity recognition to + 94.07\ + corpus. Our code and pre-trained models viBERT and vELECTRA are + released as open source to facilitate adoption and further research. +\end{abstract} + +# Introduction +\label{sec:introduction} + +Sequence modeling plays a central role in natural language +processing. Many fundamental language processing tasks can be treated +as sequence tagging problems, including part-of-speech tagging and +named-entity recognition. In this paper, we present our study on +adapting and developing the multi-lingual BERT~[Devlin:2019] and +ELECTRA~[Clark:2020] models for improving +Vietnamese part-of-speech tagging (PoS) and named entity recognition +(NER). + +Many natural language processing tasks have been shown to be greatly +benefited from large network pre-trained models. In recent years, +these pre-trained models has led to a series of breakthroughs in +language representation learning~[Radford:2018,Peters:2018,Devlin:2019,Yang:2019,Clark:2020]. Current state-of-the-art +representation learning methods for language can be divided into two +broad approaches, namely *denoising auto-encoders* and +*replaced token detection*. + +In the denoising auto-encoder approach, a small subset of tokens of +the unlabelled input sequence, typically 15\ +tokens are masked (e.g., BERT~[Devlin:2019]), or attended (e.g., +XLNet~[Yang:2019]); and then train the network to recover the +original input. The network is mostly transformer-based models which +learn bidirectional representation. The main disadvantage of these +models is that they often require a substantial compute cost because +only 15\ +corpus is usually required for the pre-trained models to be +effective. In the replaced token detection approach, the model learns +to distinguish real input tokens from plausible but synthetically +generated replacements (e.g., ELECTRA~[Clark:2020]) Instead of +masking, this method corrupts the input by replacing some tokens with +samples from a proposal distribution. The network is pre-trained as a +discriminator that predicts for every token whether it is an original +or a replacement. The main advantage of this method is that the model +can learn from all input tokens instead of just the small masked-out +subset. This is therefore much more efficient, requiring less than +$1/4$ of compute cost as compared to RoBERTa~[Liu:2019] and +XLNet~[Yang:2019]. + +Both of the approaches belong to the fine-tuning method in natural +language processing where we first pretrain a model architecture on a +language modeling objective before fine-tuning that same model for a +supervised downstream task. A major advantage of this method is that +few parameters need to be learned from scratch. + +In this paper, we propose some improvements over the recent +transformer-based models to push the state-of-the-arts of two common +sequence labeling tasks for Vietnamese. Our main contributions in this +work are: + +- We propose pre-trained language models for Vietnamese which are + based on BERT and ELECTRA architectures; the models are trained on large + corpora of 10GB and 60GB uncompressed Vietnamese text. +- We propose the fine-tuning methods by using attentional + recurrent neural networks instead of the original fine-tuning with + linear layers. This improvement helps improve the accuracy of + sequence tagging. +- Our proposed system achieves new state-of-the-art results on all + the four PoS tagging and NER tasks: achieving 95.04\ + VLSP 2010, 96.77\ + 2016, and 90.31\ +- We release code as open source to facilitate adoption and + further research, including pre-trained models viBERT and vELECTRA. + +The remainder of this paper is structured as +follows. Section~\ref{sec:models} presents the methods used in the +current work. Section~\ref{sec:experiments} describes the experimental +results. Finally, Section~\ref{sec:conclusion} concludes the papers +and outlines some directions for future work. + +# Models +\label{sec:models} + +## BERT Embeddings + +### BERT +The basic structure of BERT~[Devlin:2019] (*Bidirectional + Encoder Representations from Transformers*) is summarized on +Figure~\ref{fig:bert} where Trm are transformation and $E_k$ are +embeddings of the $k$-th token. + +\begin{figure}[t] + \centering + \begin{tikzpicture}[x=2.25cm,y=1.5cm] + \tikzstyle{every node} = [rectangle, draw, fill=gray!30] + + \node[fill=green!60] (a) at (0,4) {$T_1$}; + \node[fill=green!60] (b) at (1,4) {$T_2$}; + \node[fill=none,draw=none] (c) at (2,4) {$\cdots$}; + \node[fill=green!60] (d) at (3,4) {$T_N$}; + \node[circle,fill=blue!60] (xa) at (0,3) {Trm}; + \node[circle,fill=blue!60] (xb) at (1,3) {Trm}; + \node[draw=none,fill=none] (xc) at (2,3) {$\cdots$}; + \node[circle,fill=blue!60] (xd) at (3,3) {Trm}; + \node[circle,fill=blue!60] (ya) at (0,2) {Trm}; + \node[circle,fill=blue!60] (yb) at (1,2) {Trm}; + \node[draw=none,fill=none] (yc) at (2,2) {$\cdots$}; + \node[circle,fill=blue!60] (yd) at (3,2) {Trm}; + \node[draw,fill=yellow!60] (ea) at (0,1) {$E_1$}; + \node[draw,fill=yellow!60] (eb) at (1,1) {$E_2$}; + \node[draw=none,fill=none] (ec) at (2,1) {$\cdots$}; + \node[draw,fill=yellow!60] (ed) at (3,1) {$E_N$}; + \foreach \from/\to in {xa/a, xb/b, xc/c, xd/d, + ya/xa, ya/xb, ya/xc, ya/xd, + yb/xa, yb/xb, yb/xc, yb/xd, + yd/xa, yd/xb, yd/xc, yd/xd, + ea/ya, ea/yb, ea/yc, ea/yd, + eb/ya, eb/yb, eb/yc, eb/yd, + ed/ya, ed/yb, ed/yc, ed/yd} + \draw[->,thick] (\from) -- (\to); + \end{tikzpicture} + \caption{The basic structure of BERT} + \label{fig:bert} +\end{figure} + +In essence, BERT's model architecture is a multilayer bidirectional +Transformer encoder based on the original implementation described +in~[Vaswani:2017]. In this model, each input token of a sentence +is represented by a sum of the corresponding token embedding, its +segment embedding and its position embedding. The WordPiece embeddings +are used; split word pieces are denoted by \#\#. In our experiments, +we use learned positional embedding with supported sequence lengths up +to 256 tokens. + +The BERT model trains a deep bidirectional representation by masking +some percentage of the input tokens at random and then predicting only +those masked tokens. The final hidden vectors corresponding to the +mask tokens are fed into an output softmax over the vocabulary. We use +the whole word masking approach in this work. The masked language +model objective is a cross-entropy loss on predicting the masked +tokens. BERT uniformly selects 15\ +masking. Of the selected tokens, 80\ +are left unchanged, and 10\ +vocabulary token. + +\begin{figure*} + \centering + \begin{tikzpicture}[x=1.6cm,y=1.15cm] + \tikzstyle{every node} = [rectangle,draw,fill=gray!30] + \foreach \pos in {0,...,8} { + \node[fill=none,draw=none] (a\pos) at (1*\pos,5) {$\cdots$}; + } + \node (a1) at (1,5) {`Np`}; + \node (a3) at (3,5) {`V`}; + + + + + + + \foreach \pos in {0,...,8} { + \matt{m\pos}{\pos}{2.5}{Att}; + } + \foreach \pos in {0,...,8} { + \draw[->,thick] (m\pos) -- (a\pos); + } + \node (c0) at (0,4) {}; + \node (c8) at (8,4) {}; + \node[fill=red!60,align=center,fit={(c0) (c8)}] {}; + \node[fill=none,draw=none] at (4,4) {Linear}; + + \foreach \pos in {0,...,8} { + \node[double,fill=yellow!30] (r\pos) at (\pos,1.2) {RNN}; + } + \foreach \pos in {0,...,7} { + \draw[<->,thick] (r\pos) -- (r\the\numexpr\pos+1\relax); + } + + \node (t0) at (0,-0.8) {`[CLS]`}; + \node (t1) at (1,-0.8) {`Đ`}; + \node (t2) at (2,-0.8) {`\#\#ông`}; + \node (t3) at (3,-0.8) {`gi`}; + \node (t4) at (4,-0.8) {`\#\#ới`}; + \node (t5) at (5,-0.8) {`th`}; + \node (t6) at (6,-0.8) {`\#\#iệ`}; + \node (t7) at (7,-0.8) {`\#\#u`}; + \node (t8) at (8,-0.8) {`[SEP]`}; + \foreach \pos in {0,...,8} { + \draw[->,thick] (t\pos) -- (r\pos); + \draw[->,thick] (r\pos) -- (m\pos); + } + \node (B0) at (0,0.2) {}; + \node (B8) at (8,0.2) {}; + \node[fill=yellow!60,align=center,fit={(B0) (B8)}] {}; + \node[fill=none,draw=none] at (4,0.2) {BERT}; + \end{tikzpicture} + \caption{Our proposed end-to-end architecture} + \label{fig:arch} +\end{figure*} + +In our experiment, we start with the open-source mBERT +package\footnote{https://github.com/google-research/bert/blob/master/multilingual.md}. We +keep the standard hyper-parameters of 12 layers, 768 hidden units, and +12 heads. The model is optimized with Adam~[Kingma:2015] using +the following parameters: $\beta_1 = 0.9, \beta_2 = 0.999$, $\epsilon += 1e-6$ and $L_2$ weight decay of $0.01$. + +The output of BERT is computed as follows~[Peters:2018]: +\begin{equation*} + B_k = \gamma \left( w_0 E_k + \sum_{k=1}^m w_i h_{ki} \right), +\end{equation*} +where + +- $B_k$ is the BERT output of $k$-th token; +- $E_k$ is the embedding of $k$-th token; +- $m$ is the number of hidden layers of BERT; +- $h_{ki}$ is the $i$-th hidden state of of $k$-th token; +- $\gamma, w_0, w_1,\dots,w_m$ are trainable parameters. + +### Proposed Architecture + +Our proposed architecture contains five main layers as follows: + +- The input layer encodes a sequence of tokens which are + substrings of the input sentence, including ignored indices, padding + and separators; +- A BERT layer; +- A bidirectional RNN layer with either LSTM or GRU units; +- An attention layer; +- A linear layer; + +A schematic view of our model architecture is shown in +Figure~\ref{fig:arch}. + +## ELECTRA + +\begin{figure*} + \centering + \begin{tikzpicture}[x=1.75cm,y=0.8cm] + \tikzstyle{every node} = [fill=gray!30,text width=1.2cm] + + \node (m1) at (0,4) {phi}; + \node (m2) at (0,3) {công}; + \node (m3) at (0,2) {điều}; + \node (m4) at (0,1) {khiển}; + \node (m5) at (0,0) {máy}; + \node (m6) at (0,-1) {bay}; + + \node (n1) at (1,4) {phi}; + \node[fill=green!60] (n2) at (1,3) {MASK}; + \node (n3) at (1,2) {điều}; + \node (n4) at (1,1) {khiển}; + \node[fill=green!60] (n5) at (1,0) {MASK}; + \node (n6) at (1,-1) {bay}; + + \node (p1) at (4,4) {phi}; + \node[fill=blue!60] (p2) at (4,3) {công}; + \node (p3) at (4,2) {điều}; + \node (p4) at (4,1) {khiển}; + \node[fill=red!60] (p5) at (4,0) {sân}; + \node (p6) at (4,-1) {bay}; + + \foreach \from/\to in { + m1/n1, m2/n2, m3/n3, m4/n4, m5/n5, m6/n6, n1/p1, n2/p2, n3/p3, + n4/p4, n5/p5, n6/p6} + \draw[->,thick] (\from) -- (\to); + + \draw[draw=black,fill=white] (1.75,4.5) rectangle ++(1.5,-6); + \node[fill=none] (g) at (2.25,2) + {**\begin{tabular**{c}Generator\\(BERT)\end{tabular}}}; + + \node[fill=none] (q1) at (7,4) {original}; + \node[fill=blue!60] (q2) at (7,3) {original}; + \node[fill=none] (q3) at (7,2) {original}; + \node[fill=none] (q4) at (7,1) {original}; + \node[fill=red!60] (q5) at (7,0) {replaced}; + \node[fill=none] (q6) at (7,-1) {original}; + + \foreach \from/\to in { + p1/q1, p2/q2, p3/q3, p4/q4, p5/q5, p6/q6} + \draw[->,thick] (\from) -- (\to); + + \draw[draw=black,fill=white] (4.75,4.5) rectangle ++(1.5,-6); + \node[fill=none] (d) at (5.1,2) + {**\begin{tabular**{c}Discriminator\\(ELECTRA)\end{tabular}}}; + + \end{tikzpicture} + \caption{An overview of replaced token detection by the ELECTRA + model on a sample drawn from vELECTRA} + \label{fig:electra} +\end{figure*} + +ELECTRA~[Clark:2020] is currently the latest development of BERT-based model +where a more sample-efficient pre-training method is used. This method +is called replaced token detection. In this method, two neural +networks, a generator $G$ and a discriminator $D$, are trained +simultaneously. Each one consists of a Transformer network (an +encoder) that maps a sequence of input tokens $\vec x = [x_1, +x_2,\dots,x_n]$ into a sequence of contextualized vectors $h(\vec x) = +[h_1, h_2,\dots, h_n]$. For a given position $t$ where $x_t$ is the +masked token, the generator outputs a probability for generating a +particular token $x_t$ with a softmax distribution: +\begin{equation*} + p_G(x_t|\vec x) = \frac{\exp(x_t^\top h_G(\vec x)_t) }{\sum_{u} + \exp(u_t^\top h_G(\vec x)_t)}. +\end{equation*} +For a given position $t$, the discriminator predicts whether the +token $x_t$ is ``real'', i.e., that it comes from the data rather than the generator distribution, with a +sigmoid function: +\begin{equation*} +D(\vec x, t) = \sigma \left (w^\top h_D(\vec x)_t \right ) +\end{equation*} + +An overview of the replaced token detection in the ELECTRA model is +shown in Figure~\ref{fig:electra}. The generator is a BERT model which +is trained jointly with the discriminator. The Vietnamese example is a +real one which is sampled from our training corpus. + +# Experiments +\label{sec:experiments} + +## Experimental Settings + +### Model Training +To train the proposed models, we use a CPU (Intel Xeon E5-2699 v4 +@2.20GHz) and a GPU (NVIDIA GeForce GTX 1080 Ti 11G). The +hyper-parameters that we chose are as follows: maximum sequence length +is 256, BERT learning rate is $2E-05$, learning rate is $1E-3$, number +of epochs is 100, batch size is 16, use apex and BERT weight decay is +set to 0, the Adam rate is $1E-08$. The configuration of our model is +as follows: number of RNN hidden units is 256, one RNN layer, +attention hidden dimension is 64, number of attention heads is 3 and a +dropout rate of 0.5. + +To build the pre-training language model, it is very important to have +a good and big dataset. This dataset was collected from online +newspapers\footnote{vnexpress.net, dantri.com.vn, baomoi.com, + zingnews.vn, vitalk.vn, etc.} in Vietnamese. To clean the data, we +perform the following pre-processing steps: + + - Remove duplicated news + - Only accept valid letters in Vietnamese + - Remove too short sentences (less than 4 words) + +We obtained approximately 10GB of texts after collection. This dataset was + used to further pre-train the mBERT to build our viBERT which better + represents Vietnamese texts. About the vocab, we removed insufficient + vocab from mBERT because its vocab contains ones for other + languages. This was done by keeping only vocabs existed in the + dataset. + + In pre-training vELECTRA, we collect more data from two sources: + + - NewsCorpus: 27.4 + GB\footnote{https://github.com/binhvq/news-corpus} + - OscarCorpus: 31.0 + GB\footnote{https://traces1.inria.fr/oscar/} + + +Totally, with more than 60GB of texts, we start training different +versions of vELECTRA. It is worth noting that pre-training viBERT +is much slower than pre-training vELECTRA. For this reason, we +pre-trained viBERT on the 10GB corpus rather than on the large 60GB +corpus. + +\begin{table*} + \centering + \begin{tabular}{|l | l | c | c | c | c | c| c | } + \hline + **No.**&\multicolumn{3}{c|}{**VLSP 2010**}&\multicolumn{4}{c|}{**VLSP 2013**}\\ \hline + +\multicolumn{8}{|l|}{**Existing models**} \\ \hline +1. & \multicolumn{2}{|l|}{MEM~[Le:2010]}& 93.4 & \multicolumn{3}{|l|}{RDRPOSTagger~[Nguyen:2014]} & 95.1 \\ \hline +2. & \multicolumn{3}{c}{} & \multicolumn{3}{|l|}{BiLSTM-CNN-CRF~[Ma:2016]} & 95.4 \\ \hline +3. & \multicolumn{3}{c}{} & \multicolumn{3}{|l|}{VnCoreNLP-POS~[Nguyen:2017]} & 95.9 \\ \hline +4. & \multicolumn{3}{c}{} & \multicolumn{3}{|l|}{jointWPD~[Nguyen:2019]} & 96.0 \\ \hline +5. & \multicolumn{3}{c}{} & \multicolumn{3}{|l|}{PhoBERT\_base~[Nguyen:2020]} & 96.7 \\ \hline + +\hline +\multicolumn{8}{|l|}{**Proposed models**} \\ \hline + & **Model Name** & **mBERT** & **viBERT** & **vELEC** & **mBERT** & **viBERT** & **vELEC**\\ + \hline +1.&+Fine-Tune & 94.34 & 95.07 & 95.35 & 96.35 & 96.60 & 96.62 \\ \hline +2.&+BiLSTM& 94.34 & 95.12 & 95.32 & 96.38 & 96.63 & **96.77** \\ \hline +3.&+BiGRU& 94.37 & 95.13 & 95.37 & 96.45 & 96.68& 96.73\\ \hline +4.&+BiLSTM\_Attn& 94.37 & 95.12 & **95.40** & 96.36 & 96.61 & 96.61 \\\hline +5.&+BiGRU\_Attn& 94.41 & 95.13 & 95.35 & 96.33 & 96.56 & 96.55 \\ \hline + \end{tabular} + \caption{Performance of our proposed models on the POS tagging task} + \label{tab:result-POS} +\end{table*} + +### Testing and evaluation methods +In performing experiments, for datasets without development sets, we +randomly selected 10\ + +To evaluate the effectiveness of the models, we use the commonly-used +metrics which are proposed by the organizers of VLSP. Specifically, we +measure the accuracy score on the POS tagging task which is calculated +as follows: +\begin{equation*} + Acc = \frac{\#of\_words\_correcly\_tagged}{\#of\_words\_in\_the\_test\_set} +\end{equation*} + +and the $F_1$ score on the NER task using the following equations: +\begin{equation*} + F_1 = 2*\frac{Pre*Rec}{Pre+Rec} +\end{equation*} +where *Pre* and *Rec* are determined as follows: +\begin{equation*} + Pre = \frac{NE\_true}{NE\_sys} +\end{equation*} + +\begin{equation*} + Rec = \frac{NE\_true}{NE\_ref} +\end{equation*} + +where *NE\_ref* is the number of NEs in gold data, +*NE\_sys* is the number of NEs in recognizing system, and +*NE\_true* is the number of NEs which is correctly recognized +by the system. + +## Experimental Results + +### On the PoS Tagging Task + +Table~\ref{tab:result-POS} shows experimental results using different +proposed architectures on the top of mBERT and +viBERT and vELECTRA on two benchmark datasets from +the campaign VLSP 2010 and VLSP 2013. + +As can be seen that, with further pre-training techniques on a +Vietnamese dataset, we could significantly improve the performance of +the model. On the dataset of VLSP 2010, both viBERT and +vELECTRA significantly improved the performance by about 1\ +in the $F_1$ scores. On the dataset of VLSP 2013, these two models +slightly improved the performance. + +From the table, we can also see the performance of different +architectures including fine-tuning, BiLSTM, biGRU, and their +combination with attention mechanisms. Fine-tuning mBERT with +linear functions in several epochs could produce nearly state-of-the-art +results. It is also shown that building different architectures on top +slightly improve the performance of all mBERT, +viBERT and vELECTRA models. On the VLSP 2010, we got +the accuracy of 95.40\ +vELECTRA. On the VLSP 2013 dataset, we got 96.77\ +accuracy scores using only biLSTM on top of vELECTRA. + +In comparison to previous work, our proposed model - vELECTRA +- outperformed previous ones. It achieved from 1\ +existing work using different innovation in deep learning such as CNN, +LSTM, and joint learning techniques. Moreover, vELECTRA also +gained a slightly better than PhoBERT\_base, the same +pre-training language model released so far, by nearly 0.1\ +accuracy score. + +\begin{table*} + \centering + \begin{tabular}{|l | l | c | c | c | c |c | c | } + \hline + **No.**& \multicolumn{4}{c|}{**VLSP 2016**}&\multicolumn{3}{c|}{**VLSP 2018**}\\ \hline +\multicolumn{8}{|l|}{**Existing models**} \\ \hline + + 1. & \multicolumn{3}{|l|}{TRE+BI~[Le:2016]} & 87.98 & \multicolumn{2}{|l|}{VietNER} & 76.63 \\ \hline + +2. & \multicolumn{3}{|l|}{BiLSTM\_CNN\_CRF~[Pham:2017a]} & 88.59 & \multicolumn{2}{|l|}{ZA-NER} & 74.70 \\ \hline + 3. & \multicolumn{3}{|l|}{BiLSTM~[Pham:2017c]} & 92.02 & \multicolumn{3}{|l|}{} \\ \hline + 4. & \multicolumn{3}{|l|}{NNVLP~[Pham:2017b]} & 92.91 & \multicolumn{3}{|l|}{} \\ \hline + + +5. & \multicolumn{3}{|l|}{VnCoreNLP-NER~[Vu:2018]} & 88.6 & \multicolumn{3}{|l|}{} \\ \hline +6. & \multicolumn{3}{|l|}{VNER~[Nguyen:2019]} & 89.6 & \multicolumn{3}{|l|}{}\\ \hline +7. & \multicolumn{3}{|l|}{ETNLP~[Vu:2019]} & 91.1 & \multicolumn{3}{|l|}{} \\ \hline +8. & \multicolumn{3}{|l|}{PhoBERT\_base~[Nguyen:2020]} & 93.6 & \multicolumn{3}{|l|}{} \\ \hline + +\multicolumn{8}{|l|}{**Proposed models**} \\ \hline + &**Model Name**& **mBERT** & **viBERT** & **VELEC** & **mBERT** & **viBERT** & **VELEC** \\ + \hline +1.&+Fine-Tune & 91.28 & 92.84 & 94.00 & 86.86 & 88.04 & 89.79 \\ \hline +2.&+BiLSTM& 91.03 & 93.00 & 93.70 & 86.62 & 88.68 & 89.92 \\ \hline +3.&+BiGRU& 91.52 & 93.44 & 93.93 & 86.72 & 88.98 & **90.31**\\ \hline +4.&+BiLSTM\_Attn& 91.23 & 92.97 & **94.07** & 87.12& 89.12 & 90.26 \\\hline +5.&+BiGRU\_Attn& 90.91 & 93.32 & 93.27 & 86.33 & 88.59 &89.94 \\ \hline + \end{tabular} + \caption{Performance of our proposed models on the NER task. ZA-NER~[Luong:2018] + is the best system of VLSP 2018~[Nguyen:2018]. VietNER is + from~[NguyenKA:2019]} + \label{tab:result-NER} +\end{table*} + +### On the NER Task + +Table~\ref{tab:result-NER} shows experimental results using different +proposed architectures on the top of mBERT, viBERT +and vELECTRA on two benchmark datasets from the campaign +VLSP 2016 and VLSP 2018. + +These results once again gave a strong evidence to the above statement +that further training mBERT on a small raw dataset could +significantly improve the performance of transformation-based language +models on downstream tasks. Training vELECTRA from scratch on +a big Vietnamese dataset could further enhance the performance. On two +datasets, vELECTRA improve the $F_1$ score by from 1\ +comparison to viBERT and mBERT. + +Looking at the performance of different architectures on top of these +pre-trained models, we acknowledged that biLSTM with attention once a +gain yielded the SOTA result on VLSP 2016 dataset. On VLSP 2018 +dataset, the architecture of biGRU yielded the best performance at +90.31\ + +Comparing to previous work, the best proposed model outperformed all +work by a large margin on both datasets. + +\begin{figure*} + \centering + \begin{tikzpicture} + \begin{axis}[ + height=5.8cm, + width=\textwidth, + ybar, + bar width=10, + enlarge y limits=0.25, + legend style={at={(0.5,0.85)},anchor=south,legend columns=-1}, + xlabel={\textit{}}, + ylabel={*milliseconds per sentence*}, + symbolic x coords={+Fine-Tune,+biLSTM,+biGRU,+biLSTM-Att,+biGRU-Att}, + xtick=data, + nodes near coords, + every node near coord/.append style={font=\tiny,rotate=90,anchor=east}, + nodes near coords={\pgfmathprintnumber[precision=3]{\pgfplotspointmeta}}, + nodes near coords align={vertical}, + ] + \addplot coordinates {(+Fine-Tune,1.8) (+biLSTM,2.8) (+biGRU,3.1) (+biLSTM-Att, 2.9) (+biGRU-Att, 3.3)}; + \addplot coordinates {(+Fine-Tune,2.9) (+biLSTM,2.8) (+biGRU,2.7) (+biLSTM-Att, 2.9) (+biGRU-Att, 2.9)}; + \addplot coordinates {(+Fine-Tune,1.6) (+biLSTM,2.4) (+biGRU,2.6) (+biLSTM-Att, 1.8) (+biGRU-Att, 2.4)}; + \legend{mBERT, viBERT, vELECTRA} + \end{axis} + \end{tikzpicture} + \caption{Decoding time on PoS task -- VLSP 2013} + \label{fig:vlsp2013} +\end{figure*} + +## Decoding Time + +Figure \ref{fig:vlsp2013} and \ref{fig:vlsp2016} shows the averaged +decoding time measured on one sentence. According to our statistics, +the averaged length of one sentence in VLSP 2013 and VLSP 2016 +datasets are 22.55 and 21.87 words, respectively. + +For the POS tagging task measured on VLSP 2013 dataset, among three +models, the fastest decoding time is of vELECTRA model, followed by +viBERT model, and finally by mBERT model. This statement holds for +four proposed architectures on top of these three models. However, for +the fine-tuning technique, the decoding time of mBERT is faster than +that of viBERT. + +For the NER task measured on the VLSP 2016 dataset, among three models, +the slowest time is of viBERT model with more than 2 +milliseconds per sentence. The decoding times on mBERT topped with +simple fine-tuning techniques, or biGRU, or biLSTM-attention is a +little bit faster than on vELECTRA with the same architecture. + +This experiment shows that our proposed models are of practical +use. In fact, they are currently deployed as a core component of our +commercial chatbot engine FPT.AI\footnote{http://fpt.ai/} which +is serving effectively many customers. More precisely, the FPT.AI +platform has been used by about 70 large enterprises, and of over 27,000 +frequent developers, serving more than 30 million end +users.\footnote{These numbers are reported as of August, 2020.} + +# Conclusion +\label{sec:conclusion} + +This paper presents some new model architectures for sequence tagging +and our experimental results for Vietnamese part-of-speech tagging and +named entity recognition. Our proposed model vELECTRA outperforms +previous ones. For part-of-speech tagging, it improves about 2\ +absolute point in comparison with existing work which use different +innovation in deep learning such as CNN, LSTM, or joint learning +techniques. For named entity recognition, the vELECTRA outperforms +all previous work by a large margin on both VLSP 2016 and VLSP 2018 +datasets. + +Our code and pre-trained models are published as an open source +project for facilitate adoption and further research in the Vietnamese +language processing community.\footnote{viBERT is available at + https://github.com/fpt-corp/viBERT and vELECTRA is available + at https://github.com/fpt-corp/vELECTRA.} An online service of +the models for demonstration is also accessible at +https://fpt.ai/nlp/bert/. A variant and more advanced version +of this model is currently deployed as a core component of our +commercial chatbot engine FPT.AI which is serving effectively +millions of end users. In particular, these models are being +fine-tuned to improve task-oriented dialogue in mixed and multiple +domains~[Luong:2019] and dependency parsing~[Le:2015c]. + +\begin{figure*} + \centering + \begin{tikzpicture} + \begin{axis}[ + height=5.8cm, + width=\textwidth, + ybar, + bar width=10, + + enlarge y limits=0.25, + legend style={at={(0.5,0.85)},anchor=south,legend columns=-1}, + xlabel={\textit{}}, + ylabel={*milliseconds per sentence*}, + symbolic x coords={+Fine-Tune,+biLSTM,+biGRU,+biLSTM-Att,+biGRU-Att}, + xtick=data, + nodes near coords, + every node near coord/.append style={font=\tiny,rotate=90,anchor=east}, + nodes near coords={\pgfmathprintnumber[precision=3]{\pgfplotspointmeta}}, + nodes near coords align={vertical}, + ] + \addplot coordinates {(+Fine-Tune,1.1) (+biLSTM,1.7) (+biGRU,1.4) (+biLSTM-Att, 1.8) (+biGRU-Att, 2.0)}; + \addplot coordinates {(+Fine-Tune,1.9) (+biLSTM,2.1) (+biGRU,2.2) (+biLSTM-Att, 2.3) (+biGRU-Att, 2.2)}; + \addplot coordinates {(+Fine-Tune,1.5) (+biLSTM,1.7) (+biGRU,1.6) (+biLSTM-Att, 2.0) (+biGRU-Att, 1.6)}; + \legend{mBERT, viBERT, vELECTRA} + \end{axis} + \end{tikzpicture} + \caption{Decoding time on NER task -- VLSP 2016} + \label{fig:vlsp2016} +\end{figure*} + +# Acknowledgement + +We thank three anonymous reviewers for their valuable comments for +improving our manuscript. + +\bibliographystyle{acl} +\bibliography{references} \ No newline at end of file diff --git a/references/2020.arxiv.the/paper.pdf b/references/2020.arxiv.the/paper.pdf new file mode 100644 index 0000000000000000000000000000000000000000..3761e2c3ce9acea26d023a1153dab3ce6fa00c4d --- /dev/null +++ b/references/2020.arxiv.the/paper.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8a8c9cf9a51fec26872baceabe6ac6a5393069002d0513503034d7156dac7090 +size 118633 diff --git a/references/2020.arxiv.the/paper.tex b/references/2020.arxiv.the/paper.tex new file mode 100644 index 0000000000000000000000000000000000000000..9c99adfa92fdf0e170204f22e10e11c77152f348 --- /dev/null +++ b/references/2020.arxiv.the/paper.tex @@ -0,0 +1,740 @@ +\documentclass[11pt]{article} +\usepackage{paclic34} +\usepackage{times} +\usepackage{latexsym} +\usepackage{amsmath} +\usepackage{multirow} +\usepackage{url} + +\setlength\titlebox{6.5cm} % Expanding the titlebox + +\usepackage{graphicx} +\usepackage[utf8]{vietnam} +\usepackage{tikz} +\usepackage{pgfplots} +\usetikzlibrary{fit} +\usetikzlibrary{arrows,shapes} +\usetikzlibrary{shapes.misc} + + +\title{Improving Sequence Tagging for Vietnamese Text using + Transformer-based Neural Models} + +\author{ + The Viet Bui$^{1}$\\ + {\tt vietbt6@fpt.com.vn} \\ \And + Thi Oanh Tran$^{1,2}$\\ + {\tt oanhtt@isvnu.vn} \\ \AND + Phuong Le-Hong$^{1,2}$\\ + {\tt phuonglh@vnu.edu.vn}\\ \\ + $^1$ FPT Technology Research Institute, + FPT University, Hanoi, Vietnam\\ + $^2$ Vietnam National University, Hanoi, Vietnam\\ +} + +\date{} + +\DeclareMathOperator*{\argmax}{arg\,max} +\DeclareMathOperator*{\argmin}{arg\,min} +\DeclareMathOperator{\yy}{\mathbf y} +\DeclareMathOperator{\oo}{\mathbf o} +\DeclareMathOperator{\xx}{\mathbf x} +\DeclareMathOperator{\hh}{\mathbf h} +\DeclareMathOperator{\RR}{\mathbb R} +\DeclareMathOperator{\EE}{\mathbb E} + +% database icon in Tikz +\usetikzlibrary{shapes.geometric} +\usetikzlibrary{positioning, shadows} + +\usetikzlibrary{calc} + +\newcommand{\matt}[4] +{ + \node[draw, minimum height=4em, minimum width=3em, + fill=purple!30, double copy shadow={shadow xshift=3pt,shadow yshift=3pt, draw} + ] (#1) at (#2,#3) {#4}; + +} + +%\usepackage{multirow} + +\begin{document} + +\captionsenglish + +\maketitle + +\begin{abstract} + This paper describes our study on using mutilingual BERT embeddings + and some new neural models for improving sequence tagging tasks for + the Vietnamese language. We propose new model architectures and + evaluate them extensively on two named entity recognition datasets + of VLSP 2016 and VLSP 2018, and on two part-of-speech tagging + datasets of VLSP 2010 and VLSP 2013. Our proposed models outperform + existing methods and achieve new state-of-the-art results. In + particular, we have pushed the accuracy of part-of-speech + tagging to 95.40\% on the VLSP 2010 corpus, to 96.77\% on the VLSP + 2013 corpus; and the $F_1$ score of named entity recognition to + 94.07\% on the VLSP 2016 corpus, to 90.31\% on the VLSP 2018 + corpus. Our code and pre-trained models viBERT and vELECTRA are + released as open source to facilitate adoption and further research. +\end{abstract} + +\section{Introduction} +\label{sec:introduction} + +Sequence modeling plays a central role in natural language +processing. Many fundamental language processing tasks can be treated +as sequence tagging problems, including part-of-speech tagging and +named-entity recognition. In this paper, we present our study on +adapting and developing the multi-lingual BERT~\cite{Devlin:2019} and +ELECTRA~\cite{Clark:2020} models for improving +Vietnamese part-of-speech tagging (PoS) and named entity recognition +(NER). + +Many natural language processing tasks have been shown to be greatly +benefited from large network pre-trained models. In recent years, +these pre-trained models has led to a series of breakthroughs in +language representation learning~\cite{Radford:2018,Peters:2018,Devlin:2019,Yang:2019,Clark:2020}. Current state-of-the-art +representation learning methods for language can be divided into two +broad approaches, namely \textit{denoising auto-encoders} and +\textit{replaced token detection}. + +In the denoising auto-encoder approach, a small subset of tokens of +the unlabelled input sequence, typically 15\%, is selected; these +tokens are masked (e.g., BERT~\cite{Devlin:2019}), or attended (e.g., +XLNet~\cite{Yang:2019}); and then train the network to recover the +original input. The network is mostly transformer-based models which +learn bidirectional representation. The main disadvantage of these +models is that they often require a substantial compute cost because +only 15\% of the tokens per example is learned while a very large +corpus is usually required for the pre-trained models to be +effective. In the replaced token detection approach, the model learns +to distinguish real input tokens from plausible but synthetically +generated replacements (e.g., ELECTRA~\cite{Clark:2020}) Instead of +masking, this method corrupts the input by replacing some tokens with +samples from a proposal distribution. The network is pre-trained as a +discriminator that predicts for every token whether it is an original +or a replacement. The main advantage of this method is that the model +can learn from all input tokens instead of just the small masked-out +subset. This is therefore much more efficient, requiring less than +$1/4$ of compute cost as compared to RoBERTa~\cite{Liu:2019} and +XLNet~\cite{Yang:2019}. + +Both of the approaches belong to the fine-tuning method in natural +language processing where we first pretrain a model architecture on a +language modeling objective before fine-tuning that same model for a +supervised downstream task. A major advantage of this method is that +few parameters need to be learned from scratch. + +In this paper, we propose some improvements over the recent +transformer-based models to push the state-of-the-arts of two common +sequence labeling tasks for Vietnamese. Our main contributions in this +work are: +\begin{itemize} +\item We propose pre-trained language models for Vietnamese which are + based on BERT and ELECTRA architectures; the models are trained on large + corpora of 10GB and 60GB uncompressed Vietnamese text. +\item We propose the fine-tuning methods by using attentional + recurrent neural networks instead of the original fine-tuning with + linear layers. This improvement helps improve the accuracy of + sequence tagging. +\item Our proposed system achieves new state-of-the-art results on all + the four PoS tagging and NER tasks: achieving 95.04\% of accuracy on + VLSP 2010, 96.77\% of accuracy on VLSP 2013, 94.07\% of $F_1 $ score on NER + 2016, and 90.31\% of $F_1$ score on NER 2018. +\item We release code as open source to facilitate adoption and + further research, including pre-trained models viBERT and vELECTRA. +\end{itemize} + +The remainder of this paper is structured as +follows. Section~\ref{sec:models} presents the methods used in the +current work. Section~\ref{sec:experiments} describes the experimental +results. Finally, Section~\ref{sec:conclusion} concludes the papers +and outlines some directions for future work. + +\section{Models} +\label{sec:models} + +\subsection{BERT Embeddings} + +\subsubsection{BERT} +The basic structure of BERT~\cite{Devlin:2019} (\textit{Bidirectional + Encoder Representations from Transformers}) is summarized on +Figure~\ref{fig:bert} where Trm are transformation and $E_k$ are +embeddings of the $k$-th token. + +\begin{figure}[t] + \centering + \begin{tikzpicture}[x=2.25cm,y=1.5cm] + \tikzstyle{every node} = [rectangle, draw, fill=gray!30] + % (a) + \node[fill=green!60] (a) at (0,4) {$T_1$}; + \node[fill=green!60] (b) at (1,4) {$T_2$}; + \node[fill=none,draw=none] (c) at (2,4) {$\cdots$}; + \node[fill=green!60] (d) at (3,4) {$T_N$}; + \node[circle,fill=blue!60] (xa) at (0,3) {Trm}; + \node[circle,fill=blue!60] (xb) at (1,3) {Trm}; + \node[draw=none,fill=none] (xc) at (2,3) {$\cdots$}; + \node[circle,fill=blue!60] (xd) at (3,3) {Trm}; + \node[circle,fill=blue!60] (ya) at (0,2) {Trm}; + \node[circle,fill=blue!60] (yb) at (1,2) {Trm}; + \node[draw=none,fill=none] (yc) at (2,2) {$\cdots$}; + \node[circle,fill=blue!60] (yd) at (3,2) {Trm}; + \node[draw,fill=yellow!60] (ea) at (0,1) {$E_1$}; + \node[draw,fill=yellow!60] (eb) at (1,1) {$E_2$}; + \node[draw=none,fill=none] (ec) at (2,1) {$\cdots$}; + \node[draw,fill=yellow!60] (ed) at (3,1) {$E_N$}; + \foreach \from/\to in {xa/a, xb/b, xc/c, xd/d, + ya/xa, ya/xb, ya/xc, ya/xd, + yb/xa, yb/xb, yb/xc, yb/xd, + yd/xa, yd/xb, yd/xc, yd/xd, + ea/ya, ea/yb, ea/yc, ea/yd, + eb/ya, eb/yb, eb/yc, eb/yd, + ed/ya, ed/yb, ed/yc, ed/yd} + \draw[->,thick] (\from) -- (\to); + \end{tikzpicture} + \caption{The basic structure of BERT} + \label{fig:bert} +\end{figure} + +In essence, BERT's model architecture is a multilayer bidirectional +Transformer encoder based on the original implementation described +in~\cite{Vaswani:2017}. In this model, each input token of a sentence +is represented by a sum of the corresponding token embedding, its +segment embedding and its position embedding. The WordPiece embeddings +are used; split word pieces are denoted by \#\#. In our experiments, +we use learned positional embedding with supported sequence lengths up +to 256 tokens. + +The BERT model trains a deep bidirectional representation by masking +some percentage of the input tokens at random and then predicting only +those masked tokens. The final hidden vectors corresponding to the +mask tokens are fed into an output softmax over the vocabulary. We use +the whole word masking approach in this work. The masked language +model objective is a cross-entropy loss on predicting the masked +tokens. BERT uniformly selects 15\% of the input tokens for +masking. Of the selected tokens, 80\% are replaced with [MASK], 10\% +are left unchanged, and 10\% are replaced by a randomly selected +vocabulary token. + +\begin{figure*} + \centering + \begin{tikzpicture}[x=1.6cm,y=1.15cm] + \tikzstyle{every node} = [rectangle,draw,fill=gray!30] + \foreach \pos in {0,...,8} { + \node[fill=none,draw=none] (a\pos) at (1*\pos,5) {$\cdots$}; + } + \node (a1) at (1,5) {\texttt{Np}}; + \node (a3) at (3,5) {\texttt{V}}; + % \foreach \pos in {0,...,8} { + % \node[fill=blue!30] (b\pos) at (1*\pos,4) {Linear}; + % } + % \foreach \pos in {0,...,8} { + % \draw[->,thick] (b\pos) -- (a\pos); + % } + \foreach \pos in {0,...,8} { + \matt{m\pos}{\pos}{2.5}{Att}; + } + \foreach \pos in {0,...,8} { + \draw[->,thick] (m\pos) -- (a\pos); + } + \node (c0) at (0,4) {}; + \node (c8) at (8,4) {}; + \node[fill=red!60,align=center,fit={(c0) (c8)}] {}; + \node[fill=none,draw=none] at (4,4) {Linear}; + + \foreach \pos in {0,...,8} { + \node[double,fill=yellow!30] (r\pos) at (\pos,1.2) {RNN}; + } + \foreach \pos in {0,...,7} { + \draw[<->,thick] (r\pos) -- (r\the\numexpr\pos+1\relax); + } + + \node (t0) at (0,-0.8) {\texttt{[CLS]}}; + \node (t1) at (1,-0.8) {\texttt{Đ}}; + \node (t2) at (2,-0.8) {\texttt{\#\#ông}}; + \node (t3) at (3,-0.8) {\texttt{gi}}; + \node (t4) at (4,-0.8) {\texttt{\#\#ới}}; + \node (t5) at (5,-0.8) {\texttt{th}}; + \node (t6) at (6,-0.8) {\texttt{\#\#iệ}}; + \node (t7) at (7,-0.8) {\texttt{\#\#u}}; + \node (t8) at (8,-0.8) {\texttt{[SEP]}}; + \foreach \pos in {0,...,8} { + \draw[->,thick] (t\pos) -- (r\pos); + \draw[->,thick] (r\pos) -- (m\pos); + } + \node (B0) at (0,0.2) {}; + \node (B8) at (8,0.2) {}; + \node[fill=yellow!60,align=center,fit={(B0) (B8)}] {}; + \node[fill=none,draw=none] at (4,0.2) {BERT}; + \end{tikzpicture} + \caption{Our proposed end-to-end architecture} + \label{fig:arch} +\end{figure*} + +In our experiment, we start with the open-source mBERT +package\footnote{\url{https://github.com/google-research/bert/blob/master/multilingual.md}}. We +keep the standard hyper-parameters of 12 layers, 768 hidden units, and +12 heads. The model is optimized with Adam~\cite{Kingma:2015} using +the following parameters: $\beta_1 = 0.9, \beta_2 = 0.999$, $\epsilon += 1e-6$ and $L_2$ weight decay of $0.01$. + +The output of BERT is computed as follows~\cite{Peters:2018}: +\begin{equation*} + B_k = \gamma \left( w_0 E_k + \sum_{k=1}^m w_i h_{ki} \right), +\end{equation*} +where +\begin{itemize} +\item $B_k$ is the BERT output of $k$-th token; +\item $E_k$ is the embedding of $k$-th token; +\item $m$ is the number of hidden layers of BERT; +\item $h_{ki}$ is the $i$-th hidden state of of $k$-th token; +\item $\gamma, w_0, w_1,\dots,w_m$ are trainable parameters. +\end{itemize} + +\subsubsection{Proposed Architecture} + +Our proposed architecture contains five main layers as follows: +\begin{enumerate} +\item The input layer encodes a sequence of tokens which are + substrings of the input sentence, including ignored indices, padding + and separators; +\item A BERT layer; +\item A bidirectional RNN layer with either LSTM or GRU units; +\item An attention layer; +\item A linear layer; +% \item a Conditional Random Field (CRF) layer which supports ignored +% indices, which means that the ignored labels are not acummulated into +% the loss function of the end-to-end model; +\end{enumerate} + +A schematic view of our model architecture is shown in +Figure~\ref{fig:arch}. + +\subsection{ELECTRA} + +\begin{figure*} + \centering + \begin{tikzpicture}[x=1.75cm,y=0.8cm] + \tikzstyle{every node} = [fill=gray!30,text width=1.2cm] + % (a) + \node (m1) at (0,4) {phi}; + \node (m2) at (0,3) {công}; + \node (m3) at (0,2) {điều}; + \node (m4) at (0,1) {khiển}; + \node (m5) at (0,0) {máy}; + \node (m6) at (0,-1) {bay}; + + \node (n1) at (1,4) {phi}; + \node[fill=green!60] (n2) at (1,3) {MASK}; + \node (n3) at (1,2) {điều}; + \node (n4) at (1,1) {khiển}; + \node[fill=green!60] (n5) at (1,0) {MASK}; + \node (n6) at (1,-1) {bay}; + + \node (p1) at (4,4) {phi}; + \node[fill=blue!60] (p2) at (4,3) {công}; + \node (p3) at (4,2) {điều}; + \node (p4) at (4,1) {khiển}; + \node[fill=red!60] (p5) at (4,0) {sân}; + \node (p6) at (4,-1) {bay}; + + \foreach \from/\to in { + m1/n1, m2/n2, m3/n3, m4/n4, m5/n5, m6/n6, n1/p1, n2/p2, n3/p3, + n4/p4, n5/p5, n6/p6} + \draw[->,thick] (\from) -- (\to); + + \draw[draw=black,fill=white] (1.75,4.5) rectangle ++(1.5,-6); + \node[fill=none] (g) at (2.25,2) + {\textbf{\begin{tabular}{c}Generator\\(BERT)\end{tabular}}}; + + \node[fill=none] (q1) at (7,4) {original}; + \node[fill=blue!60] (q2) at (7,3) {original}; + \node[fill=none] (q3) at (7,2) {original}; + \node[fill=none] (q4) at (7,1) {original}; + \node[fill=red!60] (q5) at (7,0) {replaced}; + \node[fill=none] (q6) at (7,-1) {original}; + + \foreach \from/\to in { + p1/q1, p2/q2, p3/q3, p4/q4, p5/q5, p6/q6} + \draw[->,thick] (\from) -- (\to); + + \draw[draw=black,fill=white] (4.75,4.5) rectangle ++(1.5,-6); + \node[fill=none] (d) at (5.1,2) + {\textbf{\begin{tabular}{c}Discriminator\\(ELECTRA)\end{tabular}}}; + + \end{tikzpicture} + \caption{An overview of replaced token detection by the ELECTRA + model on a sample drawn from vELECTRA} + \label{fig:electra} +\end{figure*} + +ELECTRA~\cite{Clark:2020} is currently the latest development of BERT-based model +where a more sample-efficient pre-training method is used. This method +is called replaced token detection. In this method, two neural +networks, a generator $G$ and a discriminator $D$, are trained +simultaneously. Each one consists of a Transformer network (an +encoder) that maps a sequence of input tokens $\vec x = [x_1, +x_2,\dots,x_n]$ into a sequence of contextualized vectors $h(\vec x) = +[h_1, h_2,\dots, h_n]$. For a given position $t$ where $x_t$ is the +masked token, the generator outputs a probability for generating a +particular token $x_t$ with a softmax distribution: +\begin{equation*} + p_G(x_t|\vec x) = \frac{\exp(x_t^\top h_G(\vec x)_t) }{\sum_{u} + \exp(u_t^\top h_G(\vec x)_t)}. +\end{equation*} +For a given position $t$, the discriminator predicts whether the +token $x_t$ is ``real'', i.e., that it comes from the data rather than the generator distribution, with a +sigmoid function: +\begin{equation*} +D(\vec x, t) = \sigma \left (w^\top h_D(\vec x)_t \right ) +\end{equation*} + +An overview of the replaced token detection in the ELECTRA model is +shown in Figure~\ref{fig:electra}. The generator is a BERT model which +is trained jointly with the discriminator. The Vietnamese example is a +real one which is sampled from our training corpus. + +\section{Experiments} +\label{sec:experiments} + +\subsection{Experimental Settings} + +\subsubsection{Model Training} +To train the proposed models, we use a CPU (Intel Xeon E5-2699 v4 +@2.20GHz) and a GPU (NVIDIA GeForce GTX 1080 Ti 11G). The +hyper-parameters that we chose are as follows: maximum sequence length +is 256, BERT learning rate is $2E-05$, learning rate is $1E-3$, number +of epochs is 100, batch size is 16, use apex and BERT weight decay is +set to 0, the Adam rate is $1E-08$. The configuration of our model is +as follows: number of RNN hidden units is 256, one RNN layer, +attention hidden dimension is 64, number of attention heads is 3 and a +dropout rate of 0.5. + +To build the pre-training language model, it is very important to have +a good and big dataset. This dataset was collected from online +newspapers\footnote{vnexpress.net, dantri.com.vn, baomoi.com, + zingnews.vn, vitalk.vn, etc.} in Vietnamese. To clean the data, we +perform the following pre-processing steps: +\begin{itemize} + \item Remove duplicated news + \item Only accept valid letters in Vietnamese + \item Remove too short sentences (less than 4 words) +\end{itemize} + +We obtained approximately 10GB of texts after collection. This dataset was + used to further pre-train the mBERT to build our viBERT which better + represents Vietnamese texts. About the vocab, we removed insufficient + vocab from mBERT because its vocab contains ones for other + languages. This was done by keeping only vocabs existed in the + dataset. + + In pre-training vELECTRA, we collect more data from two sources: + \begin{itemize} + \item NewsCorpus: 27.4 + GB\footnote{\url{https://github.com/binhvq/news-corpus}} + \item OscarCorpus: 31.0 + GB\footnote{\url{https://traces1.inria.fr/oscar/}} + \end{itemize} + +Totally, with more than 60GB of texts, we start training different +versions of vELECTRA. It is worth noting that pre-training viBERT +is much slower than pre-training vELECTRA. For this reason, we +pre-trained viBERT on the 10GB corpus rather than on the large 60GB +corpus. + +\begin{table*} + \centering + \begin{tabular}{|l | l | c | c | c | c | c| c | } + \hline + \textbf{No.}&\multicolumn{3}{c|}{\textbf{VLSP 2010}}&\multicolumn{4}{c|}{\textbf{VLSP 2013}}\\ \hline + +\multicolumn{8}{|l|}{\textbf{Existing models}} \\ \hline +1. & \multicolumn{2}{|l|}{MEM~\cite{Le:2010}}& 93.4 & \multicolumn{3}{|l|}{RDRPOSTagger~\cite{Nguyen:2014}} & 95.1 \\ \hline +2. & \multicolumn{3}{c}{} & \multicolumn{3}{|l|}{BiLSTM-CNN-CRF~\cite{Ma:2016}} & 95.4 \\ \hline +3. & \multicolumn{3}{c}{} & \multicolumn{3}{|l|}{VnCoreNLP-POS~\cite{Nguyen:2017}} & 95.9 \\ \hline +4. & \multicolumn{3}{c}{} & \multicolumn{3}{|l|}{jointWPD~\cite{Nguyen:2019}} & 96.0 \\ \hline +5. & \multicolumn{3}{c}{} & \multicolumn{3}{|l|}{PhoBERT\_base~\cite{Nguyen:2020}} & 96.7 \\ \hline + +\hline +\multicolumn{8}{|l|}{\textbf{Proposed models}} \\ \hline + & \textbf{Model Name} & \textbf{mBERT} & \textbf{viBERT} & \textbf{vELEC} & \textbf{mBERT} & \textbf{viBERT} & \textbf{vELEC}\\ + \hline +1.&+Fine-Tune & 94.34 & 95.07 & 95.35 & 96.35 & 96.60 & 96.62 \\ \hline +2.&+BiLSTM& 94.34 & 95.12 & 95.32 & 96.38 & 96.63 & \textbf{96.77} \\ \hline +3.&+BiGRU& 94.37 & 95.13 & 95.37 & 96.45 & 96.68& 96.73\\ \hline +4.&+BiLSTM\_Attn& 94.37 & 95.12 & \textbf{95.40} & 96.36 & 96.61 & 96.61 \\\hline +5.&+BiGRU\_Attn& 94.41 & 95.13 & 95.35 & 96.33 & 96.56 & 96.55 \\ \hline + \end{tabular} + \caption{Performance of our proposed models on the POS tagging task} + \label{tab:result-POS} +\end{table*} + +\subsubsection{Testing and evaluation methods} +In performing experiments, for datasets without development sets, we +randomly selected 10\% for fine-tuning the best parameters. + +To evaluate the effectiveness of the models, we use the commonly-used +metrics which are proposed by the organizers of VLSP. Specifically, we +measure the accuracy score on the POS tagging task which is calculated +as follows: +\begin{equation*} + Acc = \frac{\#of\_words\_correcly\_tagged}{\#of\_words\_in\_the\_test\_set} +\end{equation*} + +and the $F_1$ score on the NER task using the following equations: +\begin{equation*} + F_1 = 2*\frac{Pre*Rec}{Pre+Rec} +\end{equation*} +where \textit{Pre} and \textit{Rec} are determined as follows: +\begin{equation*} + Pre = \frac{NE\_true}{NE\_sys} +\end{equation*} + +\begin{equation*} + Rec = \frac{NE\_true}{NE\_ref} +\end{equation*} + +where \textit{NE\_ref} is the number of NEs in gold data, +\textit{NE\_sys} is the number of NEs in recognizing system, and +\textit{NE\_true} is the number of NEs which is correctly recognized +by the system. + +\subsection{Experimental Results} + +\subsubsection{On the PoS Tagging Task} + +Table~\ref{tab:result-POS} shows experimental results using different +proposed architectures on the top of mBERT and +viBERT and vELECTRA on two benchmark datasets from +the campaign VLSP 2010 and VLSP 2013. + +As can be seen that, with further pre-training techniques on a +Vietnamese dataset, we could significantly improve the performance of +the model. On the dataset of VLSP 2010, both viBERT and +vELECTRA significantly improved the performance by about 1\% +in the $F_1$ scores. On the dataset of VLSP 2013, these two models +slightly improved the performance. + +From the table, we can also see the performance of different +architectures including fine-tuning, BiLSTM, biGRU, and their +combination with attention mechanisms. Fine-tuning mBERT with +linear functions in several epochs could produce nearly state-of-the-art +results. It is also shown that building different architectures on top +slightly improve the performance of all mBERT, +viBERT and vELECTRA models. On the VLSP 2010, we got +the accuracy of 95.40\% using biLSTM with attention on top of +vELECTRA. On the VLSP 2013 dataset, we got 96.77\% in the +accuracy scores using only biLSTM on top of vELECTRA. + +In comparison to previous work, our proposed model - vELECTRA +- outperformed previous ones. It achieved from 1\% to 2\% higher than +existing work using different innovation in deep learning such as CNN, +LSTM, and joint learning techniques. Moreover, vELECTRA also +gained a slightly better than PhoBERT\_base, the same +pre-training language model released so far, by nearly 0.1\% in the +accuracy score. + + +\begin{table*} + \centering + \begin{tabular}{|l | l | c | c | c | c |c | c | } + \hline + \textbf{No.}& \multicolumn{4}{c|}{\textbf{VLSP 2016}}&\multicolumn{3}{c|}{\textbf{VLSP 2018}}\\ \hline +\multicolumn{8}{|l|}{\textbf{Existing models}} \\ \hline + + 1. & \multicolumn{3}{|l|}{TRE+BI~\cite{Le:2016}} & 87.98 & \multicolumn{2}{|l|}{VietNER} & 76.63 \\ \hline + +2. & \multicolumn{3}{|l|}{BiLSTM\_CNN\_CRF~\cite{Pham:2017a}} & 88.59 & \multicolumn{2}{|l|}{ZA-NER} & 74.70 \\ \hline + 3. & \multicolumn{3}{|l|}{BiLSTM~\cite{Pham:2017c}} & 92.02 & \multicolumn{3}{|l|}{} \\ \hline + 4. & \multicolumn{3}{|l|}{NNVLP~\cite{Pham:2017b}} & 92.91 & \multicolumn{3}{|l|}{} \\ \hline + + +5. & \multicolumn{3}{|l|}{VnCoreNLP-NER~\cite{Vu:2018}} & 88.6 & \multicolumn{3}{|l|}{} \\ \hline +6. & \multicolumn{3}{|l|}{VNER~\cite{Nguyen:2019}} & 89.6 & \multicolumn{3}{|l|}{}\\ \hline +7. & \multicolumn{3}{|l|}{ETNLP~\cite{Vu:2019}} & 91.1 & \multicolumn{3}{|l|}{} \\ \hline +8. & \multicolumn{3}{|l|}{PhoBERT\_base~\cite{Nguyen:2020}} & 93.6 & \multicolumn{3}{|l|}{} \\ \hline + +\multicolumn{8}{|l|}{\textbf{Proposed models}} \\ \hline + &\textbf{Model Name}& \textbf{mBERT} & \textbf{viBERT} & \textbf{VELEC} & \textbf{mBERT} & \textbf{viBERT} & \textbf{VELEC} \\ + \hline +1.&+Fine-Tune & 91.28 & 92.84 & 94.00 & 86.86 & 88.04 & 89.79 \\ \hline +2.&+BiLSTM& 91.03 & 93.00 & 93.70 & 86.62 & 88.68 & 89.92 \\ \hline +3.&+BiGRU& 91.52 & 93.44 & 93.93 & 86.72 & 88.98 & \textbf{90.31}\\ \hline +4.&+BiLSTM\_Attn& 91.23 & 92.97 & \textbf{94.07} & 87.12& 89.12 & 90.26 \\\hline +5.&+BiGRU\_Attn& 90.91 & 93.32 & 93.27 & 86.33 & 88.59 &89.94 \\ \hline + \end{tabular} + \caption{Performance of our proposed models on the NER task. ZA-NER~\cite{Luong:2018} + is the best system of VLSP 2018~\cite{Nguyen:2018}. VietNER is + from~\cite{NguyenKA:2019}} + \label{tab:result-NER} +\end{table*} + +\subsubsection{On the NER Task} + +Table~\ref{tab:result-NER} shows experimental results using different +proposed architectures on the top of mBERT, viBERT +and vELECTRA on two benchmark datasets from the campaign +VLSP 2016 and VLSP 2018. + +These results once again gave a strong evidence to the above statement +that further training mBERT on a small raw dataset could +significantly improve the performance of transformation-based language +models on downstream tasks. Training vELECTRA from scratch on +a big Vietnamese dataset could further enhance the performance. On two +datasets, vELECTRA improve the $F_1$ score by from 1\% to 3\% in +comparison to viBERT and mBERT. + +Looking at the performance of different architectures on top of these +pre-trained models, we acknowledged that biLSTM with attention once a +gain yielded the SOTA result on VLSP 2016 dataset. On VLSP 2018 +dataset, the architecture of biGRU yielded the best performance at +90.31\% in the $F_1$ score. + +Comparing to previous work, the best proposed model outperformed all +work by a large margin on both datasets. + + +\begin{figure*} + \centering + \begin{tikzpicture} + \begin{axis}[ + height=5.8cm, + width=\textwidth, + ybar, + bar width=10, + enlarge y limits=0.25, + legend style={at={(0.5,0.85)},anchor=south,legend columns=-1}, + xlabel={\textit{}}, + ylabel={\textit{milliseconds per sentence}}, + symbolic x coords={+Fine-Tune,+biLSTM,+biGRU,+biLSTM-Att,+biGRU-Att}, + xtick=data, + nodes near coords, + every node near coord/.append style={font=\tiny,rotate=90,anchor=east}, + nodes near coords={\pgfmathprintnumber[precision=3]{\pgfplotspointmeta}}, + nodes near coords align={vertical}, + ] + \addplot coordinates {(+Fine-Tune,1.8) (+biLSTM,2.8) (+biGRU,3.1) (+biLSTM-Att, 2.9) (+biGRU-Att, 3.3)}; + \addplot coordinates {(+Fine-Tune,2.9) (+biLSTM,2.8) (+biGRU,2.7) (+biLSTM-Att, 2.9) (+biGRU-Att, 2.9)}; + \addplot coordinates {(+Fine-Tune,1.6) (+biLSTM,2.4) (+biGRU,2.6) (+biLSTM-Att, 1.8) (+biGRU-Att, 2.4)}; + \legend{mBERT, viBERT, vELECTRA} + \end{axis} + \end{tikzpicture} + \caption{Decoding time on PoS task -- VLSP 2013} + \label{fig:vlsp2013} +\end{figure*} + +\subsection{Decoding Time} + +Figure \ref{fig:vlsp2013} and \ref{fig:vlsp2016} shows the averaged +decoding time measured on one sentence. According to our statistics, +the averaged length of one sentence in VLSP 2013 and VLSP 2016 +datasets are 22.55 and 21.87 words, respectively. + +For the POS tagging task measured on VLSP 2013 dataset, among three +models, the fastest decoding time is of vELECTRA model, followed by +viBERT model, and finally by mBERT model. This statement holds for +four proposed architectures on top of these three models. However, for +the fine-tuning technique, the decoding time of mBERT is faster than +that of viBERT. + +For the NER task measured on the VLSP 2016 dataset, among three models, +the slowest time is of viBERT model with more than 2 +milliseconds per sentence. The decoding times on mBERT topped with +simple fine-tuning techniques, or biGRU, or biLSTM-attention is a +little bit faster than on vELECTRA with the same architecture. + +This experiment shows that our proposed models are of practical +use. In fact, they are currently deployed as a core component of our +commercial chatbot engine FPT.AI\footnote{\url{http://fpt.ai/}} which +is serving effectively many customers. More precisely, the FPT.AI +platform has been used by about 70 large enterprises, and of over 27,000 +frequent developers, serving more than 30 million end +users.\footnote{These numbers are reported as of August, 2020.} + +\section{Conclusion} +\label{sec:conclusion} + +This paper presents some new model architectures for sequence tagging +and our experimental results for Vietnamese part-of-speech tagging and +named entity recognition. Our proposed model vELECTRA outperforms +previous ones. For part-of-speech tagging, it improves about 2\% of +absolute point in comparison with existing work which use different +innovation in deep learning such as CNN, LSTM, or joint learning +techniques. For named entity recognition, the vELECTRA outperforms +all previous work by a large margin on both VLSP 2016 and VLSP 2018 +datasets. + +Our code and pre-trained models are published as an open source +project for facilitate adoption and further research in the Vietnamese +language processing community.\footnote{viBERT is available at + \url{https://github.com/fpt-corp/viBERT} and vELECTRA is available + at \url{https://github.com/fpt-corp/vELECTRA}.} An online service of +the models for demonstration is also accessible at +\url{https://fpt.ai/nlp/bert/}. A variant and more advanced version +of this model is currently deployed as a core component of our +commercial chatbot engine FPT.AI which is serving effectively +millions of end users. In particular, these models are being +fine-tuned to improve task-oriented dialogue in mixed and multiple +domains~\cite{Luong:2019} and dependency parsing~\cite{Le:2015c}. + +\begin{figure*} + \centering + \begin{tikzpicture} + \begin{axis}[ + height=5.8cm, + width=\textwidth, + ybar, + bar width=10, + %enlarge x limits=0.05, + enlarge y limits=0.25, + legend style={at={(0.5,0.85)},anchor=south,legend columns=-1}, + xlabel={\textit{}}, + ylabel={\textit{milliseconds per sentence}}, + symbolic x coords={+Fine-Tune,+biLSTM,+biGRU,+biLSTM-Att,+biGRU-Att}, + xtick=data, + nodes near coords, + every node near coord/.append style={font=\tiny,rotate=90,anchor=east}, + nodes near coords={\pgfmathprintnumber[precision=3]{\pgfplotspointmeta}}, + nodes near coords align={vertical}, + ] + \addplot coordinates {(+Fine-Tune,1.1) (+biLSTM,1.7) (+biGRU,1.4) (+biLSTM-Att, 1.8) (+biGRU-Att, 2.0)}; + \addplot coordinates {(+Fine-Tune,1.9) (+biLSTM,2.1) (+biGRU,2.2) (+biLSTM-Att, 2.3) (+biGRU-Att, 2.2)}; + \addplot coordinates {(+Fine-Tune,1.5) (+biLSTM,1.7) (+biGRU,1.6) (+biLSTM-Att, 2.0) (+biGRU-Att, 1.6)}; + \legend{mBERT, viBERT, vELECTRA} + \end{axis} + \end{tikzpicture} + \caption{Decoding time on NER task -- VLSP 2016} + \label{fig:vlsp2016} +\end{figure*} + +\section*{Acknowledgement} + +We thank three anonymous reviewers for their valuable comments for +improving our manuscript. + +% \begin{tikzpicture}[cap=round,line width=3pt] +% \filldraw [fill=examplefill] (0,0) circle (2cm); + +% \foreach \angle / \label in +% {0/3, 30/2, 60/1, 90/12, 120/11, 150/10, 180/9, +% 210/8, 240/7, 270/6, 300/5, 330/4} +% { +% \draw[line width=1pt] (\angle:1.8cm) -- (\angle:2cm); +% \draw (\angle:1.4cm) node{\textsf{\label}}; +% } + +% \foreach \angle in {0,90,180,270} +% \draw[line width=2pt] (\angle:1.6cm) -- (\angle:2cm); + +% \draw (0,0) -- (120:0.8cm); % hour +% \draw (0,0) -- (90:1cm); % minute +% \end{tikzpicture}% + +%\newpage +\bibliographystyle{acl} +\bibliography{references} + +\end{document} diff --git a/references/2020.arxiv.the/source/acl.bst b/references/2020.arxiv.the/source/acl.bst new file mode 100644 index 0000000000000000000000000000000000000000..84e6f17bd6e19a34bffb9716d147fc04a4809961 --- /dev/null +++ b/references/2020.arxiv.the/source/acl.bst @@ -0,0 +1,1322 @@ +% BibTeX `acl' style file for BibTeX version 0.99c, LaTeX version 2.09 +% This version was made by modifying `aaai-named' format based on the master +% file by Oren Patashnik (PATASHNIK@SCORE.STANFORD.EDU) + +% Copyright (C) 1985, all rights reserved. +% Modifications Copyright 1988, Peter F. Patel-Schneider +% Further modifictions by Stuart Shieber, 1991, and Fernando Pereira, 1992. +% Copying of this file is authorized only if either +% (1) you make absolutely no changes to your copy, including name, or +% (2) if you do make changes, you name it something other than +% btxbst.doc, plain.bst, unsrt.bst, alpha.bst, and abbrv.bst. +% This restriction helps ensure that all standard styles are identical. + +% There are undoubtably bugs in this style. If you make bug fixes, +% improvements, etc. please let me know. My e-mail address is: +% pfps@spar.slb.com + +% Citation format: [author-last-name, year] +% [author-last-name and author-last-name, year] +% [author-last-name {\em et al.}, year] +% +% Reference list ordering: alphabetical by author or whatever passes +% for author in the absence of one. +% +% This BibTeX style has support for short (year only) citations. This +% is done by having the citations actually look like +% \citename{name-info, }year +% The LaTeX style has to have the following +% \let\@internalcite\cite +% \def\cite{\def\citename##1{##1}\@internalcite} +% \def\shortcite{\def\citename##1{}\@internalcite} +% \def\@biblabel#1{\def\citename##1{##1}[#1]\hfill} +% which makes \shortcite the macro for short citations. + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Changes made by SMS for thesis style +% no emphasis on "et al." +% "Ph.D." includes periods (not "PhD") +% moved year to immediately after author's name +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +ENTRY + { address + author + booktitle + chapter + edition + editor + howpublished + institution + journal + key + month + note + number + organization + pages + publisher + school + series + title + type + volume + year + } + {} + { label extra.label sort.label } + +INTEGERS { output.state before.all mid.sentence after.sentence after.block } + +FUNCTION {init.state.consts} +{ #0 'before.all := + #1 'mid.sentence := + #2 'after.sentence := + #3 'after.block := +} + +STRINGS { s t } + +FUNCTION {output.nonnull} +{ 's := + output.state mid.sentence = + { ", " * write$ } + { output.state after.block = + { add.period$ write$ + newline$ + "\newblock " write$ + } + { output.state before.all = + 'write$ + { add.period$ " " * write$ } + if$ + } + if$ + mid.sentence 'output.state := + } + if$ + s +} + +FUNCTION {output} +{ duplicate$ empty$ + 'pop$ + 'output.nonnull + if$ +} + +FUNCTION {output.check} +{ 't := + duplicate$ empty$ + { pop$ "empty " t * " in " * cite$ * warning$ } + 'output.nonnull + if$ +} + +FUNCTION {output.bibitem} +{ newline$ + + "\bibitem[" write$ + label write$ + "]{" write$ + + cite$ write$ + "}" write$ + newline$ + "" + before.all 'output.state := +} + +FUNCTION {fin.entry} +{ add.period$ + write$ + newline$ +} + +FUNCTION {new.block} +{ output.state before.all = + 'skip$ + { after.block 'output.state := } + if$ +} + +FUNCTION {new.sentence} +{ output.state after.block = + 'skip$ + { output.state before.all = + 'skip$ + { after.sentence 'output.state := } + if$ + } + if$ +} + +FUNCTION {not} +{ { #0 } + { #1 } + if$ +} + +FUNCTION {and} +{ 'skip$ + { pop$ #0 } + if$ +} + +FUNCTION {or} +{ { pop$ #1 } + 'skip$ + if$ +} + +FUNCTION {new.block.checka} +{ empty$ + 'skip$ + 'new.block + if$ +} + +FUNCTION {new.block.checkb} +{ empty$ + swap$ empty$ + and + 'skip$ + 'new.block + if$ +} + +FUNCTION {new.sentence.checka} +{ empty$ + 'skip$ + 'new.sentence + if$ +} + +FUNCTION {new.sentence.checkb} +{ empty$ + swap$ empty$ + and + 'skip$ + 'new.sentence + if$ +} + +FUNCTION {field.or.null} +{ duplicate$ empty$ + { pop$ "" } + 'skip$ + if$ +} + +FUNCTION {emphasize} +{ duplicate$ empty$ + { pop$ "" } + { "{\em " swap$ * "}" * } + if$ +} + +INTEGERS { nameptr namesleft numnames } + +FUNCTION {format.names} +{ 's := + #1 'nameptr := + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + + { s nameptr "{ff~}{vv~}{ll}{, jj}" format.name$ 't := + + nameptr #1 > + { namesleft #1 > + { ", " * t * } + { numnames #2 > + { "," * } + 'skip$ + if$ + t "others" = + { " et~al." * } + { " and " * t * } + if$ + } + if$ + } + 't + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ +} + +FUNCTION {format.authors} +{ author empty$ + { "" } + { author format.names } + if$ +} + +FUNCTION {format.editors} +{ editor empty$ + { "" } + { editor format.names + editor num.names$ #1 > + { ", editors" * } + { ", editor" * } + if$ + } + if$ +} + +FUNCTION {format.title} +{ title empty$ + { "" } + + { title "t" change.case$ } + + if$ +} + +FUNCTION {n.dashify} +{ 't := + "" + { t empty$ not } + { t #1 #1 substring$ "-" = + { t #1 #2 substring$ "--" = not + { "--" * + t #2 global.max$ substring$ 't := + } + { { t #1 #1 substring$ "-" = } + { "-" * + t #2 global.max$ substring$ 't := + } + while$ + } + if$ + } + { t #1 #1 substring$ * + t #2 global.max$ substring$ 't := + } + if$ + } + while$ +} + +FUNCTION {format.date} +{ year empty$ + { month empty$ + { "" } + { "there's a month but no year in " cite$ * warning$ + month + } + if$ + } + { month empty$ + { "" } + { month } + if$ + } + if$ +} + +FUNCTION {format.btitle} +{ title emphasize +} + +FUNCTION {tie.or.space.connect} +{ duplicate$ text.length$ #3 < + { "~" } + { " " } + if$ + swap$ * * +} + +FUNCTION {either.or.check} +{ empty$ + 'pop$ + { "can't use both " swap$ * " fields in " * cite$ * warning$ } + if$ +} + +FUNCTION {format.bvolume} +{ volume empty$ + { "" } + { "volume" volume tie.or.space.connect + series empty$ + 'skip$ + { " of " * series emphasize * } + if$ + "volume and number" number either.or.check + } + if$ +} + +FUNCTION {format.number.series} +{ volume empty$ + { number empty$ + { series field.or.null } + { output.state mid.sentence = + { "number" } + { "Number" } + if$ + number tie.or.space.connect + series empty$ + { "there's a number but no series in " cite$ * warning$ } + { " in " * series * } + if$ + } + if$ + } + { "" } + if$ +} + +FUNCTION {format.edition} +{ edition empty$ + { "" } + { output.state mid.sentence = + { edition "l" change.case$ " edition" * } + { edition "t" change.case$ " edition" * } + if$ + } + if$ +} + +INTEGERS { multiresult } + +FUNCTION {multi.page.check} +{ 't := + #0 'multiresult := + { multiresult not + t empty$ not + and + } + { t #1 #1 substring$ + duplicate$ "-" = + swap$ duplicate$ "," = + swap$ "+" = + or or + { #1 'multiresult := } + { t #2 global.max$ substring$ 't := } + if$ + } + while$ + multiresult +} + +FUNCTION {format.pages} +{ pages empty$ + { "" } + { pages multi.page.check + { "pages" pages n.dashify tie.or.space.connect } + { "page" pages tie.or.space.connect } + if$ + } + if$ +} + +FUNCTION {format.year.label} +{ year extra.label * +} + +FUNCTION {format.vol.num.pages} +{ volume field.or.null + number empty$ + 'skip$ + { "(" number * ")" * * + volume empty$ + { "there's a number but no volume in " cite$ * warning$ } + 'skip$ + if$ + } + if$ + pages empty$ + 'skip$ + { duplicate$ empty$ + { pop$ format.pages } + { ":" * pages n.dashify * } + if$ + } + if$ +} + +FUNCTION {format.chapter.pages} +{ chapter empty$ + 'format.pages + { type empty$ + { "chapter" } + { type "l" change.case$ } + if$ + chapter tie.or.space.connect + pages empty$ + 'skip$ + { ", " * format.pages * } + if$ + } + if$ +} + +FUNCTION {format.in.ed.booktitle} +{ booktitle empty$ + { "" } + { editor empty$ + { "In " booktitle emphasize * } + { "In " format.editors * ", " * booktitle emphasize * } + if$ + } + if$ +} + +FUNCTION {empty.misc.check} +{ author empty$ title empty$ howpublished empty$ + month empty$ year empty$ note empty$ + and and and and and + + key empty$ not and + + { "all relevant fields are empty in " cite$ * warning$ } + 'skip$ + if$ +} + +FUNCTION {format.thesis.type} +{ type empty$ + 'skip$ + { pop$ + type "t" change.case$ + } + if$ +} + +FUNCTION {format.tr.number} +{ type empty$ + { "Technical Report" } + 'type + if$ + number empty$ + { "t" change.case$ } + { number tie.or.space.connect } + if$ +} + +FUNCTION {format.article.crossref} +{ key empty$ + { journal empty$ + { "need key or journal for " cite$ * " to crossref " * crossref * + warning$ + "" + } + { "In {\em " journal * "\/}" * } + if$ + } + { "In " key * } + if$ + " \cite{" * crossref * "}" * +} + +FUNCTION {format.crossref.editor} +{ editor #1 "{vv~}{ll}" format.name$ + editor num.names$ duplicate$ + #2 > + { pop$ " et~al." * } + { #2 < + 'skip$ + { editor #2 "{ff }{vv }{ll}{ jj}" format.name$ "others" = + { " et~al." * } + { " and " * editor #2 "{vv~}{ll}" format.name$ * } + if$ + } + if$ + } + if$ +} + +FUNCTION {format.book.crossref} +{ volume empty$ + { "empty volume in " cite$ * "'s crossref of " * crossref * warning$ + "In " + } + { "Volume" volume tie.or.space.connect + " of " * + } + if$ + editor empty$ + editor field.or.null author field.or.null = + or + { key empty$ + { series empty$ + { "need editor, key, or series for " cite$ * " to crossref " * + crossref * warning$ + "" * + } + { "{\em " * series * "\/}" * } + if$ + } + { key * } + if$ + } + { format.crossref.editor * } + if$ + " \cite{" * crossref * "}" * +} + +FUNCTION {format.incoll.inproc.crossref} +{ editor empty$ + editor field.or.null author field.or.null = + or + { key empty$ + { booktitle empty$ + { "need editor, key, or booktitle for " cite$ * " to crossref " * + crossref * warning$ + "" + } + { "In {\em " booktitle * "\/}" * } + if$ + } + { "In " key * } + if$ + } + { "In " format.crossref.editor * } + if$ + " \cite{" * crossref * "}" * +} + +FUNCTION {article} +{ output.bibitem + format.authors "author" output.check + new.block + format.year.label "year" output.check + new.block + format.title "title" output.check + new.block + crossref missing$ + { journal emphasize "journal" output.check + format.vol.num.pages output + format.date output + } + { format.article.crossref output.nonnull + format.pages output + } + if$ + new.block + note output + fin.entry +} + +FUNCTION {book} +{ output.bibitem + author empty$ + { format.editors "author and editor" output.check } + { format.authors output.nonnull + crossref missing$ + { "author and editor" editor either.or.check } + 'skip$ + if$ + } + if$ + new.block + format.year.label "year" output.check + new.block + format.btitle "title" output.check + crossref missing$ + { format.bvolume output + new.block + format.number.series output + new.sentence + publisher "publisher" output.check + address output + } + { new.block + format.book.crossref output.nonnull + } + if$ + format.edition output + format.date output + new.block + note output + fin.entry +} + +FUNCTION {booklet} +{ output.bibitem + format.authors output + new.block + format.year.label "year" output.check + new.block + format.title "title" output.check + howpublished address new.block.checkb + howpublished output + address output + format.date output + new.block + note output + fin.entry +} + +FUNCTION {inbook} +{ output.bibitem + author empty$ + { format.editors "author and editor" output.check } + { format.authors output.nonnull + crossref missing$ + { "author and editor" editor either.or.check } + 'skip$ + if$ + } + if$ + format.year.label "year" output.check + new.block + new.block + format.btitle "title" output.check + crossref missing$ + { format.bvolume output + format.chapter.pages "chapter and pages" output.check + new.block + format.number.series output + new.sentence + publisher "publisher" output.check + address output + } + { format.chapter.pages "chapter and pages" output.check + new.block + format.book.crossref output.nonnull + } + if$ + format.edition output + format.date output + new.block + note output + fin.entry +} + +FUNCTION {incollection} +{ output.bibitem + format.authors "author" output.check + new.block + format.year.label "year" output.check + new.block + format.title "title" output.check + new.block + crossref missing$ + { format.in.ed.booktitle "booktitle" output.check + format.bvolume output + format.number.series output + format.chapter.pages output + new.sentence + publisher "publisher" output.check + address output + format.edition output + format.date output + } + { format.incoll.inproc.crossref output.nonnull + format.chapter.pages output + } + if$ + new.block + note output + fin.entry +} + +FUNCTION {inproceedings} +{ output.bibitem + format.authors "author" output.check + new.block + format.year.label "year" output.check + new.block + format.title "title" output.check + new.block + crossref missing$ + { format.in.ed.booktitle "booktitle" output.check + format.bvolume output + format.number.series output + format.pages output + address empty$ + { organization publisher new.sentence.checkb + organization output + publisher output + format.date output + } + { address output.nonnull + format.date output + new.sentence + organization output + publisher output + } + if$ + } + { format.incoll.inproc.crossref output.nonnull + format.pages output + } + if$ + new.block + note output + fin.entry +} + +FUNCTION {conference} { inproceedings } + +FUNCTION {manual} +{ output.bibitem + author empty$ + { organization empty$ + 'skip$ + { organization output.nonnull + address output + } + if$ + } + { format.authors output.nonnull } + if$ + format.year.label "year" output.check + new.block + new.block + format.btitle "title" output.check + author empty$ + { organization empty$ + { address new.block.checka + address output + } + 'skip$ + if$ + } + { organization address new.block.checkb + organization output + address output + } + if$ + format.edition output + format.date output + new.block + note output + fin.entry +} + +FUNCTION {mastersthesis} +{ output.bibitem + format.authors "author" output.check + new.block + format.year.label "year" output.check + new.block + format.title "title" output.check + new.block + "Master's thesis" format.thesis.type output.nonnull + school "school" output.check + address output + format.date output + new.block + note output + fin.entry +} + +FUNCTION {misc} +{ output.bibitem + format.authors output + new.block + format.year.label output + new.block + title howpublished new.block.checkb + format.title output + howpublished new.block.checka + howpublished output + format.date output + new.block + note output + fin.entry + empty.misc.check +} + +FUNCTION {phdthesis} +{ output.bibitem + format.authors "author" output.check + new.block + format.year.label "year" output.check + new.block + format.btitle "title" output.check + new.block + "{Ph.D.} thesis" format.thesis.type output.nonnull + school "school" output.check + address output + format.date output + new.block + note output + fin.entry +} + +FUNCTION {proceedings} +{ output.bibitem + editor empty$ + { organization output } + { format.editors output.nonnull } + if$ + new.block + format.year.label "year" output.check + new.block + format.btitle "title" output.check + format.bvolume output + format.number.series output + address empty$ + { editor empty$ + { publisher new.sentence.checka } + { organization publisher new.sentence.checkb + organization output + } + if$ + publisher output + format.date output + } + { address output.nonnull + format.date output + new.sentence + editor empty$ + 'skip$ + { organization output } + if$ + publisher output + } + if$ + new.block + note output + fin.entry +} + +FUNCTION {techreport} +{ output.bibitem + format.authors "author" output.check + new.block + format.year.label "year" output.check + new.block + format.title "title" output.check + new.block + format.tr.number output.nonnull + institution "institution" output.check + address output + format.date output + new.block + note output + fin.entry +} + +FUNCTION {unpublished} +{ output.bibitem + format.authors "author" output.check + new.block + format.year.label "year" output.check + new.block + format.title "title" output.check + new.block + note "note" output.check + format.date output + fin.entry +} + +FUNCTION {default.type} { misc } + +MACRO {jan} {"January"} + +MACRO {feb} {"February"} + +MACRO {mar} {"March"} + +MACRO {apr} {"April"} + +MACRO {may} {"May"} + +MACRO {jun} {"June"} + +MACRO {jul} {"July"} + +MACRO {aug} {"August"} + +MACRO {sep} {"September"} + +MACRO {oct} {"October"} + +MACRO {nov} {"November"} + +MACRO {dec} {"December"} + +MACRO {acmcs} {"ACM Computing Surveys"} + +MACRO {acta} {"Acta Informatica"} + +MACRO {cacm} {"Communications of the ACM"} + +MACRO {ibmjrd} {"IBM Journal of Research and Development"} + +MACRO {ibmsj} {"IBM Systems Journal"} + +MACRO {ieeese} {"IEEE Transactions on Software Engineering"} + +MACRO {ieeetc} {"IEEE Transactions on Computers"} + +MACRO {ieeetcad} + {"IEEE Transactions on Computer-Aided Design of Integrated Circuits"} + +MACRO {ipl} {"Information Processing Letters"} + +MACRO {jacm} {"Journal of the ACM"} + +MACRO {jcss} {"Journal of Computer and System Sciences"} + +MACRO {scp} {"Science of Computer Programming"} + +MACRO {sicomp} {"SIAM Journal on Computing"} + +MACRO {tocs} {"ACM Transactions on Computer Systems"} + +MACRO {tods} {"ACM Transactions on Database Systems"} + +MACRO {tog} {"ACM Transactions on Graphics"} + +MACRO {toms} {"ACM Transactions on Mathematical Software"} + +MACRO {toois} {"ACM Transactions on Office Information Systems"} + +MACRO {toplas} {"ACM Transactions on Programming Languages and Systems"} + +MACRO {tcs} {"Theoretical Computer Science"} + +READ + +FUNCTION {sortify} +{ purify$ + "l" change.case$ +} + +INTEGERS { len } + +FUNCTION {chop.word} +{ 's := + 'len := + s #1 len substring$ = + { s len #1 + global.max$ substring$ } + 's + if$ +} + +INTEGERS { et.al.char.used } + +FUNCTION {initialize.et.al.char.used} +{ #0 'et.al.char.used := +} + +EXECUTE {initialize.et.al.char.used} + +FUNCTION {format.lab.names} +{ 's := + s num.names$ 'numnames := + + numnames #1 = + { s #1 "{vv }{ll}" format.name$ } + { numnames #2 = + { s #1 "{vv }{ll }and " format.name$ s #2 "{vv }{ll}" format.name$ * + } + { s #1 "{vv }{ll }\bgroup et al.\egroup " format.name$ } + if$ + } + if$ + +} + +FUNCTION {author.key.label} +{ author empty$ + { key empty$ + + { cite$ #1 #3 substring$ } + + { key #3 text.prefix$ } + if$ + } + { author format.lab.names } + if$ +} + +FUNCTION {author.editor.key.label} +{ author empty$ + { editor empty$ + { key empty$ + + { cite$ #1 #3 substring$ } + + { key #3 text.prefix$ } + if$ + } + { editor format.lab.names } + if$ + } + { author format.lab.names } + if$ +} + +FUNCTION {author.key.organization.label} +{ author empty$ + { key empty$ + { organization empty$ + + { cite$ #1 #3 substring$ } + + { "The " #4 organization chop.word #3 text.prefix$ } + if$ + } + { key #3 text.prefix$ } + if$ + } + { author format.lab.names } + if$ +} + +FUNCTION {editor.key.organization.label} +{ editor empty$ + { key empty$ + { organization empty$ + + { cite$ #1 #3 substring$ } + + { "The " #4 organization chop.word #3 text.prefix$ } + if$ + } + { key #3 text.prefix$ } + if$ + } + { editor format.lab.names } + if$ +} + +FUNCTION {calc.label} +{ type$ "book" = + type$ "inbook" = + or + 'author.editor.key.label + { type$ "proceedings" = + 'editor.key.organization.label + { type$ "manual" = + 'author.key.organization.label + 'author.key.label + if$ + } + if$ + } + if$ + duplicate$ + + "\protect\citename{" swap$ * "}" * + year field.or.null purify$ * + 'label := + year field.or.null purify$ * + + sortify 'sort.label := +} + +FUNCTION {sort.format.names} +{ 's := + #1 'nameptr := + "" + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { nameptr #1 > + { " " * } + 'skip$ + if$ + + s nameptr "{vv{ } }{ll{ }}{ ff{ }}{ jj{ }}" format.name$ 't := + + nameptr numnames = t "others" = and + { "et al" * } + { t sortify * } + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ +} + +FUNCTION {sort.format.title} +{ 't := + "A " #2 + "An " #3 + "The " #4 t chop.word + chop.word + chop.word + sortify + #1 global.max$ substring$ +} + +FUNCTION {author.sort} +{ author empty$ + { key empty$ + { "to sort, need author or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { author sort.format.names } + if$ +} + +FUNCTION {author.editor.sort} +{ author empty$ + { editor empty$ + { key empty$ + { "to sort, need author, editor, or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { editor sort.format.names } + if$ + } + { author sort.format.names } + if$ +} + +FUNCTION {author.organization.sort} +{ author empty$ + { organization empty$ + { key empty$ + { "to sort, need author, organization, or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { "The " #4 organization chop.word sortify } + if$ + } + { author sort.format.names } + if$ +} + +FUNCTION {editor.organization.sort} +{ editor empty$ + { organization empty$ + { key empty$ + { "to sort, need editor, organization, or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { "The " #4 organization chop.word sortify } + if$ + } + { editor sort.format.names } + if$ +} + +FUNCTION {presort} + +{ calc.label + sort.label + " " + * + type$ "book" = + + type$ "inbook" = + or + 'author.editor.sort + { type$ "proceedings" = + 'editor.organization.sort + { type$ "manual" = + 'author.organization.sort + 'author.sort + if$ + } + if$ + } + if$ + + * + + " " + * + year field.or.null sortify + * + " " + * + title field.or.null + sort.format.title + * + #1 entry.max$ substring$ + 'sort.key$ := +} + +ITERATE {presort} + +SORT + +STRINGS { longest.label last.sort.label next.extra } + +INTEGERS { longest.label.width last.extra.num } + +FUNCTION {initialize.longest.label} +{ "" 'longest.label := + #0 int.to.chr$ 'last.sort.label := + "" 'next.extra := + #0 'longest.label.width := + #0 'last.extra.num := +} + +FUNCTION {forward.pass} +{ last.sort.label sort.label = + { last.extra.num #1 + 'last.extra.num := + last.extra.num int.to.chr$ 'extra.label := + } + { "a" chr.to.int$ 'last.extra.num := + "" 'extra.label := + sort.label 'last.sort.label := + } + if$ +} + +FUNCTION {reverse.pass} +{ next.extra "b" = + { "a" 'extra.label := } + 'skip$ + if$ + label extra.label * 'label := + label width$ longest.label.width > + { label 'longest.label := + label width$ 'longest.label.width := + } + 'skip$ + if$ + extra.label 'next.extra := +} + +EXECUTE {initialize.longest.label} + +ITERATE {forward.pass} + +REVERSE {reverse.pass} + +FUNCTION {begin.bib} + +{ et.al.char.used + { "\newcommand{\etalchar}[1]{$^{#1}$}" write$ newline$ } + 'skip$ + if$ + preamble$ empty$ + + 'skip$ + { preamble$ write$ newline$ } + if$ + + "\begin{thebibliography}{" "}" * write$ newline$ + +} + +EXECUTE {begin.bib} + +EXECUTE {init.state.consts} + +ITERATE {call.type$} + +FUNCTION {end.bib} +{ newline$ + "\end{thebibliography}" write$ newline$ +} + +EXECUTE {end.bib} + + diff --git a/references/2020.arxiv.the/source/arxiv.sty b/references/2020.arxiv.the/source/arxiv.sty new file mode 100644 index 0000000000000000000000000000000000000000..f33b36ad53e74f621998429cb61af86078405a1a --- /dev/null +++ b/references/2020.arxiv.the/source/arxiv.sty @@ -0,0 +1,262 @@ +\NeedsTeXFormat{LaTeX2e} + +\ProcessOptions\relax + +% fonts +\renewcommand{\rmdefault}{ptm} +\renewcommand{\sfdefault}{phv} + +% set page geometry +\usepackage[verbose=true,letterpaper]{geometry} +\AtBeginDocument{ + \newgeometry{ + textheight=9in, + textwidth=6.5in, + top=1in, + headheight=14pt, + headsep=25pt, + footskip=30pt + } +} + +\widowpenalty=10000 +\clubpenalty=10000 +\flushbottom +\sloppy + + + +%\newcommand{\headeright}{A Preprint} +\newcommand{\headeright}{} +\newcommand{\undertitle}{A Preprint} + +\usepackage{fancyhdr} +\fancyhf{} +\pagestyle{fancy} +\renewcommand{\headrulewidth}{0.4pt} +\fancyheadoffset{0pt} +\rhead{\scshape \footnotesize \headeright} +\chead{\@title} +\cfoot{\thepage} + + +%Handling Keywords +\def\keywordname{{\bfseries \emph Keywords}}% +\def\keywords#1{\par\addvspace\medskipamount{\rightskip=0pt plus1cm +\def\and{\ifhmode\unskip\nobreak\fi\ $\cdot$ +}\noindent\keywordname\enspace\ignorespaces#1\par}} + +% font sizes with reduced leading +\renewcommand{\normalsize}{% + \@setfontsize\normalsize\@xpt\@xipt + \abovedisplayskip 7\p@ \@plus 2\p@ \@minus 5\p@ + \abovedisplayshortskip \z@ \@plus 3\p@ + \belowdisplayskip \abovedisplayskip + \belowdisplayshortskip 4\p@ \@plus 3\p@ \@minus 3\p@ +} +\normalsize +\renewcommand{\small}{% + \@setfontsize\small\@ixpt\@xpt + \abovedisplayskip 6\p@ \@plus 1.5\p@ \@minus 4\p@ + \abovedisplayshortskip \z@ \@plus 2\p@ + \belowdisplayskip \abovedisplayskip + \belowdisplayshortskip 3\p@ \@plus 2\p@ \@minus 2\p@ +} +\renewcommand{\footnotesize}{\@setfontsize\footnotesize\@ixpt\@xpt} +\renewcommand{\scriptsize}{\@setfontsize\scriptsize\@viipt\@viiipt} +\renewcommand{\tiny}{\@setfontsize\tiny\@vipt\@viipt} +\renewcommand{\large}{\@setfontsize\large\@xiipt{14}} +\renewcommand{\Large}{\@setfontsize\Large\@xivpt{16}} +\renewcommand{\LARGE}{\@setfontsize\LARGE\@xviipt{20}} +\renewcommand{\huge}{\@setfontsize\huge\@xxpt{23}} +\renewcommand{\Huge}{\@setfontsize\Huge\@xxvpt{28}} + +% sections with less space +\providecommand{\section}{} +\renewcommand{\section}{% + \@startsection{section}{1}{\z@}% + {-2.0ex \@plus -0.5ex \@minus -0.2ex}% + { 1.5ex \@plus 0.3ex \@minus 0.2ex}% + {\large\bf\raggedright}% +} +\providecommand{\subsection}{} +\renewcommand{\subsection}{% + \@startsection{subsection}{2}{\z@}% + {-1.8ex \@plus -0.5ex \@minus -0.2ex}% + { 0.8ex \@plus 0.2ex}% + {\normalsize\bf\raggedright}% +} +\providecommand{\subsubsection}{} +\renewcommand{\subsubsection}{% + \@startsection{subsubsection}{3}{\z@}% + {-1.5ex \@plus -0.5ex \@minus -0.2ex}% + { 0.5ex \@plus 0.2ex}% + {\normalsize\bf\raggedright}% +} +\providecommand{\paragraph}{} +\renewcommand{\paragraph}{% + \@startsection{paragraph}{4}{\z@}% + {1.5ex \@plus 0.5ex \@minus 0.2ex}% + {-1em}% + {\normalsize\bf}% +} +\providecommand{\subparagraph}{} +\renewcommand{\subparagraph}{% + \@startsection{subparagraph}{5}{\z@}% + {1.5ex \@plus 0.5ex \@minus 0.2ex}% + {-1em}% + {\normalsize\bf}% +} +\providecommand{\subsubsubsection}{} +\renewcommand{\subsubsubsection}{% + \vskip5pt{\noindent\normalsize\rm\raggedright}% +} + +% float placement +\renewcommand{\topfraction }{0.85} +\renewcommand{\bottomfraction }{0.4} +\renewcommand{\textfraction }{0.1} +\renewcommand{\floatpagefraction}{0.7} + +\newlength{\@abovecaptionskip}\setlength{\@abovecaptionskip}{7\p@} +\newlength{\@belowcaptionskip}\setlength{\@belowcaptionskip}{\z@} + +\setlength{\abovecaptionskip}{\@abovecaptionskip} +\setlength{\belowcaptionskip}{\@belowcaptionskip} + +% swap above/belowcaptionskip lengths for tables +\renewenvironment{table} + {\setlength{\abovecaptionskip}{\@belowcaptionskip}% + \setlength{\belowcaptionskip}{\@abovecaptionskip}% + \@float{table}} + {\end@float} + +% footnote formatting +\setlength{\footnotesep }{6.65\p@} +\setlength{\skip\footins}{9\p@ \@plus 4\p@ \@minus 2\p@} +\renewcommand{\footnoterule}{\kern-3\p@ \hrule width 12pc \kern 2.6\p@} +\setcounter{footnote}{0} + +% paragraph formatting +\setlength{\parindent}{\z@} +\setlength{\parskip }{5.5\p@} + +% list formatting +\setlength{\topsep }{4\p@ \@plus 1\p@ \@minus 2\p@} +\setlength{\partopsep }{1\p@ \@plus 0.5\p@ \@minus 0.5\p@} +\setlength{\itemsep }{2\p@ \@plus 1\p@ \@minus 0.5\p@} +\setlength{\parsep }{2\p@ \@plus 1\p@ \@minus 0.5\p@} +\setlength{\leftmargin }{3pc} +\setlength{\leftmargini }{\leftmargin} +\setlength{\leftmarginii }{2em} +\setlength{\leftmarginiii}{1.5em} +\setlength{\leftmarginiv }{1.0em} +\setlength{\leftmarginv }{0.5em} +\def\@listi {\leftmargin\leftmargini} +\def\@listii {\leftmargin\leftmarginii + \labelwidth\leftmarginii + \advance\labelwidth-\labelsep + \topsep 2\p@ \@plus 1\p@ \@minus 0.5\p@ + \parsep 1\p@ \@plus 0.5\p@ \@minus 0.5\p@ + \itemsep \parsep} +\def\@listiii{\leftmargin\leftmarginiii + \labelwidth\leftmarginiii + \advance\labelwidth-\labelsep + \topsep 1\p@ \@plus 0.5\p@ \@minus 0.5\p@ + \parsep \z@ + \partopsep 0.5\p@ \@plus 0\p@ \@minus 0.5\p@ + \itemsep \topsep} +\def\@listiv {\leftmargin\leftmarginiv + \labelwidth\leftmarginiv + \advance\labelwidth-\labelsep} +\def\@listv {\leftmargin\leftmarginv + \labelwidth\leftmarginv + \advance\labelwidth-\labelsep} +\def\@listvi {\leftmargin\leftmarginvi + \labelwidth\leftmarginvi + \advance\labelwidth-\labelsep} + +% create title +\providecommand{\maketitle}{} +\renewcommand{\maketitle}{% + \par + \begingroup + \renewcommand{\thefootnote}{\fnsymbol{footnote}} + % for perfect author name centering + \renewcommand{\@makefnmark}{\hbox to \z@{$^{\@thefnmark}$\hss}} + % The footnote-mark was overlapping the footnote-text, + % added the following to fix this problem (MK) + \long\def\@makefntext##1{% + \parindent 1em\noindent + \hbox to 1.8em{\hss $\m@th ^{\@thefnmark}$}##1 + } + \thispagestyle{empty} + \@maketitle + \@thanks + %\@notice + \endgroup + \let\maketitle\relax + \let\thanks\relax +} + +% rules for title box at top of first page +\newcommand{\@toptitlebar}{ + \hrule height 2\p@ + \vskip 0.25in + \vskip -\parskip% +} +\newcommand{\@bottomtitlebar}{ + \vskip 0.29in + \vskip -\parskip + \hrule height 2\p@ + \vskip 0.09in% +} + +% create title (includes both anonymized and non-anonymized versions) +\providecommand{\@maketitle}{} +\renewcommand{\@maketitle}{% + \vbox{% + \hsize\textwidth + \linewidth\hsize + \vskip 0.1in + \@toptitlebar + \centering + {\LARGE\sc \@title\par} + \@bottomtitlebar + \textsc{\undertitle}\\ + \vskip 0.1in + \def\And{% + \end{tabular}\hfil\linebreak[0]\hfil% + \begin{tabular}[t]{c}\bf\rule{\z@}{24\p@}\ignorespaces% + } + \def\AND{% + \end{tabular}\hfil\linebreak[4]\hfil% + \begin{tabular}[t]{c}\bf\rule{\z@}{24\p@}\ignorespaces% + } + \begin{tabular}[t]{c}\bf\rule{\z@}{24\p@}\@author\end{tabular}% + \vskip 0.4in \@minus 0.1in \center{\@date} \vskip 0.2in + } +} + +% add conference notice to bottom of first page +\newcommand{\ftype@noticebox}{8} +\newcommand{\@notice}{% + % give a bit of extra room back to authors on first page + \enlargethispage{2\baselineskip}% + \@float{noticebox}[b]% + \footnotesize\@noticestring% + \end@float% +} + +% abstract styling +\renewenvironment{abstract} +{ + \centerline + {\large \bfseries \scshape Abstract} + \begin{quote} +} +{ + \end{quote} +} + +\endinput diff --git a/references/2020.arxiv.the/source/paclic34.sty b/references/2020.arxiv.the/source/paclic34.sty new file mode 100644 index 0000000000000000000000000000000000000000..350bfcd96174563de14eb563388a24ccf7cdfadb --- /dev/null +++ b/references/2020.arxiv.the/source/paclic34.sty @@ -0,0 +1,345 @@ +% File paclic34.sty +% Contact: paclic34@gmail.com +% +% This is the LaTeX style file for PACLIC 34. It is virtually identical to the +% style file for ACL 2012. +% +% This is the LaTeX style file for ACL 2005. It is nearly identical to the +% style files for ACL 2002, ACL 2001, ACL 2000, EACL 95 and EACL +% 99. +% +% This is the LaTeX style file for ACL 2000. It is nearly identical to the +% style files for EACL 95 and EACL 99. Minor changes include editing the +% instructions to reflect use of \documentclass rather than \documentstyle +% and removing the white space before the title on the first page +% -- John Chen, June 29, 2000 + +% To convert from submissions prepared using the style file aclsub.sty +% prepared for the ACL 2000 conference, proceed as follows: +% 1) Remove submission-specific information: \whichsession, \id, +% \wordcount, \otherconferences, \area, \keywords +% 2) \summary should be removed. The summary material should come +% after \maketitle and should be in the ``abstract'' environment +% 3) Check all citations. This style should handle citations correctly +% and also allows multiple citations separated by semicolons. +% 4) Check figures and examples. Because the final format is double- +% column, some adjustments may have to be made to fit text in the column +% or to choose full-width (\figure*} figures. +% 5) Change the style reference from aclsub to acl2000, and be sure +% this style file is in your TeX search path + +% This is the LaTeX style file for EACL-95. It is identical to the +% style file for ANLP '94 except that the margins are adjusted for A4 +% paper. -- abney 13 Dec 94 + +% The ANLP '94 style file is a slightly modified +% version of the style used for AAAI and IJCAI, using some changes +% prepared by Fernando Pereira and others and some minor changes +% by Paul Jacobs. + +% Papers prepared using the aclsub.sty file and acl.bst bibtex style +% should be easily converted to final format using this style. +% (1) Submission information (\wordcount, \subject, and \makeidpage) +% should be removed. +% (2) \summary should be removed. The summary material should come +% after \maketitle and should be in the ``abstract'' environment +% (between \begin{abstract} and \end{abstract}). +% (3) Check all citations. This style should handle citations correctly +% and also allows multiple citations separated by semicolons. +% (4) Check figures and examples. Because the final format is double- +% column, some adjustments may have to be made to fit text in the column +% or to choose full-width (\figure*} figures. + +% Place this in a file called aclap.sty in the TeX search path. +% (Placing it in the same directory as the paper should also work.) + +% Prepared by Peter F. Patel-Schneider, liberally using the ideas of +% other style hackers, including Barbara Beeton. +% This style is NOT guaranteed to work. It is provided in the hope +% that it will make the preparation of papers easier. +% +% There are undoubtably bugs in this style. If you make bug fixes, +% improvements, etc. please let me know. My e-mail address is: +% pfps@research.att.com + +% Papers are to be prepared using the ``acl'' bibliography style, +% as follows: +% \documentclass[11pt]{article} +% \usepackage{acl2000} +% \title{Title} +% \author{Author 1 \and Author 2 \\ Address line \\ Address line \And +% Author 3 \\ Address line \\ Address line} +% \begin{document} +% ... +% \bibliography{bibliography-file} +% \bibliographystyle{acl} +% \end{document} + +% Author information can be set in various styles: +% For several authors from the same institution: +% \author{Author 1 \and ... \and Author n \\ +% Address line \\ ... \\ Address line} +% if the names do not fit well on one line use +% Author 1 \\ {\bf Author 2} \\ ... \\ {\bf Author n} \\ +% For authors from different institutions: +% \author{Author 1 \\ Address line \\ ... \\ Address line +% \And ... \And +% Author n \\ Address line \\ ... \\ Address line} +% To start a seperate ``row'' of authors use \AND, as in +% \author{Author 1 \\ Address line \\ ... \\ Address line +% \AND +% Author 2 \\ Address line \\ ... \\ Address line \And +% Author 3 \\ Address line \\ ... \\ Address line} + +% If the title and author information does not fit in the area allocated, +% place \setlength\titlebox{} right after +% \usepackage{acl2000} +% where can be something larger than 2.25in + +% \typeout{Conference Style for ACL 2000 -- released June 20, 2000} +% \typeout{Conference Style for ACL 2005 -- released Octobe 11, 2004} +\typeout{Conference Style for PACLIC 34} + +% NOTE: Some laser printers have a serious problem printing TeX output. +% These printing devices, commonly known as ``write-white'' laser +% printers, tend to make characters too light. To get around this +% problem, a darker set of fonts must be created for these devices. +% + +% Physical page layout - slightly modified from IJCAI by pj +\setlength\topmargin{0.0in} \setlength\oddsidemargin{-0.0in} +\setlength\textheight{9.0in} \setlength\textwidth{6.5in} +\setlength\columnsep{0.2in} +\newlength\titlebox +\setlength\titlebox{2.25in} +\setlength\headheight{0pt} \setlength\headsep{0pt} +%\setlength\footheight{0pt} +\setlength\footskip{0pt} +\thispagestyle{empty} \pagestyle{empty} +\flushbottom \twocolumn \sloppy + +% A4 version of page layout +% \setlength\topmargin{-0.45cm} % changed by Rz -1.4 +% \setlength\oddsidemargin{.8mm} % was -0cm, changed by Rz +% \setlength\textheight{23.5cm} +% \setlength\textwidth{15.8cm} +% \setlength\columnsep{0.6cm} +% \newlength\titlebox +% \setlength\titlebox{2.00in} +% \setlength\headheight{5pt} +% \setlength\headsep{0pt} +% \setlength\footheight{0pt} +% \setlength\footskip{0pt} +% \thispagestyle{empty} +% \pagestyle{empty} + +% \flushbottom \twocolumn \sloppy + +% We're never going to need a table of contents, so just flush it to +% save space --- suggested by drstrip@sandia-2 +\def\addcontentsline#1#2#3{} + +% Title stuff, taken from deproc. +\def\maketitle{\par + \begingroup + \def\thefootnote{\fnsymbol{footnote}} + \def\@makefnmark{\hbox to 0pt{$^{\@thefnmark}$\hss}} + \twocolumn[\@maketitle] \@thanks + \endgroup + \setcounter{footnote}{0} + \let\maketitle\relax \let\@maketitle\relax + \gdef\@thanks{}\gdef\@author{}\gdef\@title{}\let\thanks\relax} +\def\@maketitle{\vbox to \titlebox{\hsize\textwidth + \linewidth\hsize \vskip 0.125in minus 0.125in \centering + {\Large\bf \@title \par} \vskip 0.2in plus 1fil minus 0.1in + {\def\and{\unskip\enspace{\rm and}\enspace}% + \def\And{\end{tabular}\hss \egroup \hskip 1in plus 2fil + \hbox to 0pt\bgroup\hss \begin{tabular}[t]{c}\bf}% + \def\AND{\end{tabular}\hss\egroup \hfil\hfil\egroup + \vskip 0.25in plus 1fil minus 0.125in + \hbox to \linewidth\bgroup\large \hfil\hfil + \hbox to 0pt\bgroup\hss \begin{tabular}[t]{c}\bf} + \hbox to \linewidth\bgroup\large \hfil\hfil + \hbox to 0pt\bgroup\hss \begin{tabular}[t]{c}\bf\@author + \end{tabular}\hss\egroup + \hfil\hfil\egroup} + \vskip 0.3in plus 2fil minus 0.1in +}} +\renewenvironment{abstract}{\centerline{\large\bf + Abstract}\vspace{0.5ex}\begin{quote} \small}{\par\end{quote}\vskip 1ex} + + +% bibliography + +\def\thebibliography#1{\section*{References} + \global\def\@listi{\leftmargin\leftmargini + \labelwidth\leftmargini \advance\labelwidth-\labelsep + \topsep 1pt plus 2pt minus 1pt + \parsep 0.25ex plus 1pt \itemsep 0.25ex plus 1pt} + \list {[\arabic{enumi}]}{\settowidth\labelwidth{[#1]}\leftmargin\labelwidth + \advance\leftmargin\labelsep\usecounter{enumi}} + \def\newblock{\hskip .11em plus .33em minus -.07em} + \sloppy + \sfcode`\.=1000\relax} + +\def\@up#1{\raise.2ex\hbox{#1}} + +% most of cite format is from aclsub.sty by SMS + +% don't box citations, separate with ; and a space +% also, make the penalty between citations negative: a good place to break +% changed comma back to semicolon pj 2/1/90 +% \def\@citex[#1]#2{\if@filesw\immediate\write\@auxout{\string\citation{#2}}\fi +% \def\@citea{}\@cite{\@for\@citeb:=#2\do +% {\@citea\def\@citea{;\penalty\@citeseppen\ }\@ifundefined +% {b@\@citeb}{{\bf ?}\@warning +% {Citation `\@citeb' on page \thepage \space undefined}}% +% {\csname b@\@citeb\endcsname}}}{#1}} + +% don't box citations, separate with ; and a space +% Replaced for multiple citations (pj) +% don't box citations and also add space, semicolon between multiple citations +\def\@citex[#1]#2{\if@filesw\immediate\write\@auxout{\string\citation{#2}}\fi + \def\@citea{}\@cite{\@for\@citeb:=#2\do + {\@citea\def\@citea{; }\@ifundefined + {b@\@citeb}{{\bf ?}\@warning + {Citation `\@citeb' on page \thepage \space undefined}}% + {\csname b@\@citeb\endcsname}}}{#1}} + +% Allow short (name-less) citations, when used in +% conjunction with a bibliography style that creates labels like +% \citename{, } +% +\let\@internalcite\cite +\def\cite{\def\citename##1{##1, }\@internalcite} +\def\shortcite{\def\citename##1{}\@internalcite} +\def\newcite{\def\citename##1{{\frenchspacing##1} (}\@internalciteb} + +% Macros for \newcite, which leaves name in running text, and is +% otherwise like \shortcite. +\def\@citexb[#1]#2{\if@filesw\immediate\write\@auxout{\string\citation{#2}}\fi + \def\@citea{}\@newcite{\@for\@citeb:=#2\do + {\@citea\def\@citea{;\penalty\@m\ }\@ifundefined + {b@\@citeb}{{\bf ?}\@warning + {Citation `\@citeb' on page \thepage \space undefined}}% +{\csname b@\@citeb\endcsname}}}{#1}} +\def\@internalciteb{\@ifnextchar [{\@tempswatrue\@citexb}{\@tempswafalse\@citexb[]}} + +\def\@newcite#1#2{{#1\if@tempswa, #2\fi)}} + +\def\@biblabel#1{\def\citename##1{##1}[#1]\hfill} + +%%% More changes made by SMS (originals in latex.tex) +% Use parentheses instead of square brackets in the text. +\def\@cite#1#2{({#1\if@tempswa , #2\fi})} + +% Don't put a label in the bibliography at all. Just use the unlabeled format +% instead. +\def\thebibliography#1{\small\vskip\parskip% +\vskip\baselineskip% +\def\baselinestretch{1}% +\ifx\@currsize\normalsize\@normalsize\else\@currsize\fi% +\vskip-\parskip% +\vskip-\baselineskip% +\section*{References\@mkboth + {References}{References}}\list + {}{\setlength{\labelwidth}{0pt}\setlength{\leftmargin}{\parindent} + \setlength{\itemsep}{-0.5ex} + \setlength{\itemindent}{-\parindent}} + \def\newblock{\hskip .11em plus .33em minus -.07em} + \sloppy\clubpenalty4000\widowpenalty4000 + \sfcode`\.=1000\relax} +\let\endthebibliography=\endlist + +% Allow for a bibliography of sources of attested examples +\def\thesourcebibliography#1{\vskip\parskip% +\vskip\baselineskip% +\def\baselinestretch{1}% +\ifx\@currsize\normalsize\@normalsize\else\@currsize\fi% +\vskip-\parskip% +\vskip-\baselineskip% +\section*{Sources of Attested Examples\@mkboth + {Sources of Attested Examples}{Sources of Attested Examples}}\list + {}{\setlength{\labelwidth}{0pt}\setlength{\leftmargin}{\parindent} + \setlength{\itemindent}{-\parindent}} + \def\newblock{\hskip .11em plus .33em minus -.07em} + \sloppy\clubpenalty4000\widowpenalty4000 + \sfcode`\.=1000\relax} +\let\endthesourcebibliography=\endlist + +\def\@lbibitem[#1]#2{\item[]\if@filesw + { \def\protect##1{\string ##1\space}\immediate + \write\@auxout{\string\bibcite{#2}{#1}}\fi\ignorespaces}} + +\def\@bibitem#1{\item\if@filesw \immediate\write\@auxout + {\string\bibcite{#1}{\the\c@enumi}}\fi\ignorespaces} + +% sections with less space +\def\section{\@startsection {section}{1}{\z@}{-2.0ex plus + -0.5ex minus -.2ex}{1.5ex plus 0.3ex minus .2ex}{\large\bf\raggedright}} +\def\subsection{\@startsection{subsection}{2}{\z@}{-1.8ex plus + -0.5ex minus -.2ex}{0.8ex plus .2ex}{\normalsize\bf\raggedright}} +\def\subsubsection{\@startsection{subsubsection}{3}{\z@}{1.5ex plus + 0.5ex minus .2ex}{0.5ex plus .2ex}{\normalsize\bf\raggedright}} +\def\paragraph{\@startsection{paragraph}{4}{\z@}{1.5ex plus + 0.5ex minus .2ex}{-1em}{\normalsize\bf}} +\def\subparagraph{\@startsection{subparagraph}{5}{\parindent}{1.5ex plus + 0.5ex minus .2ex}{-1em}{\normalsize\bf}} + +% Footnotes +\footnotesep 6.65pt % +\skip\footins 9pt plus 4pt minus 2pt +\def\footnoterule{\kern-3pt \hrule width 5pc \kern 2.6pt } +\setcounter{footnote}{0} + +% Lists and paragraphs +\parindent 1em +\topsep 4pt plus 1pt minus 2pt +\partopsep 1pt plus 0.5pt minus 0.5pt +\itemsep 2pt plus 1pt minus 0.5pt +\parsep 2pt plus 1pt minus 0.5pt + +\leftmargin 2em \leftmargini\leftmargin \leftmarginii 2em +\leftmarginiii 1.5em \leftmarginiv 1.0em \leftmarginv .5em \leftmarginvi .5em +\labelwidth\leftmargini\advance\labelwidth-\labelsep \labelsep 5pt + +\def\@listi{\leftmargin\leftmargini} +\def\@listii{\leftmargin\leftmarginii + \labelwidth\leftmarginii\advance\labelwidth-\labelsep + \topsep 2pt plus 1pt minus 0.5pt + \parsep 1pt plus 0.5pt minus 0.5pt + \itemsep \parsep} +\def\@listiii{\leftmargin\leftmarginiii + \labelwidth\leftmarginiii\advance\labelwidth-\labelsep + \topsep 1pt plus 0.5pt minus 0.5pt + \parsep \z@ \partopsep 0.5pt plus 0pt minus 0.5pt + \itemsep \topsep} +\def\@listiv{\leftmargin\leftmarginiv + \labelwidth\leftmarginiv\advance\labelwidth-\labelsep} +\def\@listv{\leftmargin\leftmarginv + \labelwidth\leftmarginv\advance\labelwidth-\labelsep} +\def\@listvi{\leftmargin\leftmarginvi + \labelwidth\leftmarginvi\advance\labelwidth-\labelsep} + +\abovedisplayskip 7pt plus2pt minus5pt% +\belowdisplayskip \abovedisplayskip +\abovedisplayshortskip 0pt plus3pt% +\belowdisplayshortskip 4pt plus3pt minus3pt% + +% Less leading in most fonts (due to the narrow columns) +% The choices were between 1-pt and 1.5-pt leading +%\def\@normalsize{\@setsize\normalsize{11pt}\xpt\@xpt} +%\def\small{\@setsize\small{10pt}\ixpt\@ixpt} +%\def\footnotesize{\@setsize\footnotesize{10pt}\ixpt\@ixpt} +%\def\scriptsize{\@setsize\scriptsize{8pt}\viipt\@viipt} +%\def\tiny{\@setsize\tiny{7pt}\vipt\@vipt} +%\def\large{\@setsize\large{14pt}\xiipt\@xiipt} +%\def\Large{\@setsize\Large{16pt}\xivpt\@xivpt} +%\def\LARGE{\@setsize\LARGE{20pt}\xviipt\@xviipt} +%\def\huge{\@setsize\huge{23pt}\xxpt\@xxpt} +%\def\Huge{\@setsize\Huge{28pt}\xxvpt\@xxvpt} + +\let\@@makecaption\@makecaption +\renewcommand{\@makecaption}[1]{\@@makecaption{\small #1}} + +\newcommand{\Thanks}[1]{\thanks{\ #1}} diff --git a/references/2020.arxiv.the/source/references.bib b/references/2020.arxiv.the/source/references.bib new file mode 100644 index 0000000000000000000000000000000000000000..ed21fe15294a2ffc746767b4a527277ad966040b --- /dev/null +++ b/references/2020.arxiv.the/source/references.bib @@ -0,0 +1,252 @@ +@InProceedings{Devlin:2019, + author = {Jacob Devlin and Ming-Wei Chang and Kenton Lee and + Kristina Toutanova}, + title = {{BERT}: Pre-training of deep bidirectional + transformers for language understanding}, + booktitle = {Proceedings of NAACL}, + pages = {1--16}, + year = 2019, + address = {Minnesota, USA} +} + +@InProceedings{Peters:2018, + author = {Matthew E. Peters and Mark Neumann and Mohit Iyyer + and Matt Gardner and Christopher Clark and Kenton + Lee and Luke Zettlemoyer}, + title = {Deep contextualized word representations}, + booktitle = {Proceedings of NAACL}, + pages = {1--15}, + year = 2018, + address = {Louisiana, USA} +} + +@Article{Nguyen:2018, + author = {Nguyen Thi Minh Huyen and Ngo The Quyen and Vu Xuan + Luong and Tran Mai Vu and Nguyen Thi Thu Hien}, + title = {{VLSP} Shared Task: Named Entity Recognition}, + journal = {Journal of Computer Science and Cybernetics}, + year = 2018, + volume = 34, + number = 4, + pages = {283--294} +} + +@InProceedings{Yang:2019, + author = {Zhilin Yang and Zihang Dai and Yiming Yang and Jaime + Carbonell and Ruslan Salakhutdinov and Quoc V. Le}, + title = {{XLN}et: Generalized autoregressive pretraining for + language understanding}, + booktitle = {Proceedings of NeurIPS}, + year = 2019, + pages = {5754--5764} +} + +@InProceedings{Clark:2020, + author = {Kevin Clark and Minh-Thang Luong and Quoc V. Le and + Christopher D. Manning}, + title = {{ELECTRA}: Pre-training Text Encoders as + Discriminators Rather than Generators}, + booktitle = {Proceedings of ICLR}, + year = 2020 +} + +@InProceedings{Radford:2018, + author = {Alec Radford and Karthik Narasimhan and Tim Salimans + and Ilya Sutskever}, + title = {Improving language understanding by generative + pre-training}, + booktitle = {Preprint}, + year = 2018 +} + +@InProceedings{Liu:2019, + author = {Yinhan Liu and Myle Ott and Naman Goyal and Jingfei + Du and Mandar Joshi and Danqi Chen and Omer Levy and + Mike Lewis and Luke Zettlemoyer and Veselin + Stoyanov}, + title = {{R}o{BERT}a: A Robustly Optimized {BERT} Pretraining + Approach}, + booktitle = {Preprint}, + year = 2019 +} + +@InProceedings{Vaswani:2017, + author = {Ashish Vaswani and Noam Shazeer and Niki Parmar and + Jakob Uszkoreit and Llion Jones and Aidan N. Gomez + and Łukasz Kaiser and Illia Polosukhin}, + title = {Attention is all you need}, + booktitle = {Proceedings of NIPS}, + year = 2017} + +\bibtex +@InProceedings{Vu:2019, + author = {Xuan-Son Vu and Thanh Vu and Son Tran and Lili Jiang}, + title = { {ETNLP}: A visual-aided systematic approach to select pre-trained embeddings for a downstream task}, + booktitle = {In Proceedings of RANLP}, + year = 2019, + pages = {1285--1294} +} + + +@InProceedings{Vu:2018, + author = {Thanh Vu and Dat Quoc Nguyen and Dai Quoc Nguyen and + MarkDras and Mark Johnson}, + title = {Vn{C}ore{NLP}: A {V}ietnamese Natural Language Processing + Toolkit}, + booktitle = {In Proceedings of NAACL: Demonstrations}, + year = 2018, + pages = {56--60} +} + + + +@InProceedings{NguyenKA:2019, + author = {Kim Anh Nguyen and Ngan Dong and and Cam-Tu Nguyen}, + title = {Attentive Neural Network for Named Entity Recognition in {V}ietnamese}, + booktitle = {In Proceedings of RIVF}, + year = 2019 , + pages = {} +} + +@InProceedings{Luong:2018, + author = {Viet-Thang Luong and Long Kim Pham}, + title = {{ZA-NER}: Vietnamese Named Entity Recognition at {VLSP} 2018 Evaluation Campaign}, + booktitle = {In the proceedings of VLSP workshop 2018}, + year = 2018 , + pages = {} +} + +@InProceedings{Nguyen:2020, + author = {Dat Quoc Nguyen and Anh Tuan Nguyen}, + title = {Pho{BERT}: Pre-trained language models for {V}ietnamese}, + booktitle = {https://arxiv.org/pdf/2003.00744.pdf}, + year = 2020, + pages = {} +} + + + +@InProceedings{Nguyen:2019, + author = {Dat Quoc Nguyen}, + title = {A neural joint model for {V}ietnamese word + segmentation, {POS} tagging and dependency parsing}, + booktitle = {In Proceedings of ALTA}, + year = 2019, + pages = {28--34} +} + + +@InProceedings{Nguyen:2017, + author = {Dat Quoc Nguyen and Thanh Vu and Dai Quoc Nguyen and + MarkDras and Mark Johnson}, + title = { From word segmen-tation to {POS} tagging for + {V}ietnamese}, + booktitle = {In Proceedings of ALTA}, + year = 2017, + pages = {108--113} +} + +@InProceedings{Nguyen:2014, + author = { Dat Quoc Nguyen and Dai Quoc Nguyen and Dang Duc + Pham,and Son Bao Pham}, + title = {{RDRPOST}agger: A Ripple Down Rules-based + Part-Of-Speech Tagger}, + booktitle = {In Proceedings of the Demonstrations at EACL}, + year = 2014, + pages = {17--20} +} + +@InProceedings{Ma:2016, + author = {Xuezhe Ma and Eduard Hovy}, + title = {End-to-end sequence labeling via bi-directional {LSTM-CNN}s-{CRF}}, + booktitle = {In Proceedings of ACL}, + year = 2016, + pages = {1064--1074} +} + +@InProceedings{Le:2010, + author = {Phuong Le-Hong and Azim Roussanaly and Thi Minh + Huyen Nguyen and Mathias Rossignol}, + title = {An empirical studyof maximum entropy approach for + part-of-speech tagging of {V}ietnamese texts}, + booktitle = {Traitement Automatique des Langues Naturelles -- + TALN, Jul 2010, Montréal, Canada}, + year = 2010, + pages = {1--12} +} + +@InProceedings{Kingma:2015, + author = {Diederik Kingma and Jimmy Ba}, + title = {Adam: A method for stochastic optimization}, + booktitle = {Proceedings of the International Conference on + Learning Representations (ICLR)}, + year = 2015 +} + + +@InProceedings{Pham:2017a, + author = {Thai Hoang Pham and Phuong Le-Hong}, + title = {End-to-end Recurrent Neural Network Models for + {V}ietnamese Named Entity Recognition: Word-level + vs. Character-level}, + booktitle = {PACLING - Conference of the Pacific Association of + Computational Linguistics}, + year = 2017, + pages = {219--232} +} + +@InProceedings{Pham:2017b, + author = {Thai Hoang Pham and Xuan Khoai Pham and Tuan Anh + Nguyen and Phuong Le-Hong}, + title = {NNVLP: A Neural Network-Based {V}ietnamese Language + Processing Toolkit}, + booktitle = {The 8th International Joint Conference on Natural + Language Processing (IJCNLP 2017). Demonstration + Paper}, + year = 2017, + pages = {} +} + +@InProceedings{Pham:2017c, + author = {Thai Hoang Pham and Phuong Le-Hong}, + title = {The Importance of Automatic Syntactic Features in + {V}ietnamese Named Entity Recognition}, + booktitle = {The 31st Pacific Asia Conference on Language, + Information and Computation PACLIC 31 (2017)}, + year = 2017, + pages = {97--103} +} + + +@InProceedings{Le:2016, + author = {Phuong Le-Hong}, + title = {Vietnamese Named Entity Recognition using Token + Regular Expressions and Bidirectional Inference}, + booktitle = {VLSP NER Evaluation Campaign}, + year = 2016, + pages = {} +} + + +@InProceedings{Luong:2019, + author = {Chi-Tho Luong and Phuong Le-Hong}, + title = {Towards Task-Oriented Dialogue in Mixed Domains}, + booktitle = {Proceedings of the International Conference of the + Pacific Association for Computational Linguistics}, + year = 2019, + pages = {267--266}, + publisher = {Springer, Singapore}, + note = {DOI: https://doi.org/10.1007/978-981-15-6168-9\_22} + } + +@InCollection{Le:2015c, + title = {Fast Dependency Parsing using Distributed Word + Representations}, + author = {Phuong Le-Hong and Thi-Minh-Huyen Nguyen and + Thi-Luong Nguyen and My-Linh Ha}, + booktitle = {Trends and Applications in Knowledge Discovery and Data Mining}, + volume = {9441}, + series = {LNAI}, + year = {2015}, + publisher = {Springer} +} diff --git a/references/2020.arxiv.the/source/vibert.bbl b/references/2020.arxiv.the/source/vibert.bbl new file mode 100644 index 0000000000000000000000000000000000000000..c9abc34bc568c627bc0f2c9989d5831415e81659 --- /dev/null +++ b/references/2020.arxiv.the/source/vibert.bbl @@ -0,0 +1,179 @@ +\begin{thebibliography}{} + +\bibitem[\protect\citename{Clark \bgroup et al.\egroup }2020]{Clark:2020} +Kevin Clark, Minh-Thang Luong, Quoc~V. Le, and Christopher~D. Manning. +\newblock 2020. +\newblock {ELECTRA}: Pre-training text encoders as discriminators rather than + generators. +\newblock In {\em Proceedings of ICLR}. + +\bibitem[\protect\citename{Devlin \bgroup et al.\egroup }2019]{Devlin:2019} +Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. +\newblock 2019. +\newblock {BERT}: Pre-training of deep bidirectional transformers for language + understanding. +\newblock In {\em Proceedings of NAACL}, pages 1--16, Minnesota, USA. + +\bibitem[\protect\citename{Huyen \bgroup et al.\egroup }2018]{Nguyen:2018} +Nguyen Thi~Minh Huyen, Ngo~The Quyen, Vu~Xuan Luong, Tran~Mai Vu, and Nguyen + Thi~Thu Hien. +\newblock 2018. +\newblock {VLSP} shared task: Named entity recognition. +\newblock {\em Journal of Computer Science and Cybernetics}, 34(4):283--294. + +\bibitem[\protect\citename{Kingma and Ba}2015]{Kingma:2015} +Diederik Kingma and Jimmy Ba. +\newblock 2015. +\newblock Adam: A method for stochastic optimization. +\newblock In {\em Proceedings of the International Conference on Learning + Representations (ICLR)}. + +\bibitem[\protect\citename{Le-Hong \bgroup et al.\egroup }2010]{Le:2010} +Phuong Le-Hong, Azim Roussanaly, Thi Minh~Huyen Nguyen, and Mathias Rossignol. +\newblock 2010. +\newblock An empirical studyof maximum entropy approach for part-of-speech + tagging of {V}ietnamese texts. +\newblock In {\em Traitement Automatique des Langues Naturelles -- TALN, Jul + 2010, Montréal, Canada}, pages 1--12. + +\bibitem[\protect\citename{Le-Hong \bgroup et al.\egroup }2015]{Le:2015c} +Phuong Le-Hong, Thi-Minh-Huyen Nguyen, Thi-Luong Nguyen, and My-Linh Ha. +\newblock 2015. +\newblock Fast dependency parsing using distributed word representations. +\newblock In {\em Trends and Applications in Knowledge Discovery and Data + Mining}, volume 9441 of {\em LNAI}. Springer. + +\bibitem[\protect\citename{Le-Hong}2016]{Le:2016} +Phuong Le-Hong. +\newblock 2016. +\newblock Vietnamese named entity recognition using token regular expressions + and bidirectional inference. +\newblock In {\em VLSP NER Evaluation Campaign}. + +\bibitem[\protect\citename{Liu \bgroup et al.\egroup }2019]{Liu:2019} +Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer + Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. +\newblock 2019. +\newblock {R}o{BERT}a: A robustly optimized {BERT} pretraining approach. +\newblock In {\em Preprint}. + +\bibitem[\protect\citename{Luong and Le-Hong}2019]{Luong:2019} +Chi-Tho Luong and Phuong Le-Hong. +\newblock 2019. +\newblock Towards task-oriented dialogue in mixed domains. +\newblock In {\em Proceedings of the International Conference of the Pacific + Association for Computational Linguistics}, pages 267--266. 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Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, + Kenton Lee, and Luke Zettlemoyer. +\newblock 2018. +\newblock Deep contextualized word representations. +\newblock In {\em Proceedings of NAACL}, pages 1--15, Louisiana, USA. + +\bibitem[\protect\citename{Pham and Le-Hong}2017a]{Pham:2017a} +Thai~Hoang Pham and Phuong Le-Hong. +\newblock 2017a. +\newblock End-to-end recurrent neural network models for {V}ietnamese named + entity recognition: Word-level vs. character-level. +\newblock In {\em PACLING - Conference of the Pacific Association of + Computational Linguistics}, pages 219--232. + +\bibitem[\protect\citename{Pham and Le-Hong}2017b]{Pham:2017c} +Thai~Hoang Pham and Phuong Le-Hong. +\newblock 2017b. +\newblock The importance of automatic syntactic features in {V}ietnamese named + entity recognition. +\newblock In {\em The 31st Pacific Asia Conference on Language, Information and + Computation PACLIC 31 (2017)}, pages 97--103. + +\bibitem[\protect\citename{Pham \bgroup et al.\egroup }2017]{Pham:2017b} +Thai~Hoang Pham, Xuan~Khoai Pham, Tuan~Anh Nguyen, and Phuong Le-Hong. +\newblock 2017. +\newblock Nnvlp: A neural network-based {V}ietnamese language processing + toolkit. +\newblock In {\em The 8th International Joint Conference on Natural Language + Processing (IJCNLP 2017). 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Gomez, Łukasz Kaiser, and Illia Polosukhin. +\newblock 2017. +\newblock Attention is all you need. +\newblock In {\em Proceedings of NIPS}. + +\bibitem[\protect\citename{Vu \bgroup et al.\egroup }2018]{Vu:2018} +Thanh Vu, Dat~Quoc Nguyen, Dai~Quoc Nguyen, MarkDras, and Mark Johnson. +\newblock 2018. +\newblock Vn{C}ore{NLP}: A {V}ietnamese natural language processing toolkit. +\newblock In {\em In Proceedings of NAACL: Demonstrations}, pages 56--60. + +\bibitem[\protect\citename{Vu \bgroup et al.\egroup }2019]{Vu:2019} +Xuan-Son Vu, Thanh Vu, Son Tran, and Lili Jiang. +\newblock 2019. +\newblock {ETNLP}: A visual-aided systematic approach to select pre-trained + embeddings for a downstream task. +\newblock In {\em In Proceedings of RANLP}, pages 1285--1294. + +\bibitem[\protect\citename{Yang \bgroup et al.\egroup }2019]{Yang:2019} +Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, + and Quoc~V. Le. +\newblock 2019. +\newblock {XLN}et: Generalized autoregressive pretraining for language + understanding. +\newblock In {\em Proceedings of NeurIPS}, pages 5754--5764. + +\end{thebibliography} diff --git a/references/2020.arxiv.the/source/vibert.tex b/references/2020.arxiv.the/source/vibert.tex new file mode 100644 index 0000000000000000000000000000000000000000..9c99adfa92fdf0e170204f22e10e11c77152f348 --- /dev/null +++ b/references/2020.arxiv.the/source/vibert.tex @@ -0,0 +1,740 @@ +\documentclass[11pt]{article} +\usepackage{paclic34} +\usepackage{times} +\usepackage{latexsym} +\usepackage{amsmath} +\usepackage{multirow} +\usepackage{url} + +\setlength\titlebox{6.5cm} % Expanding the titlebox + +\usepackage{graphicx} +\usepackage[utf8]{vietnam} +\usepackage{tikz} +\usepackage{pgfplots} +\usetikzlibrary{fit} +\usetikzlibrary{arrows,shapes} +\usetikzlibrary{shapes.misc} + + +\title{Improving Sequence Tagging for Vietnamese Text using + Transformer-based Neural Models} + +\author{ + The Viet Bui$^{1}$\\ + {\tt vietbt6@fpt.com.vn} \\ \And + Thi Oanh Tran$^{1,2}$\\ + {\tt oanhtt@isvnu.vn} \\ \AND + Phuong Le-Hong$^{1,2}$\\ + {\tt phuonglh@vnu.edu.vn}\\ \\ + $^1$ FPT Technology Research Institute, + FPT University, Hanoi, Vietnam\\ + $^2$ Vietnam National University, Hanoi, Vietnam\\ +} + +\date{} + +\DeclareMathOperator*{\argmax}{arg\,max} +\DeclareMathOperator*{\argmin}{arg\,min} +\DeclareMathOperator{\yy}{\mathbf y} +\DeclareMathOperator{\oo}{\mathbf o} +\DeclareMathOperator{\xx}{\mathbf x} +\DeclareMathOperator{\hh}{\mathbf h} +\DeclareMathOperator{\RR}{\mathbb R} +\DeclareMathOperator{\EE}{\mathbb E} + +% database icon in Tikz +\usetikzlibrary{shapes.geometric} +\usetikzlibrary{positioning, shadows} + +\usetikzlibrary{calc} + +\newcommand{\matt}[4] +{ + \node[draw, minimum height=4em, minimum width=3em, + fill=purple!30, double copy shadow={shadow xshift=3pt,shadow yshift=3pt, draw} + ] (#1) at (#2,#3) {#4}; + +} + +%\usepackage{multirow} + +\begin{document} + +\captionsenglish + +\maketitle + +\begin{abstract} + This paper describes our study on using mutilingual BERT embeddings + and some new neural models for improving sequence tagging tasks for + the Vietnamese language. We propose new model architectures and + evaluate them extensively on two named entity recognition datasets + of VLSP 2016 and VLSP 2018, and on two part-of-speech tagging + datasets of VLSP 2010 and VLSP 2013. Our proposed models outperform + existing methods and achieve new state-of-the-art results. In + particular, we have pushed the accuracy of part-of-speech + tagging to 95.40\% on the VLSP 2010 corpus, to 96.77\% on the VLSP + 2013 corpus; and the $F_1$ score of named entity recognition to + 94.07\% on the VLSP 2016 corpus, to 90.31\% on the VLSP 2018 + corpus. Our code and pre-trained models viBERT and vELECTRA are + released as open source to facilitate adoption and further research. +\end{abstract} + +\section{Introduction} +\label{sec:introduction} + +Sequence modeling plays a central role in natural language +processing. Many fundamental language processing tasks can be treated +as sequence tagging problems, including part-of-speech tagging and +named-entity recognition. In this paper, we present our study on +adapting and developing the multi-lingual BERT~\cite{Devlin:2019} and +ELECTRA~\cite{Clark:2020} models for improving +Vietnamese part-of-speech tagging (PoS) and named entity recognition +(NER). + +Many natural language processing tasks have been shown to be greatly +benefited from large network pre-trained models. In recent years, +these pre-trained models has led to a series of breakthroughs in +language representation learning~\cite{Radford:2018,Peters:2018,Devlin:2019,Yang:2019,Clark:2020}. Current state-of-the-art +representation learning methods for language can be divided into two +broad approaches, namely \textit{denoising auto-encoders} and +\textit{replaced token detection}. + +In the denoising auto-encoder approach, a small subset of tokens of +the unlabelled input sequence, typically 15\%, is selected; these +tokens are masked (e.g., BERT~\cite{Devlin:2019}), or attended (e.g., +XLNet~\cite{Yang:2019}); and then train the network to recover the +original input. The network is mostly transformer-based models which +learn bidirectional representation. The main disadvantage of these +models is that they often require a substantial compute cost because +only 15\% of the tokens per example is learned while a very large +corpus is usually required for the pre-trained models to be +effective. In the replaced token detection approach, the model learns +to distinguish real input tokens from plausible but synthetically +generated replacements (e.g., ELECTRA~\cite{Clark:2020}) Instead of +masking, this method corrupts the input by replacing some tokens with +samples from a proposal distribution. The network is pre-trained as a +discriminator that predicts for every token whether it is an original +or a replacement. The main advantage of this method is that the model +can learn from all input tokens instead of just the small masked-out +subset. This is therefore much more efficient, requiring less than +$1/4$ of compute cost as compared to RoBERTa~\cite{Liu:2019} and +XLNet~\cite{Yang:2019}. + +Both of the approaches belong to the fine-tuning method in natural +language processing where we first pretrain a model architecture on a +language modeling objective before fine-tuning that same model for a +supervised downstream task. A major advantage of this method is that +few parameters need to be learned from scratch. + +In this paper, we propose some improvements over the recent +transformer-based models to push the state-of-the-arts of two common +sequence labeling tasks for Vietnamese. Our main contributions in this +work are: +\begin{itemize} +\item We propose pre-trained language models for Vietnamese which are + based on BERT and ELECTRA architectures; the models are trained on large + corpora of 10GB and 60GB uncompressed Vietnamese text. +\item We propose the fine-tuning methods by using attentional + recurrent neural networks instead of the original fine-tuning with + linear layers. This improvement helps improve the accuracy of + sequence tagging. +\item Our proposed system achieves new state-of-the-art results on all + the four PoS tagging and NER tasks: achieving 95.04\% of accuracy on + VLSP 2010, 96.77\% of accuracy on VLSP 2013, 94.07\% of $F_1 $ score on NER + 2016, and 90.31\% of $F_1$ score on NER 2018. +\item We release code as open source to facilitate adoption and + further research, including pre-trained models viBERT and vELECTRA. +\end{itemize} + +The remainder of this paper is structured as +follows. Section~\ref{sec:models} presents the methods used in the +current work. Section~\ref{sec:experiments} describes the experimental +results. Finally, Section~\ref{sec:conclusion} concludes the papers +and outlines some directions for future work. + +\section{Models} +\label{sec:models} + +\subsection{BERT Embeddings} + +\subsubsection{BERT} +The basic structure of BERT~\cite{Devlin:2019} (\textit{Bidirectional + Encoder Representations from Transformers}) is summarized on +Figure~\ref{fig:bert} where Trm are transformation and $E_k$ are +embeddings of the $k$-th token. + +\begin{figure}[t] + \centering + \begin{tikzpicture}[x=2.25cm,y=1.5cm] + \tikzstyle{every node} = [rectangle, draw, fill=gray!30] + % (a) + \node[fill=green!60] (a) at (0,4) {$T_1$}; + \node[fill=green!60] (b) at (1,4) {$T_2$}; + \node[fill=none,draw=none] (c) at (2,4) {$\cdots$}; + \node[fill=green!60] (d) at (3,4) {$T_N$}; + \node[circle,fill=blue!60] (xa) at (0,3) {Trm}; + \node[circle,fill=blue!60] (xb) at (1,3) {Trm}; + \node[draw=none,fill=none] (xc) at (2,3) {$\cdots$}; + \node[circle,fill=blue!60] (xd) at (3,3) {Trm}; + \node[circle,fill=blue!60] (ya) at (0,2) {Trm}; + \node[circle,fill=blue!60] (yb) at (1,2) {Trm}; + \node[draw=none,fill=none] (yc) at (2,2) {$\cdots$}; + \node[circle,fill=blue!60] (yd) at (3,2) {Trm}; + \node[draw,fill=yellow!60] (ea) at (0,1) {$E_1$}; + \node[draw,fill=yellow!60] (eb) at (1,1) {$E_2$}; + \node[draw=none,fill=none] (ec) at (2,1) {$\cdots$}; + \node[draw,fill=yellow!60] (ed) at (3,1) {$E_N$}; + \foreach \from/\to in {xa/a, xb/b, xc/c, xd/d, + ya/xa, ya/xb, ya/xc, ya/xd, + yb/xa, yb/xb, yb/xc, yb/xd, + yd/xa, yd/xb, yd/xc, yd/xd, + ea/ya, ea/yb, ea/yc, ea/yd, + eb/ya, eb/yb, eb/yc, eb/yd, + ed/ya, ed/yb, ed/yc, ed/yd} + \draw[->,thick] (\from) -- (\to); + \end{tikzpicture} + \caption{The basic structure of BERT} + \label{fig:bert} +\end{figure} + +In essence, BERT's model architecture is a multilayer bidirectional +Transformer encoder based on the original implementation described +in~\cite{Vaswani:2017}. In this model, each input token of a sentence +is represented by a sum of the corresponding token embedding, its +segment embedding and its position embedding. The WordPiece embeddings +are used; split word pieces are denoted by \#\#. In our experiments, +we use learned positional embedding with supported sequence lengths up +to 256 tokens. + +The BERT model trains a deep bidirectional representation by masking +some percentage of the input tokens at random and then predicting only +those masked tokens. The final hidden vectors corresponding to the +mask tokens are fed into an output softmax over the vocabulary. We use +the whole word masking approach in this work. The masked language +model objective is a cross-entropy loss on predicting the masked +tokens. BERT uniformly selects 15\% of the input tokens for +masking. Of the selected tokens, 80\% are replaced with [MASK], 10\% +are left unchanged, and 10\% are replaced by a randomly selected +vocabulary token. + +\begin{figure*} + \centering + \begin{tikzpicture}[x=1.6cm,y=1.15cm] + \tikzstyle{every node} = [rectangle,draw,fill=gray!30] + \foreach \pos in {0,...,8} { + \node[fill=none,draw=none] (a\pos) at (1*\pos,5) {$\cdots$}; + } + \node (a1) at (1,5) {\texttt{Np}}; + \node (a3) at (3,5) {\texttt{V}}; + % \foreach \pos in {0,...,8} { + % \node[fill=blue!30] (b\pos) at (1*\pos,4) {Linear}; + % } + % \foreach \pos in {0,...,8} { + % \draw[->,thick] (b\pos) -- (a\pos); + % } + \foreach \pos in {0,...,8} { + \matt{m\pos}{\pos}{2.5}{Att}; + } + \foreach \pos in {0,...,8} { + \draw[->,thick] (m\pos) -- (a\pos); + } + \node (c0) at (0,4) {}; + \node (c8) at (8,4) {}; + \node[fill=red!60,align=center,fit={(c0) (c8)}] {}; + \node[fill=none,draw=none] at (4,4) {Linear}; + + \foreach \pos in {0,...,8} { + \node[double,fill=yellow!30] (r\pos) at (\pos,1.2) {RNN}; + } + \foreach \pos in {0,...,7} { + \draw[<->,thick] (r\pos) -- (r\the\numexpr\pos+1\relax); + } + + \node (t0) at (0,-0.8) {\texttt{[CLS]}}; + \node (t1) at (1,-0.8) {\texttt{Đ}}; + \node (t2) at (2,-0.8) {\texttt{\#\#ông}}; + \node (t3) at (3,-0.8) {\texttt{gi}}; + \node (t4) at (4,-0.8) {\texttt{\#\#ới}}; + \node (t5) at (5,-0.8) {\texttt{th}}; + \node (t6) at (6,-0.8) {\texttt{\#\#iệ}}; + \node (t7) at (7,-0.8) {\texttt{\#\#u}}; + \node (t8) at (8,-0.8) {\texttt{[SEP]}}; + \foreach \pos in {0,...,8} { + \draw[->,thick] (t\pos) -- (r\pos); + \draw[->,thick] (r\pos) -- (m\pos); + } + \node (B0) at (0,0.2) {}; + \node (B8) at (8,0.2) {}; + \node[fill=yellow!60,align=center,fit={(B0) (B8)}] {}; + \node[fill=none,draw=none] at (4,0.2) {BERT}; + \end{tikzpicture} + \caption{Our proposed end-to-end architecture} + \label{fig:arch} +\end{figure*} + +In our experiment, we start with the open-source mBERT +package\footnote{\url{https://github.com/google-research/bert/blob/master/multilingual.md}}. We +keep the standard hyper-parameters of 12 layers, 768 hidden units, and +12 heads. The model is optimized with Adam~\cite{Kingma:2015} using +the following parameters: $\beta_1 = 0.9, \beta_2 = 0.999$, $\epsilon += 1e-6$ and $L_2$ weight decay of $0.01$. + +The output of BERT is computed as follows~\cite{Peters:2018}: +\begin{equation*} + B_k = \gamma \left( w_0 E_k + \sum_{k=1}^m w_i h_{ki} \right), +\end{equation*} +where +\begin{itemize} +\item $B_k$ is the BERT output of $k$-th token; +\item $E_k$ is the embedding of $k$-th token; +\item $m$ is the number of hidden layers of BERT; +\item $h_{ki}$ is the $i$-th hidden state of of $k$-th token; +\item $\gamma, w_0, w_1,\dots,w_m$ are trainable parameters. +\end{itemize} + +\subsubsection{Proposed Architecture} + +Our proposed architecture contains five main layers as follows: +\begin{enumerate} +\item The input layer encodes a sequence of tokens which are + substrings of the input sentence, including ignored indices, padding + and separators; +\item A BERT layer; +\item A bidirectional RNN layer with either LSTM or GRU units; +\item An attention layer; +\item A linear layer; +% \item a Conditional Random Field (CRF) layer which supports ignored +% indices, which means that the ignored labels are not acummulated into +% the loss function of the end-to-end model; +\end{enumerate} + +A schematic view of our model architecture is shown in +Figure~\ref{fig:arch}. + +\subsection{ELECTRA} + +\begin{figure*} + \centering + \begin{tikzpicture}[x=1.75cm,y=0.8cm] + \tikzstyle{every node} = [fill=gray!30,text width=1.2cm] + % (a) + \node (m1) at (0,4) {phi}; + \node (m2) at (0,3) {công}; + \node (m3) at (0,2) {điều}; + \node (m4) at (0,1) {khiển}; + \node (m5) at (0,0) {máy}; + \node (m6) at (0,-1) {bay}; + + \node (n1) at (1,4) {phi}; + \node[fill=green!60] (n2) at (1,3) {MASK}; + \node (n3) at (1,2) {điều}; + \node (n4) at (1,1) {khiển}; + \node[fill=green!60] (n5) at (1,0) {MASK}; + \node (n6) at (1,-1) {bay}; + + \node (p1) at (4,4) {phi}; + \node[fill=blue!60] (p2) at (4,3) {công}; + \node (p3) at (4,2) {điều}; + \node (p4) at (4,1) {khiển}; + \node[fill=red!60] (p5) at (4,0) {sân}; + \node (p6) at (4,-1) {bay}; + + \foreach \from/\to in { + m1/n1, m2/n2, m3/n3, m4/n4, m5/n5, m6/n6, n1/p1, n2/p2, n3/p3, + n4/p4, n5/p5, n6/p6} + \draw[->,thick] (\from) -- (\to); + + \draw[draw=black,fill=white] (1.75,4.5) rectangle ++(1.5,-6); + \node[fill=none] (g) at (2.25,2) + {\textbf{\begin{tabular}{c}Generator\\(BERT)\end{tabular}}}; + + \node[fill=none] (q1) at (7,4) {original}; + \node[fill=blue!60] (q2) at (7,3) {original}; + \node[fill=none] (q3) at (7,2) {original}; + \node[fill=none] (q4) at (7,1) {original}; + \node[fill=red!60] (q5) at (7,0) {replaced}; + \node[fill=none] (q6) at (7,-1) {original}; + + \foreach \from/\to in { + p1/q1, p2/q2, p3/q3, p4/q4, p5/q5, p6/q6} + \draw[->,thick] (\from) -- (\to); + + \draw[draw=black,fill=white] (4.75,4.5) rectangle ++(1.5,-6); + \node[fill=none] (d) at (5.1,2) + {\textbf{\begin{tabular}{c}Discriminator\\(ELECTRA)\end{tabular}}}; + + \end{tikzpicture} + \caption{An overview of replaced token detection by the ELECTRA + model on a sample drawn from vELECTRA} + \label{fig:electra} +\end{figure*} + +ELECTRA~\cite{Clark:2020} is currently the latest development of BERT-based model +where a more sample-efficient pre-training method is used. This method +is called replaced token detection. In this method, two neural +networks, a generator $G$ and a discriminator $D$, are trained +simultaneously. Each one consists of a Transformer network (an +encoder) that maps a sequence of input tokens $\vec x = [x_1, +x_2,\dots,x_n]$ into a sequence of contextualized vectors $h(\vec x) = +[h_1, h_2,\dots, h_n]$. For a given position $t$ where $x_t$ is the +masked token, the generator outputs a probability for generating a +particular token $x_t$ with a softmax distribution: +\begin{equation*} + p_G(x_t|\vec x) = \frac{\exp(x_t^\top h_G(\vec x)_t) }{\sum_{u} + \exp(u_t^\top h_G(\vec x)_t)}. +\end{equation*} +For a given position $t$, the discriminator predicts whether the +token $x_t$ is ``real'', i.e., that it comes from the data rather than the generator distribution, with a +sigmoid function: +\begin{equation*} +D(\vec x, t) = \sigma \left (w^\top h_D(\vec x)_t \right ) +\end{equation*} + +An overview of the replaced token detection in the ELECTRA model is +shown in Figure~\ref{fig:electra}. The generator is a BERT model which +is trained jointly with the discriminator. The Vietnamese example is a +real one which is sampled from our training corpus. + +\section{Experiments} +\label{sec:experiments} + +\subsection{Experimental Settings} + +\subsubsection{Model Training} +To train the proposed models, we use a CPU (Intel Xeon E5-2699 v4 +@2.20GHz) and a GPU (NVIDIA GeForce GTX 1080 Ti 11G). The +hyper-parameters that we chose are as follows: maximum sequence length +is 256, BERT learning rate is $2E-05$, learning rate is $1E-3$, number +of epochs is 100, batch size is 16, use apex and BERT weight decay is +set to 0, the Adam rate is $1E-08$. The configuration of our model is +as follows: number of RNN hidden units is 256, one RNN layer, +attention hidden dimension is 64, number of attention heads is 3 and a +dropout rate of 0.5. + +To build the pre-training language model, it is very important to have +a good and big dataset. This dataset was collected from online +newspapers\footnote{vnexpress.net, dantri.com.vn, baomoi.com, + zingnews.vn, vitalk.vn, etc.} in Vietnamese. To clean the data, we +perform the following pre-processing steps: +\begin{itemize} + \item Remove duplicated news + \item Only accept valid letters in Vietnamese + \item Remove too short sentences (less than 4 words) +\end{itemize} + +We obtained approximately 10GB of texts after collection. This dataset was + used to further pre-train the mBERT to build our viBERT which better + represents Vietnamese texts. About the vocab, we removed insufficient + vocab from mBERT because its vocab contains ones for other + languages. This was done by keeping only vocabs existed in the + dataset. + + In pre-training vELECTRA, we collect more data from two sources: + \begin{itemize} + \item NewsCorpus: 27.4 + GB\footnote{\url{https://github.com/binhvq/news-corpus}} + \item OscarCorpus: 31.0 + GB\footnote{\url{https://traces1.inria.fr/oscar/}} + \end{itemize} + +Totally, with more than 60GB of texts, we start training different +versions of vELECTRA. It is worth noting that pre-training viBERT +is much slower than pre-training vELECTRA. For this reason, we +pre-trained viBERT on the 10GB corpus rather than on the large 60GB +corpus. + +\begin{table*} + \centering + \begin{tabular}{|l | l | c | c | c | c | c| c | } + \hline + \textbf{No.}&\multicolumn{3}{c|}{\textbf{VLSP 2010}}&\multicolumn{4}{c|}{\textbf{VLSP 2013}}\\ \hline + +\multicolumn{8}{|l|}{\textbf{Existing models}} \\ \hline +1. & \multicolumn{2}{|l|}{MEM~\cite{Le:2010}}& 93.4 & \multicolumn{3}{|l|}{RDRPOSTagger~\cite{Nguyen:2014}} & 95.1 \\ \hline +2. & \multicolumn{3}{c}{} & \multicolumn{3}{|l|}{BiLSTM-CNN-CRF~\cite{Ma:2016}} & 95.4 \\ \hline +3. & \multicolumn{3}{c}{} & \multicolumn{3}{|l|}{VnCoreNLP-POS~\cite{Nguyen:2017}} & 95.9 \\ \hline +4. & \multicolumn{3}{c}{} & \multicolumn{3}{|l|}{jointWPD~\cite{Nguyen:2019}} & 96.0 \\ \hline +5. & \multicolumn{3}{c}{} & \multicolumn{3}{|l|}{PhoBERT\_base~\cite{Nguyen:2020}} & 96.7 \\ \hline + +\hline +\multicolumn{8}{|l|}{\textbf{Proposed models}} \\ \hline + & \textbf{Model Name} & \textbf{mBERT} & \textbf{viBERT} & \textbf{vELEC} & \textbf{mBERT} & \textbf{viBERT} & \textbf{vELEC}\\ + \hline +1.&+Fine-Tune & 94.34 & 95.07 & 95.35 & 96.35 & 96.60 & 96.62 \\ \hline +2.&+BiLSTM& 94.34 & 95.12 & 95.32 & 96.38 & 96.63 & \textbf{96.77} \\ \hline +3.&+BiGRU& 94.37 & 95.13 & 95.37 & 96.45 & 96.68& 96.73\\ \hline +4.&+BiLSTM\_Attn& 94.37 & 95.12 & \textbf{95.40} & 96.36 & 96.61 & 96.61 \\\hline +5.&+BiGRU\_Attn& 94.41 & 95.13 & 95.35 & 96.33 & 96.56 & 96.55 \\ \hline + \end{tabular} + \caption{Performance of our proposed models on the POS tagging task} + \label{tab:result-POS} +\end{table*} + +\subsubsection{Testing and evaluation methods} +In performing experiments, for datasets without development sets, we +randomly selected 10\% for fine-tuning the best parameters. + +To evaluate the effectiveness of the models, we use the commonly-used +metrics which are proposed by the organizers of VLSP. Specifically, we +measure the accuracy score on the POS tagging task which is calculated +as follows: +\begin{equation*} + Acc = \frac{\#of\_words\_correcly\_tagged}{\#of\_words\_in\_the\_test\_set} +\end{equation*} + +and the $F_1$ score on the NER task using the following equations: +\begin{equation*} + F_1 = 2*\frac{Pre*Rec}{Pre+Rec} +\end{equation*} +where \textit{Pre} and \textit{Rec} are determined as follows: +\begin{equation*} + Pre = \frac{NE\_true}{NE\_sys} +\end{equation*} + +\begin{equation*} + Rec = \frac{NE\_true}{NE\_ref} +\end{equation*} + +where \textit{NE\_ref} is the number of NEs in gold data, +\textit{NE\_sys} is the number of NEs in recognizing system, and +\textit{NE\_true} is the number of NEs which is correctly recognized +by the system. + +\subsection{Experimental Results} + +\subsubsection{On the PoS Tagging Task} + +Table~\ref{tab:result-POS} shows experimental results using different +proposed architectures on the top of mBERT and +viBERT and vELECTRA on two benchmark datasets from +the campaign VLSP 2010 and VLSP 2013. + +As can be seen that, with further pre-training techniques on a +Vietnamese dataset, we could significantly improve the performance of +the model. On the dataset of VLSP 2010, both viBERT and +vELECTRA significantly improved the performance by about 1\% +in the $F_1$ scores. On the dataset of VLSP 2013, these two models +slightly improved the performance. + +From the table, we can also see the performance of different +architectures including fine-tuning, BiLSTM, biGRU, and their +combination with attention mechanisms. Fine-tuning mBERT with +linear functions in several epochs could produce nearly state-of-the-art +results. It is also shown that building different architectures on top +slightly improve the performance of all mBERT, +viBERT and vELECTRA models. On the VLSP 2010, we got +the accuracy of 95.40\% using biLSTM with attention on top of +vELECTRA. On the VLSP 2013 dataset, we got 96.77\% in the +accuracy scores using only biLSTM on top of vELECTRA. + +In comparison to previous work, our proposed model - vELECTRA +- outperformed previous ones. It achieved from 1\% to 2\% higher than +existing work using different innovation in deep learning such as CNN, +LSTM, and joint learning techniques. Moreover, vELECTRA also +gained a slightly better than PhoBERT\_base, the same +pre-training language model released so far, by nearly 0.1\% in the +accuracy score. + + +\begin{table*} + \centering + \begin{tabular}{|l | l | c | c | c | c |c | c | } + \hline + \textbf{No.}& \multicolumn{4}{c|}{\textbf{VLSP 2016}}&\multicolumn{3}{c|}{\textbf{VLSP 2018}}\\ \hline +\multicolumn{8}{|l|}{\textbf{Existing models}} \\ \hline + + 1. & \multicolumn{3}{|l|}{TRE+BI~\cite{Le:2016}} & 87.98 & \multicolumn{2}{|l|}{VietNER} & 76.63 \\ \hline + +2. & \multicolumn{3}{|l|}{BiLSTM\_CNN\_CRF~\cite{Pham:2017a}} & 88.59 & \multicolumn{2}{|l|}{ZA-NER} & 74.70 \\ \hline + 3. & \multicolumn{3}{|l|}{BiLSTM~\cite{Pham:2017c}} & 92.02 & \multicolumn{3}{|l|}{} \\ \hline + 4. & \multicolumn{3}{|l|}{NNVLP~\cite{Pham:2017b}} & 92.91 & \multicolumn{3}{|l|}{} \\ \hline + + +5. & \multicolumn{3}{|l|}{VnCoreNLP-NER~\cite{Vu:2018}} & 88.6 & \multicolumn{3}{|l|}{} \\ \hline +6. & \multicolumn{3}{|l|}{VNER~\cite{Nguyen:2019}} & 89.6 & \multicolumn{3}{|l|}{}\\ \hline +7. & \multicolumn{3}{|l|}{ETNLP~\cite{Vu:2019}} & 91.1 & \multicolumn{3}{|l|}{} \\ \hline +8. & \multicolumn{3}{|l|}{PhoBERT\_base~\cite{Nguyen:2020}} & 93.6 & \multicolumn{3}{|l|}{} \\ \hline + +\multicolumn{8}{|l|}{\textbf{Proposed models}} \\ \hline + &\textbf{Model Name}& \textbf{mBERT} & \textbf{viBERT} & \textbf{VELEC} & \textbf{mBERT} & \textbf{viBERT} & \textbf{VELEC} \\ + \hline +1.&+Fine-Tune & 91.28 & 92.84 & 94.00 & 86.86 & 88.04 & 89.79 \\ \hline +2.&+BiLSTM& 91.03 & 93.00 & 93.70 & 86.62 & 88.68 & 89.92 \\ \hline +3.&+BiGRU& 91.52 & 93.44 & 93.93 & 86.72 & 88.98 & \textbf{90.31}\\ \hline +4.&+BiLSTM\_Attn& 91.23 & 92.97 & \textbf{94.07} & 87.12& 89.12 & 90.26 \\\hline +5.&+BiGRU\_Attn& 90.91 & 93.32 & 93.27 & 86.33 & 88.59 &89.94 \\ \hline + \end{tabular} + \caption{Performance of our proposed models on the NER task. ZA-NER~\cite{Luong:2018} + is the best system of VLSP 2018~\cite{Nguyen:2018}. VietNER is + from~\cite{NguyenKA:2019}} + \label{tab:result-NER} +\end{table*} + +\subsubsection{On the NER Task} + +Table~\ref{tab:result-NER} shows experimental results using different +proposed architectures on the top of mBERT, viBERT +and vELECTRA on two benchmark datasets from the campaign +VLSP 2016 and VLSP 2018. + +These results once again gave a strong evidence to the above statement +that further training mBERT on a small raw dataset could +significantly improve the performance of transformation-based language +models on downstream tasks. Training vELECTRA from scratch on +a big Vietnamese dataset could further enhance the performance. On two +datasets, vELECTRA improve the $F_1$ score by from 1\% to 3\% in +comparison to viBERT and mBERT. + +Looking at the performance of different architectures on top of these +pre-trained models, we acknowledged that biLSTM with attention once a +gain yielded the SOTA result on VLSP 2016 dataset. On VLSP 2018 +dataset, the architecture of biGRU yielded the best performance at +90.31\% in the $F_1$ score. + +Comparing to previous work, the best proposed model outperformed all +work by a large margin on both datasets. + + +\begin{figure*} + \centering + \begin{tikzpicture} + \begin{axis}[ + height=5.8cm, + width=\textwidth, + ybar, + bar width=10, + enlarge y limits=0.25, + legend style={at={(0.5,0.85)},anchor=south,legend columns=-1}, + xlabel={\textit{}}, + ylabel={\textit{milliseconds per sentence}}, + symbolic x coords={+Fine-Tune,+biLSTM,+biGRU,+biLSTM-Att,+biGRU-Att}, + xtick=data, + nodes near coords, + every node near coord/.append style={font=\tiny,rotate=90,anchor=east}, + nodes near coords={\pgfmathprintnumber[precision=3]{\pgfplotspointmeta}}, + nodes near coords align={vertical}, + ] + \addplot coordinates {(+Fine-Tune,1.8) (+biLSTM,2.8) (+biGRU,3.1) (+biLSTM-Att, 2.9) (+biGRU-Att, 3.3)}; + \addplot coordinates {(+Fine-Tune,2.9) (+biLSTM,2.8) (+biGRU,2.7) (+biLSTM-Att, 2.9) (+biGRU-Att, 2.9)}; + \addplot coordinates {(+Fine-Tune,1.6) (+biLSTM,2.4) (+biGRU,2.6) (+biLSTM-Att, 1.8) (+biGRU-Att, 2.4)}; + \legend{mBERT, viBERT, vELECTRA} + \end{axis} + \end{tikzpicture} + \caption{Decoding time on PoS task -- VLSP 2013} + \label{fig:vlsp2013} +\end{figure*} + +\subsection{Decoding Time} + +Figure \ref{fig:vlsp2013} and \ref{fig:vlsp2016} shows the averaged +decoding time measured on one sentence. According to our statistics, +the averaged length of one sentence in VLSP 2013 and VLSP 2016 +datasets are 22.55 and 21.87 words, respectively. + +For the POS tagging task measured on VLSP 2013 dataset, among three +models, the fastest decoding time is of vELECTRA model, followed by +viBERT model, and finally by mBERT model. This statement holds for +four proposed architectures on top of these three models. However, for +the fine-tuning technique, the decoding time of mBERT is faster than +that of viBERT. + +For the NER task measured on the VLSP 2016 dataset, among three models, +the slowest time is of viBERT model with more than 2 +milliseconds per sentence. The decoding times on mBERT topped with +simple fine-tuning techniques, or biGRU, or biLSTM-attention is a +little bit faster than on vELECTRA with the same architecture. + +This experiment shows that our proposed models are of practical +use. In fact, they are currently deployed as a core component of our +commercial chatbot engine FPT.AI\footnote{\url{http://fpt.ai/}} which +is serving effectively many customers. More precisely, the FPT.AI +platform has been used by about 70 large enterprises, and of over 27,000 +frequent developers, serving more than 30 million end +users.\footnote{These numbers are reported as of August, 2020.} + +\section{Conclusion} +\label{sec:conclusion} + +This paper presents some new model architectures for sequence tagging +and our experimental results for Vietnamese part-of-speech tagging and +named entity recognition. Our proposed model vELECTRA outperforms +previous ones. For part-of-speech tagging, it improves about 2\% of +absolute point in comparison with existing work which use different +innovation in deep learning such as CNN, LSTM, or joint learning +techniques. For named entity recognition, the vELECTRA outperforms +all previous work by a large margin on both VLSP 2016 and VLSP 2018 +datasets. + +Our code and pre-trained models are published as an open source +project for facilitate adoption and further research in the Vietnamese +language processing community.\footnote{viBERT is available at + \url{https://github.com/fpt-corp/viBERT} and vELECTRA is available + at \url{https://github.com/fpt-corp/vELECTRA}.} An online service of +the models for demonstration is also accessible at +\url{https://fpt.ai/nlp/bert/}. A variant and more advanced version +of this model is currently deployed as a core component of our +commercial chatbot engine FPT.AI which is serving effectively +millions of end users. In particular, these models are being +fine-tuned to improve task-oriented dialogue in mixed and multiple +domains~\cite{Luong:2019} and dependency parsing~\cite{Le:2015c}. + +\begin{figure*} + \centering + \begin{tikzpicture} + \begin{axis}[ + height=5.8cm, + width=\textwidth, + ybar, + bar width=10, + %enlarge x limits=0.05, + enlarge y limits=0.25, + legend style={at={(0.5,0.85)},anchor=south,legend columns=-1}, + xlabel={\textit{}}, + ylabel={\textit{milliseconds per sentence}}, + symbolic x coords={+Fine-Tune,+biLSTM,+biGRU,+biLSTM-Att,+biGRU-Att}, + xtick=data, + nodes near coords, + every node near coord/.append style={font=\tiny,rotate=90,anchor=east}, + nodes near coords={\pgfmathprintnumber[precision=3]{\pgfplotspointmeta}}, + nodes near coords align={vertical}, + ] + \addplot coordinates {(+Fine-Tune,1.1) (+biLSTM,1.7) (+biGRU,1.4) (+biLSTM-Att, 1.8) (+biGRU-Att, 2.0)}; + \addplot coordinates {(+Fine-Tune,1.9) (+biLSTM,2.1) (+biGRU,2.2) (+biLSTM-Att, 2.3) (+biGRU-Att, 2.2)}; + \addplot coordinates {(+Fine-Tune,1.5) (+biLSTM,1.7) (+biGRU,1.6) (+biLSTM-Att, 2.0) (+biGRU-Att, 1.6)}; + \legend{mBERT, viBERT, vELECTRA} + \end{axis} + \end{tikzpicture} + \caption{Decoding time on NER task -- VLSP 2016} + \label{fig:vlsp2016} +\end{figure*} + +\section*{Acknowledgement} + +We thank three anonymous reviewers for their valuable comments for +improving our manuscript. + +% \begin{tikzpicture}[cap=round,line width=3pt] +% \filldraw [fill=examplefill] (0,0) circle (2cm); + +% \foreach \angle / \label in +% {0/3, 30/2, 60/1, 90/12, 120/11, 150/10, 180/9, +% 210/8, 240/7, 270/6, 300/5, 330/4} +% { +% \draw[line width=1pt] (\angle:1.8cm) -- (\angle:2cm); +% \draw (\angle:1.4cm) node{\textsf{\label}}; +% } + +% \foreach \angle in {0,90,180,270} +% \draw[line width=2pt] (\angle:1.6cm) -- (\angle:2cm); + +% \draw (0,0) -- (120:0.8cm); % hour +% \draw (0,0) -- (90:1cm); % minute +% \end{tikzpicture}% + +%\newpage +\bibliographystyle{acl} +\bibliography{references} + +\end{document} diff --git a/references/2020.findings.anh/paper.md b/references/2020.findings.anh/paper.md new file mode 100644 index 0000000000000000000000000000000000000000..d7ce4b0624db711a47a299f287ea15a52e67d696 --- /dev/null +++ b/references/2020.findings.anh/paper.md @@ -0,0 +1,13 @@ +--- +title: "{P" +authors: + - "Nguyen, Dat Quoc and + Tuan Nguyen, Anh" +year: 2020 +venue: "Findings of the Association for Computational Linguistics: EMNLP 2020" +url: "https://aclanthology.org/2020.findings-emnlp.92" +--- + +# {P + +*Full text available in paper.pdf* diff --git a/references/2020.findings.anh/paper.pdf b/references/2020.findings.anh/paper.pdf new file mode 100644 index 0000000000000000000000000000000000000000..57eb99ab92425981f584d3f09c0e7694036b4349 --- /dev/null +++ b/references/2020.findings.anh/paper.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6be99661b7b7d8ee9372ea6177f399c6649e049fa750e8dacb5cfd47eac5abd8 +size 239769 diff --git a/references/2022.arxiv.nguyen/paper.md b/references/2022.arxiv.nguyen/paper.md new file mode 100644 index 0000000000000000000000000000000000000000..ec1ccb8b8472b4473a6fbc963df31dd0d1af11b5 --- /dev/null +++ b/references/2022.arxiv.nguyen/paper.md @@ -0,0 +1,34 @@ +--- +title: "SMTCE: A Social Media Text Classification Evaluation Benchmark and BERTology Models for Vietnamese" +authors: + - "Luan Thanh Nguyen" + - "Kiet Van Nguyen" + - "Ngan Luu-Thuy Nguyen" +year: 2022 +venue: "arXiv" +url: "https://arxiv.org/abs/2209.10482" +arxiv: "2209.10482" +--- + +\maketitle + +\begin{abstract} + +Text classification is a typical natural language processing or computational linguistics task with various interesting applications. As the number of users on social media platforms increases, data acceleration promotes emerging studies on **S**ocial **M**edia **T**ext **C**lassification (**SMTC**) or social media text mining on these valuable resources. In contrast to English, Vietnamese, one of the low-resource languages, is still not concentrated on and exploited thoroughly. Inspired by the success of the GLUE, we introduce the **S**ocial **M**edia **T**ext **C**lassification **E**valuation (**SMTCE**) benchmark, as a collection of datasets and models across a diverse set of SMTC tasks. With the proposed benchmark, we implement and analyze the effectiveness of a variety of multilingual BERT-based models (mBERT, XLM-R, and DistilmBERT) and monolingual BERT-based models (PhoBERT, viBERT, vELECTRA, and viBERT4news) for tasks in the SMTCE benchmark. Monolingual models outperform multilingual models and achieve state-of-the-art results on all text classification tasks. It provides an objective assessment of multilingual and monolingual BERT-based models on the benchmarks, which will benefit future studies about BERTology in the Vietnamese language. + +\end{abstract} + +\input{sections/1_Introduction} +\input{sections/2_RelatedWork} +\input{sections/3_VietnameseSocialMediaText} +\input{sections/4_BERTologyModel} +\input{sections/5_MultilingualVersusMonolingualLanguageModel} +\input{sections/6_WhichOneIsTheBetter} +\input{sections/7_Conclusion} + +# Acknowledgement +Luan Thanh Nguyen was funded by Vingroup JSC and supported by the Master, PhD Scholarship Programme of Vingroup Innovation Foundation (VINIF), Institute of Big Data, code VINIF.2021.ThS.41. + +\bibliographystyle{acl_natbib} + +\bibliography{REFERENCES} \ No newline at end of file diff --git a/references/2022.arxiv.nguyen/paper.pdf b/references/2022.arxiv.nguyen/paper.pdf new file mode 100644 index 0000000000000000000000000000000000000000..1568c8297b0123ce2027576f0cc9910dbdbf3f04 --- /dev/null +++ b/references/2022.arxiv.nguyen/paper.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ea46e3becf2ea0ba9229e84eb7341bc0fc649e46663dc02123f78ccb6b9bab71 +size 308168 diff --git a/references/2022.arxiv.nguyen/paper.tex b/references/2022.arxiv.nguyen/paper.tex new file mode 100644 index 0000000000000000000000000000000000000000..0e6877a31459addbf65840eb1c0d1bb66b80eeb4 --- /dev/null +++ b/references/2022.arxiv.nguyen/paper.tex @@ -0,0 +1,121 @@ + \documentclass[11pt,a4paper]{article} +% \usepackage{authblk} +\usepackage[hyperref]{acl2021} +\usepackage{times} +%\usepackage{latexsym} +%\usepackage{booktabs,chemformula} +%\renewcommand{\UrlFont}{\ttfamily\small} +%\usepackage{stackengine} +\usepackage{amsmath} +\usepackage{multirow} +\usepackage{url} +\usepackage{float} +\usepackage{pgfplots} +\usepackage{footnote} +%\usepackage[flushleft]{threeparttable} +\usepackage[T5]{fontenc} +\usepackage[utf8]{inputenc} + +\usepackage{wasysym} +\usepackage{amssymb} + +\usepackage{makecell} +\usepackage{rotating} + +%\usepackage{longtable} +%\usepackage{supertabular} +\usepackage{multicol} +\usepackage{array} +%\usepackage{lipsum} +%\usepackage{placeins} +\usepackage{flushend} + +\usepackage{makecell} + +\usepackage{flafter} + +%\usepackage{pifont}% +%\newcommand{\cmark}{\ding{51}}% +%\newcommand{\xmark}{\ding{55}}% + +%\usepackage{microtype} + +\usepackage{graphicx} +\graphicspath{ {./images/} } + +% \usepackage{pgfplots} +% \pgfplotsset{compat=newest} + +\aclfinalcopy + +\setlength\titlebox{5cm} + +\makeatletter +% \@ifpackageloaded{hyperref}{% +% \renewcommand\footref[1]{% +% \begingroup +% \unrestored@protected@xdef\@thefnmark{% +% \ref*{#1}% +% }% +% \endgroup +% \ifHy@hyperfootnotes +% \expandafter\@firstoftwo +% \else +% \expandafter\@secondoftwo +% \fi +% {\hyperref[#1]{\strut\H@@footnotemark}}{\@footnotemark}% +% }% +% }{}% + +% \newcommand\savedlabel{}% +% \AtBeginDocument{\let\savedlabel=\label}% +% \newcommand\footnotereflabel[1]{% +% \@bsphack +% \begingroup +% \def\@currentHref{Hfootnote.\theHfootnote}\savedlabel{#1}% +% \endgroup +% \@esphack +% }% + +\makeatother + +\title{SMTCE: A Social Media Text Classification Evaluation Benchmark\\and BERTology Models for Vietnamese} + +\author{ + Luan Thanh Nguyen$^{1, 2}$, + Kiet Van Nguyen$^{1, 2}$, + \bf Ngan Luu-Thuy Nguyen$^{1, 2}$ \\ + $^{1}$Faculty of Information Science and Engineering, University of Information Technology, \\Ho Chi Minh City, Vietnam \\ + $^{2}$Vietnam National University, Ho Chi Minh City, Vietnam \\ + \texttt{\{luannt, kietnv, ngannlt\}@uit.edu.vn} + } + +\date{} +%------------ + +\date{} +\begin{document} +\maketitle + +\begin{abstract} + +Text classification is a typical natural language processing or computational linguistics task with various interesting applications. As the number of users on social media platforms increases, data acceleration promotes emerging studies on \textbf{S}ocial \textbf{M}edia \textbf{T}ext \textbf{C}lassification (\textbf{SMTC}) or social media text mining on these valuable resources. In contrast to English, Vietnamese, one of the low-resource languages, is still not concentrated on and exploited thoroughly. Inspired by the success of the GLUE, we introduce the \textbf{S}ocial \textbf{M}edia \textbf{T}ext \textbf{C}lassification \textbf{E}valuation (\textbf{SMTCE}) benchmark, as a collection of datasets and models across a diverse set of SMTC tasks. With the proposed benchmark, we implement and analyze the effectiveness of a variety of multilingual BERT-based models (mBERT, XLM-R, and DistilmBERT) and monolingual BERT-based models (PhoBERT, viBERT, vELECTRA, and viBERT4news) for tasks in the SMTCE benchmark. Monolingual models outperform multilingual models and achieve state-of-the-art results on all text classification tasks. It provides an objective assessment of multilingual and monolingual BERT-based models on the benchmarks, which will benefit future studies about BERTology in the Vietnamese language. + +\end{abstract} + +\input{sections/1_Introduction} +\input{sections/2_RelatedWork} +\input{sections/3_VietnameseSocialMediaText} +\input{sections/4_BERTologyModel} +\input{sections/5_MultilingualVersusMonolingualLanguageModel} +\input{sections/6_WhichOneIsTheBetter} +\input{sections/7_Conclusion} + +\section*{Acknowledgement} +Luan Thanh Nguyen was funded by Vingroup JSC and supported by the Master, PhD Scholarship Programme of Vingroup Innovation Foundation (VINIF), Institute of Big Data, code VINIF.2021.ThS.41. + +\bibliographystyle{acl_natbib} % We choose the +% \bibliography{anthology, REFERENCES} +\bibliography{REFERENCES} + +\end{document} \ No newline at end of file diff --git a/references/2022.arxiv.nguyen/source/PAPER.bbl b/references/2022.arxiv.nguyen/source/PAPER.bbl new file mode 100644 index 0000000000000000000000000000000000000000..ee365e93589d8325088d615ab97972c314ddae01 --- /dev/null +++ b/references/2022.arxiv.nguyen/source/PAPER.bbl @@ -0,0 +1,232 @@ +\begin{thebibliography}{26} +\expandafter\ifx\csname natexlab\endcsname\relax\def\natexlab#1{#1}\fi + +\bibitem[{Abdellatif et~al.(2018)Abdellatif, Ben~Hassine, Ben~Yahia, and + Bouzeghoub}]{10.1007/978-3-319-73117-9_40} +Safa Abdellatif, Mohamed~Ali Ben~Hassine, Sadok Ben~Yahia, and Amel Bouzeghoub. + 2018. +\newblock Arcid: A new approach to deal with imbalanced datasets + classification. +\newblock In \emph{SOFSEM 2018: Theory and Practice of Computer Science}, pages + 569--580, Cham. 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Association for Computational Linguistics. + +\bibitem[{Wei and Zou(2019)}]{wei2019eda} +Jason Wei and Kai Zou. 2019. +\newblock Eda: Easy data augmentation techniques for boosting performance on + text classification tasks. +\newblock In \emph{Proceedings of the 2019 Conference on Empirical Methods in + Natural Language Processing and the 9th International Joint Conference on + Natural Language Processing (EMNLP-IJCNLP)}, pages 6382--6388. + +\end{thebibliography} diff --git a/references/2022.arxiv.nguyen/source/PAPER.tex b/references/2022.arxiv.nguyen/source/PAPER.tex new file mode 100644 index 0000000000000000000000000000000000000000..0e6877a31459addbf65840eb1c0d1bb66b80eeb4 --- /dev/null +++ b/references/2022.arxiv.nguyen/source/PAPER.tex @@ -0,0 +1,121 @@ + \documentclass[11pt,a4paper]{article} +% \usepackage{authblk} +\usepackage[hyperref]{acl2021} +\usepackage{times} +%\usepackage{latexsym} +%\usepackage{booktabs,chemformula} +%\renewcommand{\UrlFont}{\ttfamily\small} +%\usepackage{stackengine} +\usepackage{amsmath} +\usepackage{multirow} +\usepackage{url} +\usepackage{float} +\usepackage{pgfplots} +\usepackage{footnote} +%\usepackage[flushleft]{threeparttable} +\usepackage[T5]{fontenc} +\usepackage[utf8]{inputenc} + +\usepackage{wasysym} +\usepackage{amssymb} + +\usepackage{makecell} +\usepackage{rotating} + +%\usepackage{longtable} +%\usepackage{supertabular} +\usepackage{multicol} +\usepackage{array} +%\usepackage{lipsum} +%\usepackage{placeins} +\usepackage{flushend} + +\usepackage{makecell} + +\usepackage{flafter} + +%\usepackage{pifont}% +%\newcommand{\cmark}{\ding{51}}% +%\newcommand{\xmark}{\ding{55}}% + +%\usepackage{microtype} + +\usepackage{graphicx} +\graphicspath{ {./images/} } + +% \usepackage{pgfplots} +% \pgfplotsset{compat=newest} + +\aclfinalcopy + +\setlength\titlebox{5cm} + +\makeatletter +% \@ifpackageloaded{hyperref}{% +% \renewcommand\footref[1]{% +% \begingroup +% \unrestored@protected@xdef\@thefnmark{% +% \ref*{#1}% +% }% +% \endgroup +% \ifHy@hyperfootnotes +% \expandafter\@firstoftwo +% \else +% \expandafter\@secondoftwo +% \fi +% {\hyperref[#1]{\strut\H@@footnotemark}}{\@footnotemark}% +% }% +% }{}% + +% \newcommand\savedlabel{}% +% \AtBeginDocument{\let\savedlabel=\label}% +% \newcommand\footnotereflabel[1]{% +% \@bsphack +% \begingroup +% \def\@currentHref{Hfootnote.\theHfootnote}\savedlabel{#1}% +% \endgroup +% \@esphack +% }% + +\makeatother + +\title{SMTCE: A Social Media Text Classification Evaluation Benchmark\\and BERTology Models for Vietnamese} + +\author{ + Luan Thanh Nguyen$^{1, 2}$, + Kiet Van Nguyen$^{1, 2}$, + \bf Ngan Luu-Thuy Nguyen$^{1, 2}$ \\ + $^{1}$Faculty of Information Science and Engineering, University of Information Technology, \\Ho Chi Minh City, Vietnam \\ + $^{2}$Vietnam National University, Ho Chi Minh City, Vietnam \\ + \texttt{\{luannt, kietnv, ngannlt\}@uit.edu.vn} + } + +\date{} +%------------ + +\date{} +\begin{document} +\maketitle + +\begin{abstract} + +Text classification is a typical natural language processing or computational linguistics task with various interesting applications. As the number of users on social media platforms increases, data acceleration promotes emerging studies on \textbf{S}ocial \textbf{M}edia \textbf{T}ext \textbf{C}lassification (\textbf{SMTC}) or social media text mining on these valuable resources. In contrast to English, Vietnamese, one of the low-resource languages, is still not concentrated on and exploited thoroughly. Inspired by the success of the GLUE, we introduce the \textbf{S}ocial \textbf{M}edia \textbf{T}ext \textbf{C}lassification \textbf{E}valuation (\textbf{SMTCE}) benchmark, as a collection of datasets and models across a diverse set of SMTC tasks. With the proposed benchmark, we implement and analyze the effectiveness of a variety of multilingual BERT-based models (mBERT, XLM-R, and DistilmBERT) and monolingual BERT-based models (PhoBERT, viBERT, vELECTRA, and viBERT4news) for tasks in the SMTCE benchmark. Monolingual models outperform multilingual models and achieve state-of-the-art results on all text classification tasks. It provides an objective assessment of multilingual and monolingual BERT-based models on the benchmarks, which will benefit future studies about BERTology in the Vietnamese language. + +\end{abstract} + +\input{sections/1_Introduction} +\input{sections/2_RelatedWork} +\input{sections/3_VietnameseSocialMediaText} +\input{sections/4_BERTologyModel} +\input{sections/5_MultilingualVersusMonolingualLanguageModel} +\input{sections/6_WhichOneIsTheBetter} +\input{sections/7_Conclusion} + +\section*{Acknowledgement} +Luan Thanh Nguyen was funded by Vingroup JSC and supported by the Master, PhD Scholarship Programme of Vingroup Innovation Foundation (VINIF), Institute of Big Data, code VINIF.2021.ThS.41. + +\bibliographystyle{acl_natbib} % We choose the +% \bibliography{anthology, REFERENCES} +\bibliography{REFERENCES} + +\end{document} \ No newline at end of file diff --git a/references/2022.arxiv.nguyen/source/REFERENCES.bib b/references/2022.arxiv.nguyen/source/REFERENCES.bib new file mode 100644 index 0000000000000000000000000000000000000000..531156b0fd880996c9af4fd45d16ee70bb047d06 --- /dev/null +++ b/references/2022.arxiv.nguyen/source/REFERENCES.bib @@ -0,0 +1,559 @@ +@inproceedings{bui-etal-2020-improving, + title = "Improving Sequence Tagging for {V}ietnamese Text using Transformer-based Neural Models", + author = "Bui, The Viet and + Tran, Thi Oanh and + Le-Hong, Phuong", + booktitle = "Proceedings of the 34th Pacific Asia Conference on Language, Information and Computation", + month = oct, + year = "2020", + address = "Hanoi, Vietnam", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2020.paclic-1.2", + pages = "13--20", +} + +@inproceedings{nguyen-tuan-nguyen-2020-phobert, + title = "{P}ho{BERT}: Pre-trained language models for {V}ietnamese", + author = "Nguyen, Dat Quoc and + Tuan Nguyen, Anh", + booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020", + month = nov, + year = "2020", + address = "Online", + publisher = "Association for Computational Linguistics", + url = "https://www.aclweb.org/anthology/2020.findings-emnlp.92", + doi = "10.18653/v1/2020.findings-emnlp.92", + pages = "1037--1042", + abstract = "We present PhoBERT with two versions, PhoBERT-base and PhoBERT-large, the first public large-scale monolingual language models pre-trained for Vietnamese. Experimental results show that PhoBERT consistently outperforms the recent best pre-trained multilingual model XLM-R (Conneau et al., 2020) and improves the state-of-the-art in multiple Vietnamese-specific NLP tasks including Part-of-speech tagging, Dependency parsing, Named-entity recognition and Natural language inference. We release PhoBERT to facilitate future research and downstream applications for Vietnamese NLP. Our PhoBERT models are available at https://github.com/VinAIResearch/PhoBERT", +} + +@article{nguyen2021vietnamese, + author = {Kiet Van Nguyen and + Son Quoc Tran and + Luan Thanh Nguyen and + Tin Van Huynh and + Son T. Luu and + Ngan Luu{-}Thuy Nguyen}, + title = {{VLSP} 2021 - ViMRC Challenge: Vietnamese Machine Reading Comprehension}, + journal = {CoRR}, + volume = {abs/2203.11400}, + year = {2022} +} + +@inproceedings{luu2021largescale, + title={A large-scale dataset for hate speech detection on vietnamese social media texts}, + author={Luu, Son T and Nguyen, Kiet Van and Nguyen, Ngan Luu-Thuy}, + booktitle={International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems}, + pages={415--426}, + year={2021}, + organization={Springer} +} + +@InProceedings{nguyen2021constructive, +author="Nguyen, Luan Thanh +and Van Nguyen, Kiet +and Nguyen, Ngan Luu-Thuy", +editor="Fujita, Hamido +and Selamat, Ali +and Lin, Jerry Chun-Wei +and Ali, Moonis", +title="Constructive and Toxic Speech Detection for Open-Domain Social Media Comments in Vietnamese", +booktitle="Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices", +year="2021", +publisher="Springer International Publishing", +address="Cham", +pages="572--583", +isbn="978-3-030-79457-6" +} + +@inproceedings{conneau-etal-2020-unsupervised, + title = "Unsupervised Cross-lingual Representation Learning at Scale", + author = "Conneau, Alexis and + Khandelwal, Kartikay and + Goyal, Naman and + Chaudhary, Vishrav and + Wenzek, Guillaume and + Guzm{\'a}n, Francisco and + Grave, Edouard and + Ott, Myle and + Zettlemoyer, Luke and + Stoyanov, Veselin", + booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", + month = jul, + year = "2020", + address = "Online", + publisher = "Association for Computational Linguistics", + url = "https://www.aclweb.org/anthology/2020.acl-main.747", + doi = "10.18653/v1/2020.acl-main.747", + pages = "8440--8451", + abstract = "This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-R, significantly outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +14.6{\%} average accuracy on XNLI, +13{\%} average F1 score on MLQA, and +2.4{\%} F1 score on NER. XLM-R performs particularly well on low-resource languages, improving 15.7{\%} in XNLI accuracy for Swahili and 11.4{\%} for Urdu over previous XLM models. We also present a detailed empirical analysis of the key factors that are required to achieve these gains, including the trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing per-language performance; XLM-R is very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We will make our code and models publicly available.", +} + +@inproceedings{wolf-etal-2020-transformers, + title = "Transformers: State-of-the-Art Natural Language Processing", + author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush", + booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", + month = oct, + year = "2020", + address = "Online", + publisher = "Association for Computational Linguistics", + url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6", + pages = "38--45" +} + +@incollection{uit-vsmec, + doi = {10.1007/978-981-15-6168-9_27}, + year = 2020, + publisher = {Springer Singapore}, + pages = {319--333}, + author = {Vong Anh Ho and Duong Huynh-Cong Nguyen and Danh Hoang Nguyen and Linh Thi-Van Pham and Duc-Vu Nguyen and Kiet Van Nguyen and Ngan Luu-Thuy Nguyen}, + title = {Emotion Recognition for Vietnamese Social Media Text}, + booktitle = {Communications in Computer and Information Science} +} + +@misc{sanh2020distilbert, + title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter}, + author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf}, + year={2020}, + eprint={1910.01108}, + archivePrefix={arXiv}, + primaryClass={cs.CL} +} + +@inproceedings{devlin-etal-2019-bert, + title = "{BERT}: Pre-training of Deep Bidirectional Transformers for Language Understanding", + author = "Devlin, Jacob and + Chang, Ming-Wei and + Lee, Kenton and + Toutanova, Kristina", + booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)", + month = jun, + year = "2019", + address = "Minneapolis, Minnesota", + publisher = "Association for Computational Linguistics", + url = "https://www.aclweb.org/anthology/N19-1423", + doi = "10.18653/v1/N19-1423", + pages = "4171--4186", + abstract = "We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5 (7.7 point absolute improvement), MultiNLI accuracy to 86.7{\%} (4.6{\%} absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).", +} + +@inproceedings{huynh-etal-2020-simple, + title = "A simple and efficient ensemble classifier combining multiple neural network models on social media datasets in {V}ietnamese", + author = "Huynh, Huy Duc and + Do, Hang Thi-Thuy and + Nguyen, Kiet Van and + Nguyen, Ngan Thuy-Luu", + booktitle = "Proceedings of the 34th Pacific Asia Conference on Language, Information and Computation", + month = oct, + year = "2020", + address = "Hanoi, Vietnam", + publisher = "Association for Computational Linguistics", + url = "https://www.aclweb.org/anthology/2020.paclic-1.48", + pages = "420--429", +} + +@inproceedings{rybak-etal-2020-klej, + title = "{KLEJ}: Comprehensive Benchmark for {P}olish Language Understanding", + author = "Rybak, Piotr and + Mroczkowski, Robert and + Tracz, Janusz and + Gawlik, Ireneusz", + booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", + month = jul, + year = "2020", + address = "Online", + publisher = "Association for Computational Linguistics", + url = "https://www.aclweb.org/anthology/2020.acl-main.111", + doi = "10.18653/v1/2020.acl-main.111", + pages = "1191--1201", + abstract = "In recent years, a series of Transformer-based models unlocked major improvements in general natural language understanding (NLU) tasks. Such a fast pace of research would not be possible without general NLU benchmarks, which allow for a fair comparison of the proposed methods. However, such benchmarks are available only for a handful of languages. To alleviate this issue, we introduce a comprehensive multi-task benchmark for the Polish language understanding, accompanied by an online leaderboard. It consists of a diverse set of tasks, adopted from existing datasets for named entity recognition, question-answering, textual entailment, and others. We also introduce a new sentiment analysis task for the e-commerce domain, named Allegro Reviews (AR). To ensure a common evaluation scheme and promote models that generalize to different NLU tasks, the benchmark includes datasets from varying domains and applications. Additionally, we release HerBERT, a Transformer-based model trained specifically for the Polish language, which has the best average performance and obtains the best results for three out of nine tasks. Finally, we provide an extensive evaluation, including several standard baselines and recently proposed, multilingual Transformer-based models.", +} + +@inproceedings{le-etal-2020-flaubert, + title = "{F}lau{BERT}: Unsupervised Language Model Pre-training for {F}rench", + author = {Le, Hang and + Vial, Lo{\"\i}c and + Frej, Jibril and + Segonne, Vincent and + Coavoux, Maximin and + Lecouteux, Benjamin and + Allauzen, Alexandre and + Crabb{\'e}, Benoit and + Besacier, Laurent and + Schwab, Didier}, + booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference", + month = may, + year = "2020", + address = "Marseille, France", + publisher = "European Language Resources Association", + url = "https://www.aclweb.org/anthology/2020.lrec-1.302", + pages = "2479--2490", + abstract = "Language models have become a key step to achieve state-of-the art results in many different Natural Language Processing (NLP) tasks. Leveraging the huge amount of unlabeled texts nowadays available, they provide an efficient way to pre-train continuous word representations that can be fine-tuned for a downstream task, along with their contextualization at the sentence level. This has been widely demonstrated for English using contextualized representations (Dai and Le, 2015; Peters et al., 2018; Howard and Ruder, 2018; Radford et al., 2018; Devlin et al., 2019; Yang et al., 2019b). In this paper, we introduce and share FlauBERT, a model learned on a very large and heterogeneous French corpus. Models of different sizes are trained using the new CNRS (French National Centre for Scientific Research) Jean Zay supercomputer. We apply our French language models to diverse NLP tasks (text classification, paraphrasing, natural language inference, parsing, word sense disambiguation) and show that most of the time they outperform other pre-training approaches. Different versions of FlauBERT as well as a unified evaluation protocol for the downstream tasks, called FLUE (French Language Understanding Evaluation), are shared to the research community for further reproducible experiments in French NLP.", + language = "English", + ISBN = "979-10-95546-34-4", +} + +@inproceedings{cui-etal-2020-revisiting, + title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing", + author = "Cui, Yiming and + Che, Wanxiang and + Liu, Ting and + Qin, Bing and + Wang, Shijin and + Hu, Guoping", + booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings", + month = nov, + year = "2020", + address = "Online", + publisher = "Association for Computational Linguistics", + url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58", +} + +@inproceedings{martin-etal-2020-camembert, + title = "{C}amem{BERT}: a Tasty {F}rench Language Model", + author = "Martin, Louis and + Muller, Benjamin and + Ortiz Su{\'a}rez, Pedro Javier and + Dupont, Yoann and + Romary, Laurent and + de la Clergerie, {\'E}ric and + Seddah, Djam{\'e} and + Sagot, Beno{\^\i}t", + booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", + month = jul, + year = "2020", + address = "Online", + publisher = "Association for Computational Linguistics", + url = "https://www.aclweb.org/anthology/2020.acl-main.645", + doi = "10.18653/v1/2020.acl-main.645", + pages = "7203--7219", + abstract = "Pretrained language models are now ubiquitous in Natural Language Processing. Despite their success, most available models have either been trained on English data or on the concatenation of data in multiple languages. This makes practical use of such models {--}in all languages except English{--} very limited. In this paper, we investigate the feasibility of training monolingual Transformer-based language models for other languages, taking French as an example and evaluating our language models on part-of-speech tagging, dependency parsing, named entity recognition and natural language inference tasks. We show that the use of web crawled data is preferable to the use of Wikipedia data. More surprisingly, we show that a relatively small web crawled dataset (4GB) leads to results that are as good as those obtained using larger datasets (130+GB). Our best performing model CamemBERT reaches or improves the state of the art in all four downstream tasks.", +} + +@misc{bertjapanese, + author = {Yohei Kikuta}, + title = {BERT Pretrained model Trained On Japanese Wikipedia Articles}, + year = {2019}, + publisher = {GitHub}, + journal = {GitHub repository}, + howpublished = {\url{https://github.com/yoheikikuta/bert-japanese}}, +} + +@inproceedings{NIPS2017_3f5ee243, + author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, \L ukasz and Polosukhin, Illia}, + booktitle = {Advances in Neural Information Processing Systems}, + editor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett}, + pages = {}, + publisher = {Curran Associates, Inc.}, + title = {Attention is All you Need}, + url = {https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf}, + volume = {30}, + year = {2017} +} + +@article{rogers-etal-2020-primer, + title = "A Primer in {BERT}ology: What We Know About How {BERT} Works", + author = "Rogers, Anna and + Kovaleva, Olga and + Rumshisky, Anna", + journal = "Transactions of the Association for Computational Linguistics", + volume = "8", + year = "2020", + url = "https://www.aclweb.org/anthology/2020.tacl-1.54", + doi = "10.1162/tacl_a_00349", + pages = "842--866", + abstract = "Transformer-based models have pushed state of the art in many areas of NLP, but our understanding of what is behind their success is still limited. This paper is the first survey of over 150 studies of the popular BERT model. We review the current state of knowledge about how BERT works, what kind of information it learns and how it is represented, common modifications to its training objectives and architecture, the overparameterization issue, and approaches to compression. We then outline directions for future research.", +} + +@INPROCEEDINGS{9310495, + author={Nguyen, Khang Phuoc-Quy and Van Nguyen, Kiet}, + booktitle={2020 International Conference on Asian Language Processing (IALP)}, + title={Exploiting Vietnamese Social Media Characteristics for Textual Emotion Recognition in Vietnamese}, + year={2020}, + volume={}, + number={}, + pages={276-281}, + doi={10.1109/IALP51396.2020.9310495}} + + @inproceedings{zellers-etal-2018-swag, + title = "{SWAG}: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference", + author = "Zellers, Rowan and + Bisk, Yonatan and + Schwartz, Roy and + Choi, Yejin", + booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing", + month = oct # "-" # nov, + year = "2018", + address = "Brussels, Belgium", + publisher = "Association for Computational Linguistics", + url = "https://www.aclweb.org/anthology/D18-1009", + doi = "10.18653/v1/D18-1009", + pages = "93--104", + abstract = "Given a partial description like {``}she opened the hood of the car,{''} humans can reason about the situation and anticipate what might come next ({''}then, she examined the engine{''}). In this paper, we introduce the task of grounded commonsense inference, unifying natural language inference and commonsense reasoning. We present SWAG, a new dataset with 113k multiple choice questions about a rich spectrum of grounded situations. To address the recurring challenges of the annotation artifacts and human biases found in many existing datasets, we propose Adversarial Filtering (AF), a novel procedure that constructs a de-biased dataset by iteratively training an ensemble of stylistic classifiers, and using them to filter the data. To account for the aggressive adversarial filtering, we use state-of-the-art language models to massively oversample a diverse set of potential counterfactuals. Empirical results demonstrate that while humans can solve the resulting inference problems with high accuracy (88{\%}), various competitive models struggle on our task. We provide comprehensive analysis that indicates significant opportunities for future research.", +} + +@inproceedings{rondeau-hazen-2018-systematic, + title = "Systematic Error Analysis of the {S}tanford Question Answering Dataset", + author = "Rondeau, Marc-Antoine and + Hazen, T. J.", + booktitle = "Proceedings of the Workshop on Machine Reading for Question Answering", + month = jul, + year = "2018", + address = "Melbourne, Australia", + publisher = "Association for Computational Linguistics", + url = "https://www.aclweb.org/anthology/W18-2602", + doi = "10.18653/v1/W18-2602", + pages = "12--20", + abstract = "We analyzed the outputs of multiple question answering (QA) models applied to the Stanford Question Answering Dataset (SQuAD) to identify the core challenges for QA systems on this data set. Through an iterative process, challenging aspects were hypothesized through qualitative analysis of the common error cases. A classifier was then constructed to predict whether SQuAD test examples were likely to be difficult for systems to answer based on features associated with the hypothesized aspects. The classifier{'}s performance was used to accept or reject each aspect as an indicator of difficulty. With this approach, we ensured that our hypotheses were systematically tested and not simply accepted based on our pre-existing biases. Our explanations are not accepted based on human evaluation of individual examples. This process also enabled us to identify the primary QA strategy learned by the models, i.e., systems determined the acceptable answer type for a question and then selected the acceptable answer span of that type containing the highest density of words present in the question within its local vicinity in the passage.", +} + +@inproceedings{wang-etal-2018-glue, + title = "{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding", + author = "Wang, Alex and + Singh, Amanpreet and + Michael, Julian and + Hill, Felix and + Levy, Omer and + Bowman, Samuel", + booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}", + month = nov, + year = "2018", + address = "Brussels, Belgium", + publisher = "Association for Computational Linguistics", + url = "https://www.aclweb.org/anthology/W18-5446", + doi = "10.18653/v1/W18-5446", + pages = "353--355", + abstract = "Human ability to understand language is \textit{general, flexible, and robust}. In contrast, most NLU models above the word level are designed for a specific task and struggle with out-of-domain data. If we aspire to develop models with understanding beyond the detection of superficial correspondences between inputs and outputs, then it is critical to develop a unified model that can execute a range of linguistic tasks across different domains. To facilitate research in this direction, we present the General Language Understanding Evaluation (GLUE, gluebenchmark.com): a benchmark of nine diverse NLU tasks, an auxiliary dataset for probing models for understanding of specific linguistic phenomena, and an online platform for evaluating and comparing models. For some benchmark tasks, training data is plentiful, but for others it is limited or does not match the genre of the test set. GLUE thus favors models that can represent linguistic knowledge in a way that facilitates sample-efficient learning and effective knowledge-transfer across tasks. While none of the datasets in GLUE were created from scratch for the benchmark, four of them feature privately-held test data, which is used to ensure that the benchmark is used fairly. We evaluate baselines that use ELMo (Peters et al., 2018), a powerful transfer learning technique, as well as state-of-the-art sentence representation models. The best models still achieve fairly low absolute scores. Analysis with our diagnostic dataset yields similarly weak performance over all phenomena tested, with some exceptions.", +} + +@ARTICLE{650093, + author={Schuster, M. and Paliwal, K.K.}, + journal={IEEE Transactions on Signal Processing}, + title={Bidirectional recurrent neural networks}, + year={1997}, + volume={45}, + number={11}, + pages={2673-2681}, + doi={10.1109/78.650093}} + + @inproceedings{sun2019videobert, + title={Videobert: A joint model for video and language representation learning}, + author={Sun, Chen and Myers, Austin and Vondrick, Carl and Murphy, Kevin and Schmid, Cordelia}, + booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision}, + pages={7464--7473}, + year={2019} +} + +@article{lee2020biobert, + title={BioBERT: a pre-trained biomedical language representation model for biomedical text mining}, + author={Lee, Jinhyuk and Yoon, Wonjin and Kim, Sungdong and Kim, Donghyeon and Kim, Sunkyu and So, Chan Ho and Kang, Jaewoo}, + journal={Bioinformatics}, + volume={36}, + number={4}, + pages={1234--1240}, + year={2020}, + publisher={Oxford University Press} +} + +@inproceedings{beltagy-etal-2019-scibert, + title = "{S}ci{BERT}: A Pretrained Language Model for Scientific Text", + author = "Beltagy, Iz and + Lo, Kyle and + Cohan, Arman", + booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)", + month = nov, + year = "2019", + address = "Hong Kong, China", + publisher = "Association for Computational Linguistics", + url = "https://www.aclweb.org/anthology/D19-1371", + doi = "10.18653/v1/D19-1371", + pages = "3615--3620", + abstract = "Obtaining large-scale annotated data for NLP tasks in the scientific domain is challenging and expensive. We release SciBERT, a pretrained language model based on BERT (Devlin et. al., 2018) to address the lack of high-quality, large-scale labeled scientific data. SciBERT leverages unsupervised pretraining on a large multi-domain corpus of scientific publications to improve performance on downstream scientific NLP tasks. We evaluate on a suite of tasks including sequence tagging, sentence classification and dependency parsing, with datasets from a variety of scientific domains. We demonstrate statistically significant improvements over BERT and achieve new state-of-the-art results on several of these tasks. The code and pretrained models are available at https://github.com/allenai/scibert/.", +} + +@inproceedings{NEURIPS2019_c04c19c2, + author = {CONNEAU, Alexis and Lample, Guillaume}, + booktitle = {Advances in Neural Information Processing Systems}, + editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d textquotesingle Alch'{e}-Buc and E. Fox and R. Garnett}, + pages = {}, + publisher = {Curran Associates, Inc.}, + title = {Cross-lingual Language Model Pretraining}, + url = {https://proceedings.neurips.cc/paper/2019/file/c04c19c2c2474dbf5f7ac4372c5b9af1-Paper.pdf}, + volume = {32}, + year = {2019} +} + +@misc{liu2020roberta, +title={RoBERTa: A Robustly Optimized BERT Pretraining Approach}, +author={Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and Luke Zettlemoyer and Veselin Stoyanov}, +year={2020}, +url={https://openreview.net/forum?id=SyxS0T4tvS} +} + +@inproceedings{clark2020electric, + title = {Pre-Training Transformers as Energy-Based Cloze Models}, + author = {Kevin Clark and Minh-Thang Luong and Quoc V. Le and Christopher D. Manning}, + booktitle = {EMNLP}, + year = {2020}, + url = {https://www.aclweb.org/anthology/2020.emnlp-main.20.pdf} +} + +@misc{Nguyen-et-al-2020, + author = {Nha Van Nguyen}, + title = {BERT for Vietnamese is trained on more 20 GB news dataset}, + year = {2020}, + publisher = {GitHub}, + journal = {GitHub repository}, + howpublished = {\url{https://github.com/bino282/bert4news}}, + commit = {f568fb60d2106ce2f22abd93feb32bec3dd3b62a} +} + +@article{jaderberg2017population, + title={Population based training of neural networks}, + author={Jaderberg, Max and Dalibard, Valentin and Osindero, Simon and Czarnecki, Wojciech M and Donahue, Jeff and Razavi, Ali and Vinyals, Oriol and Green, Tim and Dunning, Iain and Simonyan, Karen and others}, + journal={arXiv preprint arXiv:1711.09846}, + year={2017} +} + +@misc{vu2020hsd, + title={HSD Shared Task in VLSP Campaign 2019: Hate Speech Detection for Social Good}, + author={Xuan-Son Vu and Thanh Vu and Mai-Vu Tran and Thanh Le-Cong and Huyen T M. Nguyen}, + year={2020}, + eprint={2007.06493}, + archivePrefix={arXiv}, + primaryClass={cs.CL} +} + +@inproceedings{To_2020, + doi = {10.1145/3443279.3443309}, + + url = {https://doi.org/10.1145\%2F3443279.3443309}, + + year = 2020, + month = {dec}, + + publisher = {{ACM}}, + + author = {Huy Quoc To and Kiet Van Nguyen and Ngan Luu-Thuy Nguyen and Anh Gia-Tuan Nguyen}, + + title = {Gender Prediction Based on Vietnamese Names with Machine Learning Techniques}, + + booktitle = {Proceedings of the 4th International Conference on Natural Language Processing and Information Retrieval} +} + +@INPROCEEDINGS{uit-vscf, + author={K. V. {Nguyen} and V. D. {Nguyen} and P. X. V. {Nguyen} and T. T. H. {Truong} and N. L. {Nguyen}}, + booktitle={2018 10th International Conference on Knowledge and Systems Engineering (KSE)}, + title={UIT-VSFC: Vietnamese Students’ Feedback Corpus for Sentiment Analysis}, + year={2018},} + + @inproceedings{to2021monolingual, + title={Monolingual vs multilingual BERTology for Vietnamese extractive multi-document summarization}, + author={Huy Quoc To and Kiet Van Nguyen and Ngan Luu-Thuy Nguyen and Anh Gia-Tuan Nguyen}, + booktitle={Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation}, + pages={555--562}, + year={2021} +} + +@article{van2022vlsp, + title={VLSP 2021-ViMRC Challenge: Vietnamese Machine Reading Comprehension.}, + author={Nguyen, Van Kiet and Tran, Son Quoc and Nguyen, Luan Thanh and Van Huynh, Tin and Luu, Son T and Nguyen, Ngan Luu-Thuy}, + journal={CoRR}, + year={2022} +} + +@article{rogers2020primer, + title={A primer in bertology: What we know about how bert works}, + author={Rogers, Anna and Kovaleva, Olga and Rumshisky, Anna}, + journal={Transactions of the Association for Computational Linguistics}, + volume={8}, + pages={842--866}, + year={2020}, + publisher={MIT Press} +} + +@misc{dangvthin, + doi = {10.48550/ARXIV.2103.09519}, + + url = {https://arxiv.org/abs/2103.09519}, + + author = {Van Thin, Dang and Le, Lac Si and Hoang, Vu Xuan and Nguyen, Ngan Luu-Thuy}, + + keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences}, + + title = {Investigating Monolingual and Multilingual BERTModels for Vietnamese Aspect Category Detection}, + + publisher = {arXiv}, + + year = {2021}, + + copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International} +} + +@inproceedings{to-etal-2021-monolingual, + title = "Monolingual vs multilingual {BERT}ology for {V}ietnamese extractive multi-document summarization", + author = "To, Huy Quoc and + Nguyen, Kiet Van and + Nguyen, Ngan Luu-Thuy and + Nguyen, Anh Gia-Tuan", + booktitle = "Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation", + month = "11", + year = "2021", + address = "Shanghai, China", + publisher = "Association for Computational Lingustics", + url = "https://aclanthology.org/2021.paclic-1.73", + pages = "692--699", +} + +@inproceedings{van-der-goot-etal-2021-multilexnorm, + title = "{M}ulti{L}ex{N}orm: A Shared Task on Multilingual Lexical Normalization", + author = {van der Goot, Rob and + Ramponi, Alan and + Zubiaga, Arkaitz and + Plank, Barbara and + Muller, Benjamin and + San Vicente Roncal, I{\~n}aki and + Ljube{\v{s}}i{\'c}, Nikola and + {\c{C}}etino{\u{g}}lu, {\"O}zlem and + Mahendra, Rahmad and + {\c{C}}olako{\u{g}}lu, Talha and + Baldwin, Timothy and + Caselli, Tommaso and + Sidorenko, Wladimir}, + booktitle = "Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)", + month = nov, + year = "2021", + address = "Online", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2021.wnut-1.55", + doi = "10.18653/v1/2021.wnut-1.55", + pages = "493--509", +} + +@InProceedings{10.1007/978-3-319-73117-9_40, +author="Abdellatif, Safa +and Ben Hassine, Mohamed Ali +and Ben Yahia, Sadok +and Bouzeghoub, Amel", +editor="Tjoa, A Min +and Bellatreche, Ladjel +and Biffl, Stefan +and van Leeuwen, Jan +and Wiedermann, Ji{\v{r}}{\'i} ", +title="ARCID: A New Approach to Deal with Imbalanced Datasets Classification", +booktitle="SOFSEM 2018: Theory and Practice of Computer Science", +year="2018", +publisher="Springer International Publishing", +address="Cham", +pages="569--580", +abstract="Classification is one of the most fundamental and well-known tasks in data mining. Class imbalance is the most challenging issue encountered when performing classification, i.e. when the number of instances belonging to the class of interest (minor class) is much lower than that of other classes (major classes). The class imbalance problem has become more and more marked while applying machine learning algorithms to real-world applications such as medical diagnosis, text classification, fraud detection, etc. Standard classifiers may yield very good results regarding the majority classes. However, this kind of classifiers yields bad results regarding the minority classes since they assume a relatively balanced class distribution and equal misclassification costs. To overcome this problem, we propose, in this paper, a novel associative classification algorithm called Association Rule-based Classification for Imbalanced Datasets (ARCID). This algorithm aims to extract significant knowledge from imbalanced datasets by emphasizing on information extracted from minor classes without drastically impacting the predictive accuracy of the classifier. Experimentations, against five datasets obtained from the UCI repository, have been conducted with reference to four assessment measures. Results show that ARCID outperforms standard algorithms. Furthermore, it is very competitive to Fitcare which is a class imbalance insensitive algorithm.", +isbn="978-3-319-73117-9" +} + +@inproceedings{wei2019eda, + title={EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks}, + author={Wei, Jason and Zou, Kai}, + booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)}, + pages={6382--6388}, + year={2019} +} \ No newline at end of file diff --git a/references/2022.arxiv.nguyen/source/acl2021.sty b/references/2022.arxiv.nguyen/source/acl2021.sty new file mode 100644 index 0000000000000000000000000000000000000000..b53c64784f97a387d7489ece218ab6e87001b1a4 --- /dev/null +++ b/references/2022.arxiv.nguyen/source/acl2021.sty @@ -0,0 +1,560 @@ +% This is the LaTex style file for ACL-IJCAI 2021, based off of EMNLP 2020. + +% Addressing bibtex issues mentioned in https://github.com/acl-org/acl-pub/issues/2 +% Other major modifications include +% changing the color of the line numbers to a light gray; changing font size of abstract to be 10pt; changing caption font size to be 10pt. +% -- M Mitchell and Stephanie Lukin + +% 2017: modified to support DOI links in bibliography. Now uses +% natbib package rather than defining citation commands in this file. +% Use with acl_natbib.bst bib style. -- Dan Gildea + +% This is the LaTeX style for ACL 2016. It contains Margaret Mitchell's +% line number adaptations (ported by Hai Zhao and Yannick Versley). + +% It is nearly identical to the style files for ACL 2015, +% ACL 2014, EACL 2006, ACL2005, ACL 2002, ACL 2001, ACL 2000, +% EACL 95 and EACL 99. +% +% Changes made include: adapt layout to A4 and centimeters, widen abstract + +% This is the LaTeX style file for ACL 2000. It is nearly identical to the +% style files for EACL 95 and EACL 99. Minor changes include editing the +% instructions to reflect use of \documentclass rather than \documentstyle +% and removing the white space before the title on the first page +% -- John Chen, June 29, 2000 + +% This is the LaTeX style file for EACL-95. It is identical to the +% style file for ANLP '94 except that the margins are adjusted for A4 +% paper. -- abney 13 Dec 94 + +% The ANLP '94 style file is a slightly modified +% version of the style used for AAAI and IJCAI, using some changes +% prepared by Fernando Pereira and others and some minor changes +% by Paul Jacobs. + +% Papers prepared using the aclsub.sty file and acl.bst bibtex style +% should be easily converted to final format using this style. +% (1) Submission information (\wordcount, \subject, and \makeidpage) +% should be removed. +% (2) \summary should be removed. The summary material should come +% after \maketitle and should be in the ``abstract'' environment +% (between \begin{abstract} and \end{abstract}). +% (3) Check all citations. This style should handle citations correctly +% and also allows multiple citations separated by semicolons. +% (4) Check figures and examples. Because the final format is double- +% column, some adjustments may have to be made to fit text in the column +% or to choose full-width (\figure*} figures. + +% Place this in a file called aclap.sty in the TeX search path. +% (Placing it in the same directory as the paper should also work.) + +% Prepared by Peter F. Patel-Schneider, liberally using the ideas of +% other style hackers, including Barbara Beeton. +% This style is NOT guaranteed to work. It is provided in the hope +% that it will make the preparation of papers easier. +% +% There are undoubtably bugs in this style. If you make bug fixes, +% improvements, etc. please let me know. My e-mail address is: +% pfps@research.att.com + +% Papers are to be prepared using the ``acl_natbib'' bibliography style, +% as follows: +% \documentclass[11pt]{article} +% \usepackage{acl2000} +% \title{Title} +% \author{Author 1 \and Author 2 \\ Address line \\ Address line \And +% Author 3 \\ Address line \\ Address line} +% \begin{document} +% ... +% \bibliography{bibliography-file} +% \bibliographystyle{acl_natbib} +% \end{document} + +% Author information can be set in various styles: +% For several authors from the same institution: +% \author{Author 1 \and ... \and Author n \\ +% Address line \\ ... \\ Address line} +% if the names do not fit well on one line use +% Author 1 \\ {\bf Author 2} \\ ... \\ {\bf Author n} \\ +% For authors from different institutions: +% \author{Author 1 \\ Address line \\ ... \\ Address line +% \And ... \And +% Author n \\ Address line \\ ... \\ Address line} +% To start a seperate ``row'' of authors use \AND, as in +% \author{Author 1 \\ Address line \\ ... \\ Address line +% \AND +% Author 2 \\ Address line \\ ... \\ Address line \And +% Author 3 \\ Address line \\ ... \\ Address line} + +% If the title and author information does not fit in the area allocated, +% place \setlength\titlebox{} right after +% \usepackage{acl2015} +% where can be something larger than 5cm + +% include hyperref, unless user specifies nohyperref option like this: +% \usepackage[nohyperref]{naaclhlt2018} +\newif\ifacl@hyperref +\DeclareOption{hyperref}{\acl@hyperreftrue} +\DeclareOption{nohyperref}{\acl@hyperreffalse} +\ExecuteOptions{hyperref} % default is to use hyperref +\ProcessOptions\relax +\ifacl@hyperref + \RequirePackage{hyperref} + \usepackage{xcolor} % make links dark blue + \definecolor{darkblue}{rgb}{0, 0, 0.5} + \hypersetup{colorlinks=true,citecolor=darkblue, linkcolor=darkblue, urlcolor=darkblue} +\else + % This definition is used if the hyperref package is not loaded. + % It provides a backup, no-op definiton of \href. + % This is necessary because \href command is used in the acl_natbib.bst file. + \def\href#1#2{{#2}} + % We still need to load xcolor in this case because the lighter line numbers require it. (SC/KG/WL) + \usepackage{xcolor} +\fi + +\typeout{Conference Style for ACL-IJCNLP 2021} + +% NOTE: Some laser printers have a serious problem printing TeX output. +% These printing devices, commonly known as ``write-white'' laser +% printers, tend to make characters too light. To get around this +% problem, a darker set of fonts must be created for these devices. +% + +\newcommand{\Thanks}[1]{\thanks{\ #1}} + +% A4 modified by Eneko; again modified by Alexander for 5cm titlebox +\setlength{\paperwidth}{21cm} % A4 +\setlength{\paperheight}{29.7cm}% A4 +\setlength\topmargin{-0.5cm} +\setlength\oddsidemargin{0cm} +\setlength\textheight{24.7cm} +\setlength\textwidth{16.0cm} +\setlength\columnsep{0.6cm} +\newlength\titlebox +\setlength\titlebox{5cm} +\setlength\headheight{5pt} +\setlength\headsep{0pt} +\thispagestyle{empty} +\pagestyle{empty} + + +\flushbottom \twocolumn \sloppy + +% We're never going to need a table of contents, so just flush it to +% save space --- suggested by drstrip@sandia-2 +\def\addcontentsline#1#2#3{} + +\newif\ifaclfinal +\aclfinalfalse +\def\aclfinalcopy{\global\aclfinaltrue} + +%% ----- Set up hooks to repeat content on every page of the output doc, +%% necessary for the line numbers in the submitted version. --MM +%% +%% Copied from CVPR 2015's cvpr_eso.sty, which appears to be largely copied from everyshi.sty. +%% +%% Original cvpr_eso.sty available at: http://www.pamitc.org/cvpr15/author_guidelines.php +%% Original evershi.sty available at: https://www.ctan.org/pkg/everyshi +%% +%% Copyright (C) 2001 Martin Schr\"oder: +%% +%% Martin Schr"oder +%% Cr"usemannallee 3 +%% D-28213 Bremen +%% Martin.Schroeder@ACM.org +%% +%% This program may be redistributed and/or modified under the terms +%% of the LaTeX Project Public License, either version 1.0 of this +%% license, or (at your option) any later version. +%% The latest version of this license is in +%% CTAN:macros/latex/base/lppl.txt. +%% +%% Happy users are requested to send [Martin] a postcard. :-) +%% +\newcommand{\@EveryShipoutACL@Hook}{} +\newcommand{\@EveryShipoutACL@AtNextHook}{} +\newcommand*{\EveryShipoutACL}[1] + {\g@addto@macro\@EveryShipoutACL@Hook{#1}} +\newcommand*{\AtNextShipoutACL@}[1] + {\g@addto@macro\@EveryShipoutACL@AtNextHook{#1}} +\newcommand{\@EveryShipoutACL@Shipout}{% + \afterassignment\@EveryShipoutACL@Test + \global\setbox\@cclv= % + } +\newcommand{\@EveryShipoutACL@Test}{% + \ifvoid\@cclv\relax + \aftergroup\@EveryShipoutACL@Output + \else + \@EveryShipoutACL@Output + \fi% + } +\newcommand{\@EveryShipoutACL@Output}{% + \@EveryShipoutACL@Hook% + \@EveryShipoutACL@AtNextHook% + \gdef\@EveryShipoutACL@AtNextHook{}% + \@EveryShipoutACL@Org@Shipout\box\@cclv% + } +\newcommand{\@EveryShipoutACL@Org@Shipout}{} +\newcommand*{\@EveryShipoutACL@Init}{% + \message{ABD: EveryShipout initializing macros}% + \let\@EveryShipoutACL@Org@Shipout\shipout + \let\shipout\@EveryShipoutACL@Shipout + } +\AtBeginDocument{\@EveryShipoutACL@Init} + +%% ----- Set up for placing additional items into the submitted version --MM +%% +%% Based on eso-pic.sty +%% +%% Original available at: https://www.ctan.org/tex-archive/macros/latex/contrib/eso-pic +%% Copyright (C) 1998-2002 by Rolf Niepraschk +%% +%% Which may be distributed and/or modified under the conditions of +%% the LaTeX Project Public License, either version 1.2 of this license +%% or (at your option) any later version. The latest version of this +%% license is in: +%% +%% http://www.latex-project.org/lppl.txt +%% +%% and version 1.2 or later is part of all distributions of LaTeX version +%% 1999/12/01 or later. +%% +%% In contrast to the original, we do not include the definitions for/using: +%% gridpicture, div[2], isMEMOIR[1], gridSetup[6][], subgridstyle{dotted}, labelfactor{}, gap{}, gridunitname{}, gridunit{}, gridlines{\thinlines}, subgridlines{\thinlines}, the {keyval} package, evenside margin, nor any definitions with 'color'. +%% +%% These are beyond what is needed for the NAACL/ACL style. +%% +\newcommand\LenToUnit[1]{#1\@gobble} +\newcommand\AtPageUpperLeft[1]{% + \begingroup + \@tempdima=0pt\relax\@tempdimb=\ESO@yoffsetI\relax + \put(\LenToUnit{\@tempdima},\LenToUnit{\@tempdimb}){#1}% + \endgroup +} +\newcommand\AtPageLowerLeft[1]{\AtPageUpperLeft{% + \put(0,\LenToUnit{-\paperheight}){#1}}} +\newcommand\AtPageCenter[1]{\AtPageUpperLeft{% + \put(\LenToUnit{.5\paperwidth},\LenToUnit{-.5\paperheight}){#1}}} +\newcommand\AtPageLowerCenter[1]{\AtPageUpperLeft{% + \put(\LenToUnit{.5\paperwidth},\LenToUnit{-\paperheight}){#1}}}% +\newcommand\AtPageLowishCenter[1]{\AtPageUpperLeft{% + \put(\LenToUnit{.5\paperwidth},\LenToUnit{-.96\paperheight}){#1}}} +\newcommand\AtTextUpperLeft[1]{% + \begingroup + \setlength\@tempdima{1in}% + \advance\@tempdima\oddsidemargin% + \@tempdimb=\ESO@yoffsetI\relax\advance\@tempdimb-1in\relax% + \advance\@tempdimb-\topmargin% + \advance\@tempdimb-\headheight\advance\@tempdimb-\headsep% + \put(\LenToUnit{\@tempdima},\LenToUnit{\@tempdimb}){#1}% + \endgroup +} +\newcommand\AtTextLowerLeft[1]{\AtTextUpperLeft{% + \put(0,\LenToUnit{-\textheight}){#1}}} +\newcommand\AtTextCenter[1]{\AtTextUpperLeft{% + \put(\LenToUnit{.5\textwidth},\LenToUnit{-.5\textheight}){#1}}} +\newcommand{\ESO@HookI}{} \newcommand{\ESO@HookII}{} +\newcommand{\ESO@HookIII}{} +\newcommand{\AddToShipoutPicture}{% + \@ifstar{\g@addto@macro\ESO@HookII}{\g@addto@macro\ESO@HookI}} +\newcommand{\ClearShipoutPicture}{\global\let\ESO@HookI\@empty} +\newcommand{\@ShipoutPicture}{% + \bgroup + \@tempswafalse% + \ifx\ESO@HookI\@empty\else\@tempswatrue\fi% + \ifx\ESO@HookII\@empty\else\@tempswatrue\fi% + \ifx\ESO@HookIII\@empty\else\@tempswatrue\fi% + \if@tempswa% + \@tempdima=1in\@tempdimb=-\@tempdima% + \advance\@tempdimb\ESO@yoffsetI% + \unitlength=1pt% + \global\setbox\@cclv\vbox{% + \vbox{\let\protect\relax + \pictur@(0,0)(\strip@pt\@tempdima,\strip@pt\@tempdimb)% + \ESO@HookIII\ESO@HookI\ESO@HookII% + \global\let\ESO@HookII\@empty% + \endpicture}% + \nointerlineskip% + \box\@cclv}% + \fi + \egroup +} +\EveryShipoutACL{\@ShipoutPicture} +\newif\ifESO@dvips\ESO@dvipsfalse +\newif\ifESO@grid\ESO@gridfalse +\newif\ifESO@texcoord\ESO@texcoordfalse +\newcommand*\ESO@griddelta{}\newcommand*\ESO@griddeltaY{} +\newcommand*\ESO@gridDelta{}\newcommand*\ESO@gridDeltaY{} +\newcommand*\ESO@yoffsetI{}\newcommand*\ESO@yoffsetII{} +\ifESO@texcoord + \def\ESO@yoffsetI{0pt}\def\ESO@yoffsetII{-\paperheight} + \edef\ESO@griddeltaY{-\ESO@griddelta}\edef\ESO@gridDeltaY{-\ESO@gridDelta} +\else + \def\ESO@yoffsetI{\paperheight}\def\ESO@yoffsetII{0pt} + \edef\ESO@griddeltaY{\ESO@griddelta}\edef\ESO@gridDeltaY{\ESO@gridDelta} +\fi + + +%% ----- Submitted version markup: Page numbers, ruler, and confidentiality. Using ideas/code from cvpr.sty 2015. --MM + +\font\aclhv = phvb at 8pt + +%% Define vruler %% + +%\makeatletter +\newbox\aclrulerbox +\newcount\aclrulercount +\newdimen\aclruleroffset +\newdimen\cv@lineheight +\newdimen\cv@boxheight +\newbox\cv@tmpbox +\newcount\cv@refno +\newcount\cv@tot +% NUMBER with left flushed zeros \fillzeros[] +\newcount\cv@tmpc@ \newcount\cv@tmpc +\def\fillzeros[#1]#2{\cv@tmpc@=#2\relax\ifnum\cv@tmpc@<0\cv@tmpc@=-\cv@tmpc@\fi +\cv@tmpc=1 % +\loop\ifnum\cv@tmpc@<10 \else \divide\cv@tmpc@ by 10 \advance\cv@tmpc by 1 \fi + \ifnum\cv@tmpc@=10\relax\cv@tmpc@=11\relax\fi \ifnum\cv@tmpc@>10 \repeat +\ifnum#2<0\advance\cv@tmpc1\relax-\fi +\loop\ifnum\cv@tmpc<#1\relax0\advance\cv@tmpc1\relax\fi \ifnum\cv@tmpc<#1 \repeat +\cv@tmpc@=#2\relax\ifnum\cv@tmpc@<0\cv@tmpc@=-\cv@tmpc@\fi \relax\the\cv@tmpc@}% +% \makevruler[][][][][] +\def\makevruler[#1][#2][#3][#4][#5]{\begingroup\offinterlineskip +\textheight=#5\vbadness=10000\vfuzz=120ex\overfullrule=0pt% +\global\setbox\aclrulerbox=\vbox to \textheight{% +{\parskip=0pt\hfuzz=150em\cv@boxheight=\textheight +\color{gray} +\cv@lineheight=#1\global\aclrulercount=#2% +\cv@tot\cv@boxheight\divide\cv@tot\cv@lineheight\advance\cv@tot2% +\cv@refno1\vskip-\cv@lineheight\vskip1ex% +\loop\setbox\cv@tmpbox=\hbox to0cm{{\aclhv\hfil\fillzeros[#4]\aclrulercount}}% +\ht\cv@tmpbox\cv@lineheight\dp\cv@tmpbox0pt\box\cv@tmpbox\break +\advance\cv@refno1\global\advance\aclrulercount#3\relax +\ifnum\cv@refno<\cv@tot\repeat}}\endgroup}% +%\makeatother + + +% \def\aclpaperid{***} +% \def\confidential{\textcolor{black}{PACLIC 2022 Submission~\aclpaperid. Confidential Review Copy. DO NOT DISTRIBUTE.}} + +%% Page numbering, Vruler and Confidentiality %% +% \makevruler[][][][][] + +% SC/KG/WL - changed line numbering to gainsboro +\definecolor{gainsboro}{rgb}{0.8, 0.8, 0.8} +%\def\aclruler#1{\makevruler[14.17pt][#1][1][3][\textheight]\usebox{\aclrulerbox}} %% old line +\def\aclruler#1{\textcolor{gainsboro}{\makevruler[14.17pt][#1][1][3][\textheight]\usebox{\aclrulerbox}}} + +\def\leftoffset{-2.1cm} %original: -45pt +\def\rightoffset{17.5cm} %original: 500pt +\ifaclfinal\else\pagenumbering{arabic} +\AddToShipoutPicture{% +\ifaclfinal\else +\AtPageLowishCenter{\textcolor{black}{\thepage}} +\aclruleroffset=\textheight +\advance\aclruleroffset4pt + \AtTextUpperLeft{% + \put(\LenToUnit{\leftoffset},\LenToUnit{-\aclruleroffset}){%left ruler + \aclruler{\aclrulercount}} + \put(\LenToUnit{\rightoffset},\LenToUnit{-\aclruleroffset}){%right ruler + \aclruler{\aclrulercount}} + } + \AtTextUpperLeft{%confidential + \put(0,\LenToUnit{1cm}){\parbox{\textwidth}{\centering\aclhv\confidential}} + } +\fi +} + +%%%% ----- End settings for placing additional items into the submitted version --MM ----- %%%% + +%%%% ----- Begin settings for both submitted and camera-ready version ----- %%%% + +%% Title and Authors %% + +\newcommand\outauthor{ + \begin{tabular}[t]{c} + \ifaclfinal + \bf\@author + \else + % Avoiding common accidental de-anonymization issue. --MM + \bf Anonymous PACLIC submission + \fi + \end{tabular}} + +% Changing the expanded titlebox for submissions to 2.5 in (rather than 6.5cm) +% and moving it to the style sheet, rather than within the example tex file. --MM +\ifaclfinal +\else + \addtolength\titlebox{.25in} +\fi +% Mostly taken from deproc. +\def\maketitle{\par + \begingroup + \def\thefootnote{\fnsymbol{footnote}} + \def\@makefnmark{\hbox to 0pt{$^{\@thefnmark}$\hss}} + \twocolumn[\@maketitle] \@thanks + \endgroup + \setcounter{footnote}{0} + \let\maketitle\relax \let\@maketitle\relax + \gdef\@thanks{}\gdef\@author{}\gdef\@title{}\let\thanks\relax} +\def\@maketitle{\vbox to \titlebox{\hsize\textwidth + \linewidth\hsize \vskip 0.125in minus 0.125in \centering + {\Large\bf \@title \par} \vskip 0.2in plus 1fil minus 0.1in + {\def\and{\unskip\enspace{\rm and}\enspace}% + \def\And{\end{tabular}\hss \egroup \hskip 1in plus 2fil + \hbox to 0pt\bgroup\hss \begin{tabular}[t]{c}\bf}% + \def\AND{\end{tabular}\hss\egroup \hfil\hfil\egroup + \vskip 0.25in plus 1fil minus 0.125in + \hbox to \linewidth\bgroup\large \hfil\hfil + \hbox to 0pt\bgroup\hss \begin{tabular}[t]{c}\bf} + \hbox to \linewidth\bgroup\large \hfil\hfil + \hbox to 0pt\bgroup\hss + \outauthor + \hss\egroup + \hfil\hfil\egroup} + \vskip 0.3in plus 2fil minus 0.1in +}} + +% margins and font size for abstract +\renewenvironment{abstract}% + {\centerline{\large\bf Abstract}% + \begin{list}{}% + {\setlength{\rightmargin}{0.6cm}% + \setlength{\leftmargin}{0.6cm}}% + \item[]\ignorespaces% + \@setsize\normalsize{12pt}\xpt\@xpt + }% + {\unskip\end{list}} + +%\renewenvironment{abstract}{\centerline{\large\bf +% Abstract}\vspace{0.5ex}\begin{quote}}{\par\end{quote}\vskip 1ex} + +% Resizing figure and table captions - SL +\newcommand{\figcapfont}{\rm} +\newcommand{\tabcapfont}{\rm} +\renewcommand{\fnum@figure}{\figcapfont Figure \thefigure} +\renewcommand{\fnum@table}{\tabcapfont Table \thetable} +\renewcommand{\figcapfont}{\@setsize\normalsize{12pt}\xpt\@xpt} +\renewcommand{\tabcapfont}{\@setsize\normalsize{12pt}\xpt\@xpt} +% Support for interacting with the caption, subfigure, and subcaption packages - SL +\usepackage{caption} +\DeclareCaptionFont{10pt}{\fontsize{10pt}{12pt}\selectfont} +\captionsetup{font=10pt} + +\RequirePackage{natbib} +% for citation commands in the .tex, authors can use: +% \citep, \citet, and \citeyearpar for compatibility with natbib, or +% \cite, \newcite, and \shortcite for compatibility with older ACL .sty files +\renewcommand\cite{\citep} % to get "(Author Year)" with natbib +\newcommand\shortcite{\citeyearpar}% to get "(Year)" with natbib +\newcommand\newcite{\citet} % to get "Author (Year)" with natbib + +% DK/IV: Workaround for annoying hyperref pagewrap bug +\RequirePackage{etoolbox} +%\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{\errmessage{\noexpand patch failed}} + +% bibliography + +\def\@up#1{\raise.2ex\hbox{#1}} + +% Don't put a label in the bibliography at all. Just use the unlabeled format +% instead. +\def\thebibliography#1{\vskip\parskip% +\vskip\baselineskip% +\def\baselinestretch{1}% +\ifx\@currsize\normalsize\@normalsize\else\@currsize\fi% +\vskip-\parskip% +\vskip-\baselineskip% +\section*{References\@mkboth + {References}{References}}\list + {}{\setlength{\labelwidth}{0pt}\setlength{\leftmargin}{\parindent} + \setlength{\itemindent}{-\parindent}} + \def\newblock{\hskip .11em plus .33em minus -.07em} + \sloppy\clubpenalty4000\widowpenalty4000 + \sfcode`\.=1000\relax} +\let\endthebibliography=\endlist + + +% Allow for a bibliography of sources of attested examples +\def\thesourcebibliography#1{\vskip\parskip% +\vskip\baselineskip% +\def\baselinestretch{1}% +\ifx\@currsize\normalsize\@normalsize\else\@currsize\fi% +\vskip-\parskip% +\vskip-\baselineskip% +\section*{Sources of Attested Examples\@mkboth + {Sources of Attested Examples}{Sources of Attested Examples}}\list + {}{\setlength{\labelwidth}{0pt}\setlength{\leftmargin}{\parindent} + \setlength{\itemindent}{-\parindent}} + \def\newblock{\hskip .11em plus .33em minus -.07em} + \sloppy\clubpenalty4000\widowpenalty4000 + \sfcode`\.=1000\relax} +\let\endthesourcebibliography=\endlist + +% sections with less space +\def\section{\@startsection {section}{1}{\z@}{-2.0ex plus + -0.5ex minus -.2ex}{1.5ex plus 0.3ex minus .2ex}{\large\bf\raggedright}} +\def\subsection{\@startsection{subsection}{2}{\z@}{-1.8ex plus + -0.5ex minus -.2ex}{0.8ex plus .2ex}{\normalsize\bf\raggedright}} +%% changed by KO to - values to get teh initial parindent right +\def\subsubsection{\@startsection{subsubsection}{3}{\z@}{-1.5ex plus + -0.5ex minus -.2ex}{0.5ex plus .2ex}{\normalsize\bf\raggedright}} +\def\paragraph{\@startsection{paragraph}{4}{\z@}{1.5ex plus + 0.5ex minus .2ex}{-1em}{\normalsize\bf}} +\def\subparagraph{\@startsection{subparagraph}{5}{\parindent}{1.5ex plus + 0.5ex minus .2ex}{-1em}{\normalsize\bf}} + +% Footnotes +\footnotesep 6.65pt % +\skip\footins 9pt plus 4pt minus 2pt +\def\footnoterule{\kern-3pt \hrule width 5pc \kern 2.6pt } +\setcounter{footnote}{0} + +% Lists and paragraphs +\parindent 1em +\topsep 4pt plus 1pt minus 2pt +\partopsep 1pt plus 0.5pt minus 0.5pt +\itemsep 2pt plus 1pt minus 0.5pt +\parsep 2pt plus 1pt minus 0.5pt + +\leftmargin 2em \leftmargini\leftmargin \leftmarginii 2em +\leftmarginiii 1.5em \leftmarginiv 1.0em \leftmarginv .5em \leftmarginvi .5em +\labelwidth\leftmargini\advance\labelwidth-\labelsep \labelsep 5pt + +\def\@listi{\leftmargin\leftmargini} +\def\@listii{\leftmargin\leftmarginii + \labelwidth\leftmarginii\advance\labelwidth-\labelsep + \topsep 2pt plus 1pt minus 0.5pt + \parsep 1pt plus 0.5pt minus 0.5pt + \itemsep \parsep} +\def\@listiii{\leftmargin\leftmarginiii + \labelwidth\leftmarginiii\advance\labelwidth-\labelsep + \topsep 1pt plus 0.5pt minus 0.5pt + \parsep \z@ \partopsep 0.5pt plus 0pt minus 0.5pt + \itemsep \topsep} +\def\@listiv{\leftmargin\leftmarginiv + \labelwidth\leftmarginiv\advance\labelwidth-\labelsep} +\def\@listv{\leftmargin\leftmarginv + \labelwidth\leftmarginv\advance\labelwidth-\labelsep} +\def\@listvi{\leftmargin\leftmarginvi + \labelwidth\leftmarginvi\advance\labelwidth-\labelsep} + +\abovedisplayskip 7pt plus2pt minus5pt% +\belowdisplayskip \abovedisplayskip +\abovedisplayshortskip 0pt plus3pt% +\belowdisplayshortskip 4pt plus3pt minus3pt% + +% Less leading in most fonts (due to the narrow columns) +% The choices were between 1-pt and 1.5-pt leading +\def\@normalsize{\@setsize\normalsize{11pt}\xpt\@xpt} +\def\small{\@setsize\small{10pt}\ixpt\@ixpt} +\def\footnotesize{\@setsize\footnotesize{10pt}\ixpt\@ixpt} +\def\scriptsize{\@setsize\scriptsize{8pt}\viipt\@viipt} +\def\tiny{\@setsize\tiny{7pt}\vipt\@vipt} +\def\large{\@setsize\large{14pt}\xiipt\@xiipt} +\def\Large{\@setsize\Large{16pt}\xivpt\@xivpt} +\def\LARGE{\@setsize\LARGE{20pt}\xviipt\@xviipt} +\def\huge{\@setsize\huge{23pt}\xxpt\@xxpt} +\def\Huge{\@setsize\Huge{28pt}\xxvpt\@xxvpt} diff --git a/references/2022.arxiv.nguyen/source/acl_natbib.bst b/references/2022.arxiv.nguyen/source/acl_natbib.bst new file mode 100644 index 0000000000000000000000000000000000000000..821195d8bbb77f882afb308a31e5f9da81720f6b --- /dev/null +++ b/references/2022.arxiv.nguyen/source/acl_natbib.bst @@ -0,0 +1,1975 @@ +%%% acl_natbib.bst +%%% Modification of BibTeX style file acl_natbib_nourl.bst +%%% ... by urlbst, version 0.7 (marked with "% urlbst") +%%% See +%%% Added webpage entry type, and url and lastchecked fields. +%%% Added eprint support. +%%% Added DOI support. +%%% Added PUBMED support. +%%% Added hyperref support. +%%% Original headers follow... + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% BibTeX style file acl_natbib_nourl.bst +% +% intended as input to urlbst script +% $ ./urlbst --hyperref --inlinelinks acl_natbib_nourl.bst > acl_natbib.bst +% +% adapted from compling.bst +% in order to mimic the style files for ACL conferences prior to 2017 +% by making the following three changes: +% - for @incollection, page numbers now follow volume title. +% - for @inproceedings, address now follows conference name. +% (address is intended as location of conference, +% not address of publisher.) +% - for papers with three authors, use et al. in citation +% Dan Gildea 2017/06/08 +% - fixed a bug with format.chapter - error given if chapter is empty +% with inbook. +% Shay Cohen 2018/02/16 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% BibTeX style file compling.bst +% +% Intended for the journal Computational Linguistics (ACL/MIT Press) +% Created by Ron Artstein on 2005/08/22 +% For use with for author-year citations. +% +% I created this file in order to allow submissions to the journal +% Computational Linguistics using the package for author-year +% citations, which offers a lot more flexibility than , CL's +% official citation package. This file adheres strictly to the official +% style guide available from the MIT Press: +% +% http://mitpress.mit.edu/journals/coli/compling_style.pdf +% +% This includes all the various quirks of the style guide, for example: +% - a chapter from a monograph (@inbook) has no page numbers. +% - an article from an edited volume (@incollection) has page numbers +% after the publisher and address. +% - an article from a proceedings volume (@inproceedings) has page +% numbers before the publisher and address. +% +% Where the style guide was inconsistent or not specific enough I +% looked at actual published articles and exercised my own judgment. +% I noticed two inconsistencies in the style guide: +% +% - The style guide gives one example of an article from an edited +% volume with the editor's name spelled out in full, and another +% with the editors' names abbreviated. I chose to accept the first +% one as correct, since the style guide generally shuns abbreviations, +% and editors' names are also spelled out in some recently published +% articles. +% +% - The style guide gives one example of a reference where the word +% "and" between two authors is preceded by a comma. This is most +% likely a typo, since in all other cases with just two authors or +% editors there is no comma before the word "and". +% +% One case where the style guide is not being specific is the placement +% of the edition number, for which no example is given. I chose to put +% it immediately after the title, which I (subjectively) find natural, +% and is also the place of the edition in a few recently published +% articles. +% +% This file correctly reproduces all of the examples in the official +% style guide, except for the two inconsistencies noted above. I even +% managed to get it to correctly format the proceedings example which +% has an organization, a publisher, and two addresses (the conference +% location and the publisher's address), though I cheated a bit by +% putting the conference location and month as part of the title field; +% I feel that in this case the conference location and month can be +% considered as part of the title, and that adding a location field +% is not justified. Note also that a location field is not standard, +% so entries made with this field would not port nicely to other styles. +% However, if authors feel that there's a need for a location field +% then tell me and I'll see what I can do. +% +% The file also produces to my satisfaction all the bibliographical +% entries in my recent (joint) submission to CL (this was the original +% motivation for creating the file). I also tested it by running it +% on a larger set of entries and eyeballing the results. There may of +% course still be errors, especially with combinations of fields that +% are not that common, or with cross-references (which I seldom use). +% If you find such errors please write to me. +% +% I hope people find this file useful. Please email me with comments +% and suggestions. +% +% Ron Artstein +% artstein [at] essex.ac.uk +% August 22, 2005. +% +% Some technical notes. +% +% This file is based on a file generated with the package +% by Patrick W. Daly (see selected options below), which was then +% manually customized to conform with certain CL requirements which +% cannot be met by . Departures from the generated file +% include: +% +% Function inbook: moved publisher and address to the end; moved +% edition after title; replaced function format.chapter.pages by +% new function format.chapter to output chapter without pages. +% +% Function inproceedings: moved publisher and address to the end; +% replaced function format.in.ed.booktitle by new function +% format.in.booktitle to output the proceedings title without +% the editor. +% +% Functions book, incollection, manual: moved edition after title. +% +% Function mastersthesis: formatted title as for articles (unlike +% phdthesis which is formatted as book) and added month. +% +% Function proceedings: added new.sentence between organization and +% publisher when both are present. +% +% Function format.lab.names: modified so that it gives all the +% authors' surnames for in-text citations for one, two and three +% authors and only uses "et. al" for works with four authors or more +% (thanks to Ken Shan for convincing me to go through the trouble of +% modifying this function rather than using unreliable hacks). +% +% Changes: +% +% 2006-10-27: Changed function reverse.pass so that the extra label is +% enclosed in parentheses when the year field ends in an uppercase or +% lowercase letter (change modeled after Uli Sauerland's modification +% of nals.bst). RA. +% +% +% The preamble of the generated file begins below: +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% +%% This is file `compling.bst', +%% generated with the docstrip utility. +%% +%% The original source files were: +%% +%% merlin.mbs (with options: `ay,nat,vonx,nm-revv1,jnrlst,keyxyr,blkyear,dt-beg,yr-per,note-yr,num-xser,pre-pub,xedn,nfss') +%% ---------------------------------------- +%% *** Intended for the journal Computational Linguistics *** +%% +%% Copyright 1994-2002 Patrick W Daly + % =============================================================== + % IMPORTANT NOTICE: + % This bibliographic style (bst) file has been generated from one or + % more master bibliographic style (mbs) files, listed above. + % + % This generated file can be redistributed and/or modified under the terms + % of the LaTeX Project Public License Distributed from CTAN + % archives in directory macros/latex/base/lppl.txt; either + % version 1 of the License, or any later version. + % =============================================================== + % Name and version information of the main mbs file: + % \ProvidesFile{merlin.mbs}[2002/10/21 4.05 (PWD, AO, DPC)] + % For use with BibTeX version 0.99a or later + %------------------------------------------------------------------- + % This bibliography style file is intended for texts in ENGLISH + % This is an author-year citation style bibliography. As such, it is + % non-standard LaTeX, and requires a special package file to function properly. + % Such a package is natbib.sty by Patrick W. Daly + % The form of the \bibitem entries is + % \bibitem[Jones et al.(1990)]{key}... + % \bibitem[Jones et al.(1990)Jones, Baker, and Smith]{key}... + % The essential feature is that the label (the part in brackets) consists + % of the author names, as they should appear in the citation, with the year + % in parentheses following. There must be no space before the opening + % parenthesis! + % With natbib v5.3, a full list of authors may also follow the year. + % In natbib.sty, it is possible to define the type of enclosures that is + % really wanted (brackets or parentheses), but in either case, there must + % be parentheses in the label. + % The \cite command functions as follows: + % \citet{key} ==>> Jones et al. (1990) + % \citet*{key} ==>> Jones, Baker, and Smith (1990) + % \citep{key} ==>> (Jones et al., 1990) + % \citep*{key} ==>> (Jones, Baker, and Smith, 1990) + % \citep[chap. 2]{key} ==>> (Jones et al., 1990, chap. 2) + % \citep[e.g.][]{key} ==>> (e.g. Jones et al., 1990) + % \citep[e.g.][p. 32]{key} ==>> (e.g. Jones et al., p. 32) + % \citeauthor{key} ==>> Jones et al. + % \citeauthor*{key} ==>> Jones, Baker, and Smith + % \citeyear{key} ==>> 1990 + %--------------------------------------------------------------------- + +ENTRY + { address + author + booktitle + chapter + edition + editor + howpublished + institution + journal + key + month + note + number + organization + pages + publisher + school + series + title + type + volume + year + eprint % urlbst + doi % urlbst + pubmed % urlbst + url % urlbst + lastchecked % urlbst + } + {} + { label extra.label sort.label short.list } +INTEGERS { output.state before.all mid.sentence after.sentence after.block } +% urlbst... +% urlbst constants and state variables +STRINGS { urlintro + eprinturl eprintprefix doiprefix doiurl pubmedprefix pubmedurl + citedstring onlinestring linktextstring + openinlinelink closeinlinelink } +INTEGERS { hrefform inlinelinks makeinlinelink + addeprints adddoiresolver addpubmedresolver } +FUNCTION {init.urlbst.variables} +{ + % The following constants may be adjusted by hand, if desired + + % The first set allow you to enable or disable certain functionality. + #1 'addeprints := % 0=no eprints; 1=include eprints + #1 'adddoiresolver := % 0=no DOI resolver; 1=include it + #1 'addpubmedresolver := % 0=no PUBMED resolver; 1=include it + #2 'hrefform := % 0=no crossrefs; 1=hypertex xrefs; 2=hyperref refs + #1 'inlinelinks := % 0=URLs explicit; 1=URLs attached to titles + + % String constants, which you _might_ want to tweak. + "URL: " 'urlintro := % prefix before URL; typically "Available from:" or "URL": + "online" 'onlinestring := % indication that resource is online; typically "online" + "cited " 'citedstring := % indicator of citation date; typically "cited " + "[link]" 'linktextstring := % dummy link text; typically "[link]" + "http://arxiv.org/abs/" 'eprinturl := % prefix to make URL from eprint ref + "arXiv:" 'eprintprefix := % text prefix printed before eprint ref; typically "arXiv:" + "https://doi.org/" 'doiurl := % prefix to make URL from DOI + "doi:" 'doiprefix := % text prefix printed before DOI ref; typically "doi:" + "http://www.ncbi.nlm.nih.gov/pubmed/" 'pubmedurl := % prefix to make URL from PUBMED + "PMID:" 'pubmedprefix := % text prefix printed before PUBMED ref; typically "PMID:" + + % The following are internal state variables, not configuration constants, + % so they shouldn't be fiddled with. + #0 'makeinlinelink := % state variable managed by possibly.setup.inlinelink + "" 'openinlinelink := % ditto + "" 'closeinlinelink := % ditto +} +INTEGERS { + bracket.state + outside.brackets + open.brackets + within.brackets + close.brackets +} +% ...urlbst to here +FUNCTION {init.state.consts} +{ #0 'outside.brackets := % urlbst... + #1 'open.brackets := + #2 'within.brackets := + #3 'close.brackets := % ...urlbst to here + + #0 'before.all := + #1 'mid.sentence := + #2 'after.sentence := + #3 'after.block := +} +STRINGS { s t} +% urlbst +FUNCTION {output.nonnull.original} +{ 's := + output.state mid.sentence = + { ", " * write$ } + { output.state after.block = + { add.period$ write$ + newline$ + "\newblock " write$ + } + { output.state before.all = + 'write$ + { add.period$ " " * write$ } + if$ + } + if$ + mid.sentence 'output.state := + } + if$ + s +} + +% urlbst... +% The following three functions are for handling inlinelink. They wrap +% a block of text which is potentially output with write$ by multiple +% other functions, so we don't know the content a priori. +% They communicate between each other using the variables makeinlinelink +% (which is true if a link should be made), and closeinlinelink (which holds +% the string which should close any current link. They can be called +% at any time, but start.inlinelink will be a no-op unless something has +% previously set makeinlinelink true, and the two ...end.inlinelink functions +% will only do their stuff if start.inlinelink has previously set +% closeinlinelink to be non-empty. +% (thanks to 'ijvm' for suggested code here) +FUNCTION {uand} +{ 'skip$ { pop$ #0 } if$ } % 'and' (which isn't defined at this point in the file) +FUNCTION {possibly.setup.inlinelink} +{ makeinlinelink hrefform #0 > uand + { doi empty$ adddoiresolver uand + { pubmed empty$ addpubmedresolver uand + { eprint empty$ addeprints uand + { url empty$ + { "" } + { url } + if$ } + { eprinturl eprint * } + if$ } + { pubmedurl pubmed * } + if$ } + { doiurl doi * } + if$ + % an appropriately-formatted URL is now on the stack + hrefform #1 = % hypertex + { "\special {html: }{" * 'openinlinelink := + "\special {html:}" 'closeinlinelink := } + { "\href {" swap$ * "} {" * 'openinlinelink := % hrefform=#2 -- hyperref + % the space between "} {" matters: a URL of just the right length can cause "\% newline em" + "}" 'closeinlinelink := } + if$ + #0 'makeinlinelink := + } + 'skip$ + if$ % makeinlinelink +} +FUNCTION {add.inlinelink} +{ openinlinelink empty$ + 'skip$ + { openinlinelink swap$ * closeinlinelink * + "" 'openinlinelink := + } + if$ +} +FUNCTION {output.nonnull} +{ % Save the thing we've been asked to output + 's := + % If the bracket-state is close.brackets, then add a close-bracket to + % what is currently at the top of the stack, and set bracket.state + % to outside.brackets + bracket.state close.brackets = + { "]" * + outside.brackets 'bracket.state := + } + 'skip$ + if$ + bracket.state outside.brackets = + { % We're outside all brackets -- this is the normal situation. + % Write out what's currently at the top of the stack, using the + % original output.nonnull function. + s + add.inlinelink + output.nonnull.original % invoke the original output.nonnull + } + { % Still in brackets. Add open-bracket or (continuation) comma, add the + % new text (in s) to the top of the stack, and move to the close-brackets + % state, ready for next time (unless inbrackets resets it). If we come + % into this branch, then output.state is carefully undisturbed. + bracket.state open.brackets = + { " [" * } + { ", " * } % bracket.state will be within.brackets + if$ + s * + close.brackets 'bracket.state := + } + if$ +} + +% Call this function just before adding something which should be presented in +% brackets. bracket.state is handled specially within output.nonnull. +FUNCTION {inbrackets} +{ bracket.state close.brackets = + { within.brackets 'bracket.state := } % reset the state: not open nor closed + { open.brackets 'bracket.state := } + if$ +} + +FUNCTION {format.lastchecked} +{ lastchecked empty$ + { "" } + { inbrackets citedstring lastchecked * } + if$ +} +% ...urlbst to here +FUNCTION {output} +{ duplicate$ empty$ + 'pop$ + 'output.nonnull + if$ +} +FUNCTION {output.check} +{ 't := + duplicate$ empty$ + { pop$ "empty " t * " in " * cite$ * warning$ } + 'output.nonnull + if$ +} +FUNCTION {fin.entry.original} % urlbst (renamed from fin.entry, so it can be wrapped below) +{ add.period$ + write$ + newline$ +} + +FUNCTION {new.block} +{ output.state before.all = + 'skip$ + { after.block 'output.state := } + if$ +} +FUNCTION {new.sentence} +{ output.state after.block = + 'skip$ + { output.state before.all = + 'skip$ + { after.sentence 'output.state := } + if$ + } + if$ +} +FUNCTION {add.blank} +{ " " * before.all 'output.state := +} + +FUNCTION {date.block} +{ + new.block +} + +FUNCTION {not} +{ { #0 } + { #1 } + if$ +} +FUNCTION {and} +{ 'skip$ + { pop$ #0 } + if$ +} +FUNCTION {or} +{ { pop$ #1 } + 'skip$ + if$ +} +FUNCTION {new.block.checkb} +{ empty$ + swap$ empty$ + and + 'skip$ + 'new.block + if$ +} +FUNCTION {field.or.null} +{ duplicate$ empty$ + { pop$ "" } + 'skip$ + if$ +} +FUNCTION {emphasize} +{ duplicate$ empty$ + { pop$ "" } + { "\emph{" swap$ * "}" * } + if$ +} +FUNCTION {tie.or.space.prefix} +{ duplicate$ text.length$ #3 < + { "~" } + { " " } + if$ + swap$ +} + +FUNCTION {capitalize} +{ "u" change.case$ "t" change.case$ } + +FUNCTION {space.word} +{ " " swap$ * " " * } + % Here are the language-specific definitions for explicit words. + % Each function has a name bbl.xxx where xxx is the English word. + % The language selected here is ENGLISH +FUNCTION {bbl.and} +{ "and"} + +FUNCTION {bbl.etal} +{ "et~al." } + +FUNCTION {bbl.editors} +{ "editors" } + +FUNCTION {bbl.editor} +{ "editor" } + +FUNCTION {bbl.edby} +{ "edited by" } + +FUNCTION {bbl.edition} +{ "edition" } + +FUNCTION {bbl.volume} +{ "volume" } + +FUNCTION {bbl.of} +{ "of" } + +FUNCTION {bbl.number} +{ "number" } + +FUNCTION {bbl.nr} +{ "no." } + +FUNCTION {bbl.in} +{ "in" } + +FUNCTION {bbl.pages} +{ "pages" } + +FUNCTION {bbl.page} +{ "page" } + +FUNCTION {bbl.chapter} +{ "chapter" } + +FUNCTION {bbl.techrep} +{ "Technical Report" } + +FUNCTION {bbl.mthesis} +{ "Master's thesis" } + +FUNCTION {bbl.phdthesis} +{ "Ph.D. thesis" } + +MACRO {jan} {"January"} + +MACRO {feb} {"February"} + +MACRO {mar} {"March"} + +MACRO {apr} {"April"} + +MACRO {may} {"May"} + +MACRO {jun} {"June"} + +MACRO {jul} {"July"} + +MACRO {aug} {"August"} + +MACRO {sep} {"September"} + +MACRO {oct} {"October"} + +MACRO {nov} {"November"} + +MACRO {dec} {"December"} + +MACRO {acmcs} {"ACM Computing Surveys"} + +MACRO {acta} {"Acta Informatica"} + +MACRO {cacm} {"Communications of the ACM"} + +MACRO {ibmjrd} {"IBM Journal of Research and Development"} + +MACRO {ibmsj} {"IBM Systems Journal"} + +MACRO {ieeese} {"IEEE Transactions on Software Engineering"} + +MACRO {ieeetc} {"IEEE Transactions on Computers"} + +MACRO {ieeetcad} + {"IEEE Transactions on Computer-Aided Design of Integrated Circuits"} + +MACRO {ipl} {"Information Processing Letters"} + +MACRO {jacm} {"Journal of the ACM"} + +MACRO {jcss} {"Journal of Computer and System Sciences"} + +MACRO {scp} {"Science of Computer Programming"} + +MACRO {sicomp} {"SIAM Journal on Computing"} + +MACRO {tocs} {"ACM Transactions on Computer Systems"} + +MACRO {tods} {"ACM Transactions on Database Systems"} + +MACRO {tog} {"ACM Transactions on Graphics"} + +MACRO {toms} {"ACM Transactions on Mathematical Software"} + +MACRO {toois} {"ACM Transactions on Office Information Systems"} + +MACRO {toplas} {"ACM Transactions on Programming Languages and Systems"} + +MACRO {tcs} {"Theoretical Computer Science"} +FUNCTION {bibinfo.check} +{ swap$ + duplicate$ missing$ + { + pop$ pop$ + "" + } + { duplicate$ empty$ + { + swap$ pop$ + } + { swap$ + pop$ + } + if$ + } + if$ +} +FUNCTION {bibinfo.warn} +{ swap$ + duplicate$ missing$ + { + swap$ "missing " swap$ * " in " * cite$ * warning$ pop$ + "" + } + { duplicate$ empty$ + { + swap$ "empty " swap$ * " in " * cite$ * warning$ + } + { swap$ + pop$ + } + if$ + } + if$ +} +STRINGS { bibinfo} +INTEGERS { nameptr namesleft numnames } + +FUNCTION {format.names} +{ 'bibinfo := + duplicate$ empty$ 'skip$ { + 's := + "" 't := + #1 'nameptr := + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + duplicate$ #1 > + { "{ff~}{vv~}{ll}{, jj}" } + { "{ff~}{vv~}{ll}{, jj}" } % first name first for first author +% { "{vv~}{ll}{, ff}{, jj}" } % last name first for first author + if$ + format.name$ + bibinfo bibinfo.check + 't := + nameptr #1 > + { + namesleft #1 > + { ", " * t * } + { + numnames #2 > + { "," * } + 'skip$ + if$ + s nameptr "{ll}" format.name$ duplicate$ "others" = + { 't := } + { pop$ } + if$ + t "others" = + { + " " * bbl.etal * + } + { + bbl.and + space.word * t * + } + if$ + } + if$ + } + 't + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ + } if$ +} +FUNCTION {format.names.ed} +{ + 'bibinfo := + duplicate$ empty$ 'skip$ { + 's := + "" 't := + #1 'nameptr := + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + "{ff~}{vv~}{ll}{, jj}" + format.name$ + bibinfo bibinfo.check + 't := + nameptr #1 > + { + namesleft #1 > + { ", " * t * } + { + numnames #2 > + { "," * } + 'skip$ + if$ + s nameptr "{ll}" format.name$ duplicate$ "others" = + { 't := } + { pop$ } + if$ + t "others" = + { + + " " * bbl.etal * + } + { + bbl.and + space.word * t * + } + if$ + } + if$ + } + 't + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ + } if$ +} +FUNCTION {format.key} +{ empty$ + { key field.or.null } + { "" } + if$ +} + +FUNCTION {format.authors} +{ author "author" format.names +} +FUNCTION {get.bbl.editor} +{ editor num.names$ #1 > 'bbl.editors 'bbl.editor if$ } + +FUNCTION {format.editors} +{ editor "editor" format.names duplicate$ empty$ 'skip$ + { + "," * + " " * + get.bbl.editor + * + } + if$ +} +FUNCTION {format.note} +{ + note empty$ + { "" } + { note #1 #1 substring$ + duplicate$ "{" = + 'skip$ + { output.state mid.sentence = + { "l" } + { "u" } + if$ + change.case$ + } + if$ + note #2 global.max$ substring$ * "note" bibinfo.check + } + if$ +} + +FUNCTION {format.title} +{ title + duplicate$ empty$ 'skip$ + { "t" change.case$ } + if$ + "title" bibinfo.check +} +FUNCTION {format.full.names} +{'s := + "" 't := + #1 'nameptr := + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + "{vv~}{ll}" format.name$ + 't := + nameptr #1 > + { + namesleft #1 > + { ", " * t * } + { + s nameptr "{ll}" format.name$ duplicate$ "others" = + { 't := } + { pop$ } + if$ + t "others" = + { + " " * bbl.etal * + } + { + numnames #2 > + { "," * } + 'skip$ + if$ + bbl.and + space.word * t * + } + if$ + } + if$ + } + 't + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ +} + +FUNCTION {author.editor.key.full} +{ author empty$ + { editor empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { editor format.full.names } + if$ + } + { author format.full.names } + if$ +} + +FUNCTION {author.key.full} +{ author empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { author format.full.names } + if$ +} + +FUNCTION {editor.key.full} +{ editor empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { editor format.full.names } + if$ +} + +FUNCTION {make.full.names} +{ type$ "book" = + type$ "inbook" = + or + 'author.editor.key.full + { type$ "proceedings" = + 'editor.key.full + 'author.key.full + if$ + } + if$ +} + +FUNCTION {output.bibitem.original} % urlbst (renamed from output.bibitem, so it can be wrapped below) +{ newline$ + "\bibitem[{" write$ + label write$ + ")" make.full.names duplicate$ short.list = + { pop$ } + { * } + if$ + "}]{" * write$ + cite$ write$ + "}" write$ + newline$ + "" + before.all 'output.state := +} + +FUNCTION {n.dashify} +{ + 't := + "" + { t empty$ not } + { t #1 #1 substring$ "-" = + { t #1 #2 substring$ "--" = not + { "--" * + t #2 global.max$ substring$ 't := + } + { { t #1 #1 substring$ "-" = } + { "-" * + t #2 global.max$ substring$ 't := + } + while$ + } + if$ + } + { t #1 #1 substring$ * + t #2 global.max$ substring$ 't := + } + if$ + } + while$ +} + +FUNCTION {word.in} +{ bbl.in capitalize + " " * } + +FUNCTION {format.date} +{ year "year" bibinfo.check duplicate$ empty$ + { + } + 'skip$ + if$ + extra.label * + before.all 'output.state := + after.sentence 'output.state := +} +FUNCTION {format.btitle} +{ title "title" bibinfo.check + duplicate$ empty$ 'skip$ + { + emphasize + } + if$ +} +FUNCTION {either.or.check} +{ empty$ + 'pop$ + { "can't use both " swap$ * " fields in " * cite$ * warning$ } + if$ +} +FUNCTION {format.bvolume} +{ volume empty$ + { "" } + { bbl.volume volume tie.or.space.prefix + "volume" bibinfo.check * * + series "series" bibinfo.check + duplicate$ empty$ 'pop$ + { swap$ bbl.of space.word * swap$ + emphasize * } + if$ + "volume and number" number either.or.check + } + if$ +} +FUNCTION {format.number.series} +{ volume empty$ + { number empty$ + { series field.or.null } + { series empty$ + { number "number" bibinfo.check } + { output.state mid.sentence = + { bbl.number } + { bbl.number capitalize } + if$ + number tie.or.space.prefix "number" bibinfo.check * * + bbl.in space.word * + series "series" bibinfo.check * + } + if$ + } + if$ + } + { "" } + if$ +} + +FUNCTION {format.edition} +{ edition duplicate$ empty$ 'skip$ + { + output.state mid.sentence = + { "l" } + { "t" } + if$ change.case$ + "edition" bibinfo.check + " " * bbl.edition * + } + if$ +} +INTEGERS { multiresult } +FUNCTION {multi.page.check} +{ 't := + #0 'multiresult := + { multiresult not + t empty$ not + and + } + { t #1 #1 substring$ + duplicate$ "-" = + swap$ duplicate$ "," = + swap$ "+" = + or or + { #1 'multiresult := } + { t #2 global.max$ substring$ 't := } + if$ + } + while$ + multiresult +} +FUNCTION {format.pages} +{ pages duplicate$ empty$ 'skip$ + { duplicate$ multi.page.check + { + bbl.pages swap$ + n.dashify + } + { + bbl.page swap$ + } + if$ + tie.or.space.prefix + "pages" bibinfo.check + * * + } + if$ +} +FUNCTION {format.journal.pages} +{ pages duplicate$ empty$ 'pop$ + { swap$ duplicate$ empty$ + { pop$ pop$ format.pages } + { + ":" * + swap$ + n.dashify + "pages" bibinfo.check + * + } + if$ + } + if$ +} +FUNCTION {format.vol.num.pages} +{ volume field.or.null + duplicate$ empty$ 'skip$ + { + "volume" bibinfo.check + } + if$ + number "number" bibinfo.check duplicate$ empty$ 'skip$ + { + swap$ duplicate$ empty$ + { "there's a number but no volume in " cite$ * warning$ } + 'skip$ + if$ + swap$ + "(" swap$ * ")" * + } + if$ * + format.journal.pages +} + +FUNCTION {format.chapter} +{ chapter empty$ + 'format.pages + { type empty$ + { bbl.chapter } + { type "l" change.case$ + "type" bibinfo.check + } + if$ + chapter tie.or.space.prefix + "chapter" bibinfo.check + * * + } + if$ +} + +FUNCTION {format.chapter.pages} +{ chapter empty$ + 'format.pages + { type empty$ + { bbl.chapter } + { type "l" change.case$ + "type" bibinfo.check + } + if$ + chapter tie.or.space.prefix + "chapter" bibinfo.check + * * + pages empty$ + 'skip$ + { ", " * format.pages * } + if$ + } + if$ +} + +FUNCTION {format.booktitle} +{ + booktitle "booktitle" bibinfo.check + emphasize +} +FUNCTION {format.in.booktitle} +{ format.booktitle duplicate$ empty$ 'skip$ + { + word.in swap$ * + } + if$ +} +FUNCTION {format.in.ed.booktitle} +{ format.booktitle duplicate$ empty$ 'skip$ + { + editor "editor" format.names.ed duplicate$ empty$ 'pop$ + { + "," * + " " * + get.bbl.editor + ", " * + * swap$ + * } + if$ + word.in swap$ * + } + if$ +} +FUNCTION {format.thesis.type} +{ type duplicate$ empty$ + 'pop$ + { swap$ pop$ + "t" change.case$ "type" bibinfo.check + } + if$ +} +FUNCTION {format.tr.number} +{ number "number" bibinfo.check + type duplicate$ empty$ + { pop$ bbl.techrep } + 'skip$ + if$ + "type" bibinfo.check + swap$ duplicate$ empty$ + { pop$ "t" change.case$ } + { tie.or.space.prefix * * } + if$ +} +FUNCTION {format.article.crossref} +{ + word.in + " \cite{" * crossref * "}" * +} +FUNCTION {format.book.crossref} +{ volume duplicate$ empty$ + { "empty volume in " cite$ * "'s crossref of " * crossref * warning$ + pop$ word.in + } + { bbl.volume + capitalize + swap$ tie.or.space.prefix "volume" bibinfo.check * * bbl.of space.word * + } + if$ + " \cite{" * crossref * "}" * +} +FUNCTION {format.incoll.inproc.crossref} +{ + word.in + " \cite{" * crossref * "}" * +} +FUNCTION {format.org.or.pub} +{ 't := + "" + address empty$ t empty$ and + 'skip$ + { + t empty$ + { address "address" bibinfo.check * + } + { t * + address empty$ + 'skip$ + { ", " * address "address" bibinfo.check * } + if$ + } + if$ + } + if$ +} +FUNCTION {format.publisher.address} +{ publisher "publisher" bibinfo.warn format.org.or.pub +} + +FUNCTION {format.organization.address} +{ organization "organization" bibinfo.check format.org.or.pub +} + +% urlbst... +% Functions for making hypertext links. +% In all cases, the stack has (link-text href-url) +% +% make 'null' specials +FUNCTION {make.href.null} +{ + pop$ +} +% make hypertex specials +FUNCTION {make.href.hypertex} +{ + "\special {html: }" * swap$ * + "\special {html:}" * +} +% make hyperref specials +FUNCTION {make.href.hyperref} +{ + "\href {" swap$ * "} {\path{" * swap$ * "}}" * +} +FUNCTION {make.href} +{ hrefform #2 = + 'make.href.hyperref % hrefform = 2 + { hrefform #1 = + 'make.href.hypertex % hrefform = 1 + 'make.href.null % hrefform = 0 (or anything else) + if$ + } + if$ +} + +% If inlinelinks is true, then format.url should be a no-op, since it's +% (a) redundant, and (b) could end up as a link-within-a-link. +FUNCTION {format.url} +{ inlinelinks #1 = url empty$ or + { "" } + { hrefform #1 = + { % special case -- add HyperTeX specials + urlintro "\url{" url * "}" * url make.href.hypertex * } + { urlintro "\url{" * url * "}" * } + if$ + } + if$ +} + +FUNCTION {format.eprint} +{ eprint empty$ + { "" } + { eprintprefix eprint * eprinturl eprint * make.href } + if$ +} + +FUNCTION {format.doi} +{ doi empty$ + { "" } + { doiprefix doi * doiurl doi * make.href } + if$ +} + +FUNCTION {format.pubmed} +{ pubmed empty$ + { "" } + { pubmedprefix pubmed * pubmedurl pubmed * make.href } + if$ +} + +% Output a URL. We can't use the more normal idiom (something like +% `format.url output'), because the `inbrackets' within +% format.lastchecked applies to everything between calls to `output', +% so that `format.url format.lastchecked * output' ends up with both +% the URL and the lastchecked in brackets. +FUNCTION {output.url} +{ url empty$ + 'skip$ + { new.block + format.url output + format.lastchecked output + } + if$ +} + +FUNCTION {output.web.refs} +{ + new.block + inlinelinks + 'skip$ % links were inline -- don't repeat them + { + output.url + addeprints eprint empty$ not and + { format.eprint output.nonnull } + 'skip$ + if$ + adddoiresolver doi empty$ not and + { format.doi output.nonnull } + 'skip$ + if$ + addpubmedresolver pubmed empty$ not and + { format.pubmed output.nonnull } + 'skip$ + if$ + } + if$ +} + +% Wrapper for output.bibitem.original. +% If the URL field is not empty, set makeinlinelink to be true, +% so that an inline link will be started at the next opportunity +FUNCTION {output.bibitem} +{ outside.brackets 'bracket.state := + output.bibitem.original + inlinelinks url empty$ not doi empty$ not or pubmed empty$ not or eprint empty$ not or and + { #1 'makeinlinelink := } + { #0 'makeinlinelink := } + if$ +} + +% Wrapper for fin.entry.original +FUNCTION {fin.entry} +{ output.web.refs % urlbst + makeinlinelink % ooops, it appears we didn't have a title for inlinelink + { possibly.setup.inlinelink % add some artificial link text here, as a fallback + linktextstring output.nonnull } + 'skip$ + if$ + bracket.state close.brackets = % urlbst + { "]" * } + 'skip$ + if$ + fin.entry.original +} + +% Webpage entry type. +% Title and url fields required; +% author, note, year, month, and lastchecked fields optional +% See references +% ISO 690-2 http://www.nlc-bnc.ca/iso/tc46sc9/standard/690-2e.htm +% http://www.classroom.net/classroom/CitingNetResources.html +% http://neal.ctstateu.edu/history/cite.html +% http://www.cas.usf.edu/english/walker/mla.html +% for citation formats for web pages. +FUNCTION {webpage} +{ output.bibitem + author empty$ + { editor empty$ + 'skip$ % author and editor both optional + { format.editors output.nonnull } + if$ + } + { editor empty$ + { format.authors output.nonnull } + { "can't use both author and editor fields in " cite$ * warning$ } + if$ + } + if$ + new.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ + format.title "title" output.check + inbrackets onlinestring output + new.block + year empty$ + 'skip$ + { format.date "year" output.check } + if$ + % We don't need to output the URL details ('lastchecked' and 'url'), + % because fin.entry does that for us, using output.web.refs. The only + % reason we would want to put them here is if we were to decide that + % they should go in front of the rather miscellaneous information in 'note'. + new.block + note output + fin.entry +} +% ...urlbst to here + + +FUNCTION {article} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title "title" output.check + new.block + crossref missing$ + { + journal + "journal" bibinfo.check + emphasize + "journal" output.check + possibly.setup.inlinelink format.vol.num.pages output% urlbst + } + { format.article.crossref output.nonnull + format.pages output + } + if$ + new.block + format.note output + fin.entry +} +FUNCTION {book} +{ output.bibitem + author empty$ + { format.editors "author and editor" output.check + editor format.key output + } + { format.authors output.nonnull + crossref missing$ + { "author and editor" editor either.or.check } + 'skip$ + if$ + } + if$ + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.btitle "title" output.check + format.edition output + crossref missing$ + { format.bvolume output + new.block + format.number.series output + new.sentence + format.publisher.address output + } + { + new.block + format.book.crossref output.nonnull + } + if$ + new.block + format.note output + fin.entry +} +FUNCTION {booklet} +{ output.bibitem + format.authors output + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title "title" output.check + new.block + howpublished "howpublished" bibinfo.check output + address "address" bibinfo.check output + new.block + format.note output + fin.entry +} + +FUNCTION {inbook} +{ output.bibitem + author empty$ + { format.editors "author and editor" output.check + editor format.key output + } + { format.authors output.nonnull + crossref missing$ + { "author and editor" editor either.or.check } + 'skip$ + if$ + } + if$ + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.btitle "title" output.check + format.edition output + crossref missing$ + { + format.bvolume output + format.number.series output + format.chapter "chapter" output.check + new.sentence + format.publisher.address output + new.block + } + { + format.chapter "chapter" output.check + new.block + format.book.crossref output.nonnull + } + if$ + new.block + format.note output + fin.entry +} + +FUNCTION {incollection} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title "title" output.check + new.block + crossref missing$ + { format.in.ed.booktitle "booktitle" output.check + format.edition output + format.bvolume output + format.number.series output + format.chapter.pages output + new.sentence + format.publisher.address output + } + { format.incoll.inproc.crossref output.nonnull + format.chapter.pages output + } + if$ + new.block + format.note output + fin.entry +} +FUNCTION {inproceedings} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title "title" output.check + new.block + crossref missing$ + { format.in.booktitle "booktitle" output.check + format.bvolume output + format.number.series output + format.pages output + address "address" bibinfo.check output + new.sentence + organization "organization" bibinfo.check output + publisher "publisher" bibinfo.check output + } + { format.incoll.inproc.crossref output.nonnull + format.pages output + } + if$ + new.block + format.note output + fin.entry +} +FUNCTION {conference} { inproceedings } +FUNCTION {manual} +{ output.bibitem + format.authors output + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.btitle "title" output.check + format.edition output + organization address new.block.checkb + organization "organization" bibinfo.check output + address "address" bibinfo.check output + new.block + format.note output + fin.entry +} + +FUNCTION {mastersthesis} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title + "title" output.check + new.block + bbl.mthesis format.thesis.type output.nonnull + school "school" bibinfo.warn output + address "address" bibinfo.check output + month "month" bibinfo.check output + new.block + format.note output + fin.entry +} + +FUNCTION {misc} +{ output.bibitem + format.authors output + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title output + new.block + howpublished "howpublished" bibinfo.check output + new.block + format.note output + fin.entry +} +FUNCTION {phdthesis} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.btitle + "title" output.check + new.block + bbl.phdthesis format.thesis.type output.nonnull + school "school" bibinfo.warn output + address "address" bibinfo.check output + new.block + format.note output + fin.entry +} + +FUNCTION {proceedings} +{ output.bibitem + format.editors output + editor format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.btitle "title" output.check + format.bvolume output + format.number.series output + new.sentence + publisher empty$ + { format.organization.address output } + { organization "organization" bibinfo.check output + new.sentence + format.publisher.address output + } + if$ + new.block + format.note output + fin.entry +} + +FUNCTION {techreport} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title + "title" output.check + new.block + format.tr.number output.nonnull + institution "institution" bibinfo.warn output + address "address" bibinfo.check output + new.block + format.note output + fin.entry +} + +FUNCTION {unpublished} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title "title" output.check + new.block + format.note "note" output.check + fin.entry +} + +FUNCTION {default.type} { misc } +READ +FUNCTION {sortify} +{ purify$ + "l" change.case$ +} +INTEGERS { len } +FUNCTION {chop.word} +{ 's := + 'len := + s #1 len substring$ = + { s len #1 + global.max$ substring$ } + 's + if$ +} +FUNCTION {format.lab.names} +{ 's := + "" 't := + s #1 "{vv~}{ll}" format.name$ + s num.names$ duplicate$ + #2 > + { pop$ + " " * bbl.etal * + } + { #2 < + 'skip$ + { s #2 "{ff }{vv }{ll}{ jj}" format.name$ "others" = + { + " " * bbl.etal * + } + { bbl.and space.word * s #2 "{vv~}{ll}" format.name$ + * } + if$ + } + if$ + } + if$ +} + +FUNCTION {author.key.label} +{ author empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { author format.lab.names } + if$ +} + +FUNCTION {author.editor.key.label} +{ author empty$ + { editor empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { editor format.lab.names } + if$ + } + { author format.lab.names } + if$ +} + +FUNCTION {editor.key.label} +{ editor empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { editor format.lab.names } + if$ +} + +FUNCTION {calc.short.authors} +{ type$ "book" = + type$ "inbook" = + or + 'author.editor.key.label + { type$ "proceedings" = + 'editor.key.label + 'author.key.label + if$ + } + if$ + 'short.list := +} + +FUNCTION {calc.label} +{ calc.short.authors + short.list + "(" + * + year duplicate$ empty$ + short.list key field.or.null = or + { pop$ "" } + 'skip$ + if$ + * + 'label := +} + +FUNCTION {sort.format.names} +{ 's := + #1 'nameptr := + "" + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + "{ll{ }}{ ff{ }}{ jj{ }}" + format.name$ 't := + nameptr #1 > + { + " " * + namesleft #1 = t "others" = and + { "zzzzz" * } + { t sortify * } + if$ + } + { t sortify * } + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ +} + +FUNCTION {sort.format.title} +{ 't := + "A " #2 + "An " #3 + "The " #4 t chop.word + chop.word + chop.word + sortify + #1 global.max$ substring$ +} +FUNCTION {author.sort} +{ author empty$ + { key empty$ + { "to sort, need author or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { author sort.format.names } + if$ +} +FUNCTION {author.editor.sort} +{ author empty$ + { editor empty$ + { key empty$ + { "to sort, need author, editor, or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { editor sort.format.names } + if$ + } + { author sort.format.names } + if$ +} +FUNCTION {editor.sort} +{ editor empty$ + { key empty$ + { "to sort, need editor or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { editor sort.format.names } + if$ +} +FUNCTION {presort} +{ calc.label + label sortify + " " + * + type$ "book" = + type$ "inbook" = + or + 'author.editor.sort + { type$ "proceedings" = + 'editor.sort + 'author.sort + if$ + } + if$ + #1 entry.max$ substring$ + 'sort.label := + sort.label + * + " " + * + title field.or.null + sort.format.title + * + #1 entry.max$ substring$ + 'sort.key$ := +} + +ITERATE {presort} +SORT +STRINGS { last.label next.extra } +INTEGERS { last.extra.num number.label } +FUNCTION {initialize.extra.label.stuff} +{ #0 int.to.chr$ 'last.label := + "" 'next.extra := + #0 'last.extra.num := + #0 'number.label := +} +FUNCTION {forward.pass} +{ last.label label = + { last.extra.num #1 + 'last.extra.num := + last.extra.num int.to.chr$ 'extra.label := + } + { "a" chr.to.int$ 'last.extra.num := + "" 'extra.label := + label 'last.label := + } + if$ + number.label #1 + 'number.label := +} +FUNCTION {reverse.pass} +{ next.extra "b" = + { "a" 'extra.label := } + 'skip$ + if$ + extra.label 'next.extra := + extra.label + duplicate$ empty$ + 'skip$ + { year field.or.null #-1 #1 substring$ chr.to.int$ #65 < + { "{\natexlab{" swap$ * "}}" * } + { "{(\natexlab{" swap$ * "})}" * } + if$ } + if$ + 'extra.label := + label extra.label * 'label := +} +EXECUTE {initialize.extra.label.stuff} +ITERATE {forward.pass} +REVERSE {reverse.pass} +FUNCTION {bib.sort.order} +{ sort.label + " " + * + year field.or.null sortify + * + " " + * + title field.or.null + sort.format.title + * + #1 entry.max$ substring$ + 'sort.key$ := +} +ITERATE {bib.sort.order} +SORT +FUNCTION {begin.bib} +{ preamble$ empty$ + 'skip$ + { preamble$ write$ newline$ } + if$ + "\begin{thebibliography}{" number.label int.to.str$ * "}" * + write$ newline$ + "\expandafter\ifx\csname natexlab\endcsname\relax\def\natexlab#1{#1}\fi" + write$ newline$ +} +EXECUTE {begin.bib} +EXECUTE {init.urlbst.variables} % urlbst +EXECUTE {init.state.consts} +ITERATE {call.type$} +FUNCTION {end.bib} +{ newline$ + "\end{thebibliography}" write$ newline$ +} +EXECUTE {end.bib} +%% End of customized bst file +%% +%% End of file `compling.bst'. diff --git a/references/2022.arxiv.nguyen/source/images/VSMEC_Analysis.png b/references/2022.arxiv.nguyen/source/images/VSMEC_Analysis.png new file mode 100644 index 0000000000000000000000000000000000000000..46d0f42bbd1cba96179276a80de42d018996c925 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+++ b/references/2022.arxiv.nguyen/source/sections/1_Introduction.tex @@ -0,0 +1,22 @@ +\section{Introduction} + +With the rise of social media, researching user comments may allow us to understand their behavior and enhance the quality of cyberspace. Social text classification is one of the current popular NLP tasks that aim to solve that problem with an extensive study on comments users left on social media platforms. There are several different current tasks in social media text classification (SMTC) tasks in several domains, and however, it is designed to be independent of other inconsistent data domains. + +Motivated by the popularity of the GLUE \cite{wang-etal-2018-glue}, we present a novel Social Media Text Classification Evaluation (SMTCE) benchmark with four social media text classification tasks in Vietnamese, including constructive speech detection \cite{nguyen2021constructive}, complaint comment dectection \cite{nguyen2021vietnamese}, emotion recognition \cite{uit-vsmec}, and hate speech detection \cite{luu2021largescale} tasks. + +Natural Language Processing (NLP), a subset of artificial intelligence techniques, is rapidly evolving, with numerous notable accomplishments. The primary goal of assisting computers in understanding human language is the bridge for reducing the distance between humans and computers. The current trend in NLP is releasing language models that have been pre-trained on a considerable amount of data to perform various tasks. Hence, we do not need to code from scratch for every task and annotate millions of texts each time. This is the premise for transfer learning and applying the architectures transformers to various downstream tasks in NLP. + +Bidirectional Encoder Representations from Transformers, known as BERT, have appeared and played an essential role in the current outstanding development of computational linguistics. BERT has become a typical baseline in NLP experiments. Several numerous kinds of research have been conducted to analyze the performance of BERTology models \cite{rogers-etal-2020-primer}. Following its release, multilingual BERT-based models emerged, achieving breakthrough performances in serving several languages. Moreover, monolingual language models are also concentrated, primarily focused on a single language, particularly a low-resource language like Vietnamese. In this study, we conduct experiments with multilingual and monolingual BERT-based language models on the SMTCE benchmark to see how they perform. + +Our main contributions to this research are: +\begin{itemize} + + \item We propose \textbf{SMTCE}, the {\textbf S}ocial \textbf{M}edia {\textbf T}ext {\textbf C}lassification {\textbf E}valuation benchmark for evaluating social media text classification or social media mining in Vietnamese. + + \item We implement various BERT-based models on the SMTCE benchmark. We then compare and contrast the characteristics and strengths of monolingual and multilingual BERT-based language models on computing Vietnamese text classification tasks. + + \item After achieving results of monolingual language models, which outperform multilingual models, we start to discuss the remaining problems that researchers on social media text mining have to face. + +\end{itemize} + +The remainder of this work is structured as follows: Section 2 includes related works to which we refer. Section 3 describes the tasks in the SMTCE benchmark. Section 4 overviews BERT-based language models available for Vietnamese NLP tasks. Section 5 shows the processes for implementing models and their results for each task in the SMTCE benchmark. Section 6 is the discussion of the remaining problems in this study. Section 7, the last section, is our conclusion for this research and future work. diff --git a/references/2022.arxiv.nguyen/source/sections/2_RelatedWork.tex b/references/2022.arxiv.nguyen/source/sections/2_RelatedWork.tex new file mode 100644 index 0000000000000000000000000000000000000000..f6c952165eef0c0476d90e54243d34f58c745554 --- /dev/null +++ b/references/2022.arxiv.nguyen/source/sections/2_RelatedWork.tex @@ -0,0 +1,38 @@ +\section{Related Work} + +The introduction of BERT, \citet{devlin-etal-2019-bert} led to the explosion of the transformer language models. BERT has obtained state-of-the-art performances on a variety of NLP tasks upon launch, including nine GLUE tasks, SQuAD v1.0 and 2.0, and SWAG. + +% After that, researchers developed and released BERT-based versions of other field texts in addition to the original, which is only pre-trained on English Books and Wikipedia Corpora. SciBERT \cite{beltagy-etal-2019-scibert}, for example, is pre-trained on science publications; BioBERT \cite{lee2020biobert}, on biomedical corpus; and VideoBERT \cite{sun2019videobert}, on YouTube videos. + +Shortly after releasing BERT, \citet{devlin-etal-2019-bert} then published multilingual BERT, which was capable of over 100 languages. Then, a slew of BERTology models started to emerge. \citet{NEURIPS2019_c04c19c2} released XLM, a cross-lingual language model achieving promising results on various NLP tasks. Following the introduction RoBERTa, they introduced a new pre-trained model, XLM-R \cite{conneau-etal-2020-unsupervised}, which reached breakthrough results. + +Aside from multilingual versions or BERT-based models, developing NLP in countries with different languages promotes researchers to build and improve monolingual models based on available BERT architectures for their languages. We have CamemBERT \cite{martin-etal-2020-camembert} for France, Chinese-BERT \cite{cui-etal-2020-revisiting} for Chinese, or BERT-Japanese \cite{bertjapanese} for Japanese. One of the low-resource languages, Vietnamese, has monolingual BERT-based models that have been pre-trained on Vietnamese datasets, such as: PhoBERT \cite{nguyen-tuan-nguyen-2020-phobert}, viBERT \cite{bui-etal-2020-improving}, vELECTRA \cite{bui-etal-2020-improving}, or viBERT4news\footnote{https://github.com/bino282/bert4news}. + +In Vietnamese, \citet{to2021monolingual} did research about investigating monolingual and multilingual BERT-based models for the Vietnamese summarization task. It is the first attempt to use datasets from other languages based on multilingual models to execute various existing pre-trained language models on the summarization task. + +% About social media text classification datasets, there are existing high-quality and large-scale ones in different Vietnamese NLP tasks such as hate speech detection task: HSD-VLSP \cite{vu2020hsd}, ViHSD \cite{luu2021largescale}; students' feedback sentiment analysis task: VSFC \cite{uit-vscf}; constructive and toxic speech detection task: ViCTSD \cite{nguyen2021constructive}; emotion reconigtion task: VSMEC \cite{uit-vsmec}; gender prediction based on Vietnamese name task: ViNames \cite{To_2020}; or complaint comment detection task: ViOCD \cite{nguyen2021vietnamese}. + +In this paper, we implement several monolingual and multilingual BERT-based pre-trained language models on the proposed SMTCE benchmark tasks. Ensuingly, we conduct an overview of these two types of models in Vietnamese SMTC tasks. + +\begin{table*} +\centering +\caption{Statistics and descriptions of tasks in the SMTCE benchmark. All datasets used the macro-average F1-score to measure the performance of machine learning models.} +\label{tab:sta_des_STCE} +\resizebox{\linewidth}{!}{% +\begin{tabular}{lrrrlrrl} +\hline +\multicolumn{1}{c}{\textbf{Dataset}} & \multicolumn{1}{c}{\textbf{Train}} & \multicolumn{1}{c}{\textbf{Dev}} & \multicolumn{1}{c}{\textbf{Test}} & \multicolumn{1}{c}{\textbf{Task}} & \multicolumn{1}{c}{\textbf{IAA}} & \multicolumn{1}{c}{\begin{tabular}[c]{@{}c@{}}\textbf{Baseline Result}\\\textbf{ (F1-macro \%)}\end{tabular}} & \multicolumn{1}{c}{\textbf{Data Source}} \\ +\hline +\multicolumn{8}{c}{\textit{Binary text classification}} \\ +\hline +ViCTSD & 7,000 & 2,000 & 1,000 & Constructive speech detection & 0.59 & 78.59 & News comments \\ +ViOCD & 4,387 & 548 & 549 & Complaint comment detection & 0.87 & 92.16 & E-commerce feedback \\ +\hline +\multicolumn{8}{c}{\textit{Multi-class text classification}} \\ +\hline +VSMEC & 5,548 & 686 & 693 & Emotion recognition & 0.80 & 59.74 & Social network comments \\ +ViHSD & 24,048 & 2,672 & 6,680 & Hate speech detection & 0.52 & 62.69 & Social network comments \\ +\hline +\end{tabular} +} +\end{table*} \ No newline at end of file diff --git a/references/2022.arxiv.nguyen/source/sections/3_VietnameseSocialMediaText.tex b/references/2022.arxiv.nguyen/source/sections/3_VietnameseSocialMediaText.tex new file mode 100644 index 0000000000000000000000000000000000000000..948092534bfe9b073019ff0cc21a0981198f6ac6 --- /dev/null +++ b/references/2022.arxiv.nguyen/source/sections/3_VietnameseSocialMediaText.tex @@ -0,0 +1,84 @@ +\section{Social Media Text Classification Tasks} +Technology is continuously changing, and social networks allow our users to interact and exchange information more easily. Because of this ease, many harmful and malicious comments of anonymous users aimed to attack individuals psychologically. Studies in this field are gaining attraction to help automatically classify comments as helpful, constructive, or harmful to block and hide them promptly. By giving suitable solutions depending on various situations, we hope to create positive and friendly cyberspace. + +In this study, we propose SMTCE, a new benchmark concentrating on four social media text classification tasks, which covers various domains, data sizes, and challenges in social media tasks. Overview of the tasks in the SMTCE is shown in Table \ref{tab:sta_des_STCE} with the statistics and descriptions of tasks in the SMTCE benchmark, including dataset name, the number of texts of each set, task target, inter-annotator agreement (IAA), baseline result, and data source. + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +\subsection{Emotion Recognition Task} +\citet{uit-vsmec} released a standard Vietnamese Social Media Emotion Corpus known as UIT-VSMEC (VSMEC) for solving the task of recognizing the emotion of Vietnamese comments on social media. It is made up of 6,927 sentences that had been manually annotated with seven emotion labels, including anger, disgust, enjoyment, fear, sadness, surprise, and other. Figure \ref{fig:VSMEC_Analysis} illustrates the number of sentences of each label in the dataset. As we can see, the highest number of sentences belongs to the label enjoyment with 1,965 sentences, and the lowest is the surprise label with 309 sentences. + +\begin{figure}[H] + \centering + \includegraphics[width=1.\linewidth]{images/VSMEC_Analysis.png} + \caption{The number of sentences of each label in the VSMEC dataset.} + \label{fig:VSMEC_Analysis} +\end{figure} + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +\subsection{Constructive Speech Detection Task} +The dataset UIT-ViCTSD (ViCTSD: Vietnamese Constructive and Toxic Speech Detection) \citet{nguyen2021constructive} was built for dealing with the task of detecting constructive and toxic speech. This task aims to solve two issues: detecting constructive and toxic speech in Vietnamese social media comments. Each comment is labeled into two different tasks: identifying constructive and toxic comments. To define constructiveness, there are two labels: constructive and non-constructive. Furthermore, there are two labels for classifying toxic comments: toxic and non-toxic. Figure \ref{fig:ViCTSD_Analysis} below is the statistic of the number of each label in the dataset. + +\begin{figure}[H] + \centering + \includegraphics[width=1.\linewidth]{images/ViCTSD_Analysis.png} + \caption{The statistic of the number of sentences of each label in the ViCTSD dataset.} + \label{fig:ViCTSD_Analysis} +\end{figure} + +The dataset contains 10,000 comments by ten domains of users crawled from the online discussion as VnExpress.net. The dataset serves to identify the constructiveness and toxicity of Vietnamese social media comments. The authors also evaluated the first version of this dataset with a proposed system with 78.59\% and 59.40\% F1-score for constructiveness and toxicity detection. + +As depicted in Figure \ref{fig:ViCTSD_Analysis}, we see that the dataset is very imbalanced in toxic speech detection tasks, so we only focus on the task of constructiveness detection in this study. + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +\subsection{Hate Speech Detection Task} + +\citet{luu2021largescale} provided a dataset for the task of hate speech detection on Vietnamese social media comments named UIT-ViHSD (ViHSD). The dataset includes 30,000 comments labeled by annotators with three labels CLEAN, OFFENSIVE, and HATE. We describe the proportion of each label in the dataset in Figure \ref{fig:ViHSD_Analysis}. As shown in this illustration, this dataset is severely unbalanced (82.71\% of CLEAN comments) with a low inter-annotator agreement of only 0.52, and it required several techniques in pre-processing data to deal with this imbalance. + +\begin{table*} +\centering +\caption{An overview of available BERTology languages model in Vietnamese.} +\label{tab:BERTology} +\begin{tabular}{clllll} +\hline +\multicolumn{1}{l}{} & & \multicolumn{1}{c}{\textbf{Data size}} & \multicolumn{1}{c}{\textbf{Vocab. size}} & \multicolumn{1}{c}{\textbf{Tokenization}} & \multicolumn{1}{c}{\textbf{Domain}} \\ +\hline +\multirow{3}{*}{\begin{tabular}[c]{@{}c@{}}\textbf{Multilingual}\end{tabular}} & mBERT (case uncased) & 16GB & 3.3B & Subword & Book+Wiki \\ + & XLM-R (base) & 2.5TB & 250K & Subword & Common Crawl \\ + & DistilmBERT & 16GB & 31K & Subword & Book+Wiki \\ +\hline +\multirow{4}{*}{\begin{tabular}[c]{@{}c@{}}\textbf{Monolingual}\end{tabular}} & PhoBERT & 20GB & 64K & Subword & News+Wiki \\ + & viBERT & 10GB & 32K & Subword & News \\ + & vELECTRA & 60GB & 32K & Subword & News \\ + & viBERT4news & 20GB & 62K & Syllable & News \\ +\hline +\end{tabular} +\end{table*} + +\begin{figure}[H] + \centering + \includegraphics[width=1.\linewidth]{images/ViHSD_Analysis.png} + \caption{The statistic of the number of sentences of each label in the ViHSD dataset.} + \label{fig:ViHSD_Analysis} +\end{figure} + +Hate speech has been a source of concern for social media users. The dataset aims to create a tool for identifying it in online communication interactions, censoring it to protect users from offensive content, and improving the environment of online forums. + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +\subsection{Complaint Comment Detection Task} +\citet{nguyen2021vietnamese} researched customer complaints on e-commerce sites and released a novel open-domain dataset named UIT-ViOCD (ViOCD), a collection of 5,485 human-annotated comments on four domains. It was then evaluated using multiple approaches and achieving the best performance with an F1-score of 92.16\% by the fine-tuned PhoBERT model. We can utilize this information to classify complaint comments from users on open-domain social media automatically. Table \ref{tab:ViOCD_Analysis} below depicts the distribution of each label in sets. + +\begin{table}[H] +\centering +\caption{The distribution of each label in the train, valid, and test sets of the ViOCD dataset.} +\label{tab:ViOCD_Analysis} +\begin{tabular}{lrr} +\hline + & \multicolumn{1}{c}{\textbf{Complaint}} & \multicolumn{1}{c}{\textbf{Non-complaint}} \\ \hline +\textbf{Train set} & 2,292 & 2,095 \\ +\textbf{Dev set} & 283 & 265 \\ +\textbf{Test set} & 279 & 270 \\ \hline +\textbf{Total} & \textbf{2,854} & \textbf{2,630} \\ \hline +\end{tabular} +\end{table} + +Even though this dataset contains a small number of data points, the ratio between labels 0 and 1 is quite balanced. Hence, in this task, we do not need to use significant techniques to deal with data imbalance before starting the training session. \ No newline at end of file diff --git a/references/2022.arxiv.nguyen/source/sections/4_BERTologyModel.tex b/references/2022.arxiv.nguyen/source/sections/4_BERTologyModel.tex new file mode 100644 index 0000000000000000000000000000000000000000..01d01337b23673bee30dc087ac22653240fd8838 --- /dev/null +++ b/references/2022.arxiv.nguyen/source/sections/4_BERTologyModel.tex @@ -0,0 +1,57 @@ +\section{BERTology Language Models} +Transformer-based models are currently the best-performance models in natural language processing or computational linguistics. The architecture of the transformer model consists of two parts: encoder and decoder. It differs from previous deep learning architectures that it pushes in all of the input data at once rather than progressively, thanks to the self-attention \cite{NIPS2017_3f5ee243} mechanism. Following the premiere of \citet{devlin-etal-2019-bert}, models based on BERT architectures are becoming increasingly popular. Challenging NLP tasks have been effectively solved using these models. + +BERT (Bidirectional Encoder Representations from Transformers), the same architecture as the bi-directional recurrent neural network, uses bi-directional encoder representations instead of traditional techniques that only learn from left to right or right to left. A bi-directional architecture includes two networks, one in which the input is handled from start to end and another one from end to start. The outputs of two networks are then integrated to provide a single representation. As a result, BERT is better able to understand the relationship between words and provides better performances. + +%The Figure \ref{fig:bi-directional_architecture} is the demonstration of the bi-directional architecture. + +Besides, unlike other context-free word embedding models like word2vec or GloVe, which create a single word embedding representation for each word in its vocabulary, BERT needs to consider each context for a given the word in a phrase. As a consequence, homonyms in other sentences become different in each context. + +In this study, we try to apply various BERT-based models to Vietnamese datasets for solving social media text classification tasks. With multilingual models, we implement multilingual BERT cased (mBERT cased), multilingual BERT uncased (mBERT uncased), XLM-RoBERTa (XLM-R), and Distil multilingual BERT (DistilmBERT). We also try the monolingual models, which are pre-trained in Vietnamese data, such as PhoBERT, viBERT, vELECTRA, and viBERT4news. Table \ref{tab:BERTology} is an overview of multilingual and monolingual BERT-based language models available in Vietnamese. All pre-trained models we used in this research are downloaded from Hugging Face\footnote{https://huggingface.co/}. + +\subsection{Multilingual Language Models} +Language models are usually specifically designed and trained in English, most globally used language. Moreover, these models were then deeper trained and became multilingual to expand and serve NLP problems to support more languages globally. In this study, we deploy popular multilingual models as follows. + +\subsubsection{Multilingual BERT} +\citet{devlin-etal-2019-bert} continues to develop and expand supported languages with multilingual BERT, including uncased and cased versions, after the launch of BERT. Multilingual BERT models, as opposed to their predecessors, are trained in various languages, including Vietnamese, and use masked language modeling (MLM). Each model includes a 12-layer, 768-hidden, 12-heads, and 110M parameters and supports 104 different languages. There are two multilingual BERT models: uncased\footnote{https://huggingface.co/bert-base-multilingual-uncased} and cased\footnote{https://huggingface.co/bert-base-multilingual-cased} versions, which are both used for implementation in this study. + +\subsubsection{XLM-RoBERTa} +XLM-RoBERTa (XLM-R) is a cross-lingual language model provided by \citet{conneau-etal-2020-unsupervised} and trained in 100 different languages, including Vietnamese, on 2.5TB Clean CommonCrawl data\footnote{https://github.com/facebookresearch/cc\_net}. It offers significant gains in downstream tasks such as classification, sequence labeling, and question answering over previously released multilingual models like mBERT or XLM. Under XLM, the language of the input ids cannot be correctly determined by the XLM-R language for the language of the input id to be understood by language tensors. We use the XLM-R base\footnote{https://huggingface.co/xlm-roberta-base} in this research. + +\subsubsection{DistilmBERT} + +\begin{table*}[!htbp] +\centering +\caption{The techniques for pre-processing data we employed in experiments.} +\label{tab:preprocessing_techniques} +\begin{tabular}{clcccc} +\hline +\multicolumn{1}{l}{\multirow{2}{*}{\textbf{No}.}} & \multicolumn{1}{c}{\multirow{2}{*}{\textbf{Pre-procesing technique}}} & \multicolumn{4}{c}{\textbf{Dataset}} \\ +\cline{3-6} +\multicolumn{1}{l}{} & \multicolumn{1}{c}{} & \textbf{VSMEC} & \textbf{ViCTSD} & \textbf{ViHSD} & \textbf{ViOCD} \\ +\hline +1 & Removing numbers & \checkmark & & \checkmark & \checkmark \\ +2 & Removing punctations & & \checkmark & \checkmark & \checkmark \\ +3 & Removing emojis, emoticons & & \checkmark & \checkmark & \checkmark \\ +4 & Converting emojis, emoticons into texts & \checkmark & & & \\ +5 & Tokenizing words & \checkmark & \checkmark & \checkmark & \checkmark \\ +\hline +\end{tabular} +\end{table*} + +DistilmBERT\footnote{https://huggingface.co/distilbert-base-multilingual-cased}, which is published by \citet{sanh2020distilbert}, is a different BERT version, in which its features improve the original version (faster, cheaper and lighter). This architecture requires less than BERT pre-training. In addition, it is more efficient compared to BERT, as the BERT model could be reduced by 40 while maintaining 97 of its language understanding capabilities and 60 more quickly. Hence, DistilmBERT has been known as a BERT version of reducing the number of parameters. + +\subsection{Monolingual Language Models} +In addition to developing multilingual language models, researchers in specific languages are interested in monolingual models. Monolingual models are frequently built using the BERT architecture and pre-trained on datasets in a single language. Furthermore, because these models are trained on a large amount of data in a language, they frequently achieve great performances on NLP tasks for the languages themselves. In this study, we use several existing monolingual models that have been introduced for solving Vietnamese tasks. + +\subsubsection{PhoBERT} \citet{nguyen-tuan-nguyen-2020-phobert} first presented PhoBERT models, which are pre-trained models for Vietnamese NLP. They are state-of-the-art Vietnamese language models. To handle tasks in Vietnamese, they have trained the first large-scale monolingual BERT-based with two versions as \textit{base} and \textit{large} with a 20GB word-level Vietnamese dataset combining two datasets: Vietnamese Wikipedia\footnote{https://vi.wikipedia.org/wiki/} (1GB) and modified dataset from a Vietnamese news dataset\footnote{https://github.com/binhvq/news-corpus} (19GB). The chosen version we use to implement in this study is PhoBERT base\footnote{https://huggingface.co/vinai/phobert-base}. + +\subsubsection{viBERT} ViBERT\footnote{https://huggingface.co/FPTAI/vibert-base-cased}, a pre-trained language model for Vietnamese, which is based on BERT architecture and introduced by \citet{bui-etal-2020-improving}. The architecture of viBERT is similar to that of mBERT, and it has been pre-trained on large corpora of 10GB of uncompressed Vietnamese text. Nonetheless, there is a distinction between this model and mBERT. They chose to exclude insufficient vocab because the mBERT vocab still contains languages apart from Vietnamese. + +\subsubsection{vELECTRA} +ELECTRA, first introduced by \citet{clark2020electric}, is a novel pre-training architecture that uses replaced token detection (RTD) rather than language modeling (LM) or masked language modeling (MLM), as is popular in existing language models. + +\citet{bui-etal-2020-improving} also released vELECTRA, another pre-trained model for Vietnamese, along with viBERT. They used a dataset with almost 60GB of words to pre-train vELECTRA\footnote{https://huggingface.co/FPTAI/velectra-base-discriminator-cased}. + +\subsubsection{viBERT4news} +NlpHUST published viBERT4news\footnote{https://github.com/bino282/bert4news}, a Vietnamese version of BERT trained on more than 20 GB of news datasets. ViBERT4news demonstrated its strength on Vietnamese NLP tasks, including sentiment analysis using comments of the AIViVN dataset, after launch and testing, with an F1 score of 0.90268 on public leaderboards (while the winner of the shared-task score is 0.90087). diff --git a/references/2022.arxiv.nguyen/source/sections/5_MultilingualVersusMonolingualLanguageModel.tex b/references/2022.arxiv.nguyen/source/sections/5_MultilingualVersusMonolingualLanguageModel.tex new file mode 100644 index 0000000000000000000000000000000000000000..1c41bfbaa8fd15187dfa2caf47f95cdc9f2d1770 --- /dev/null +++ b/references/2022.arxiv.nguyen/source/sections/5_MultilingualVersusMonolingualLanguageModel.tex @@ -0,0 +1,149 @@ +\section{Experiments and Results} +In this section, we carry out experiments using monolingual and multilingual BERT-based models on Vietnamese benchmark datasets. + +\begin{table*} +\centering +\caption{Experimental Results of multilingual versus monolingual models on Vietnamese social media datasets (macro-averaged F1-score (\%)).} +\label{tab:results} +\resizebox{\textwidth}{!}{% +\begin{tabular}{llcccc} +\hline + +\multicolumn{2}{c}{\textbf{Model}} & \multicolumn{1}{c}{\textbf{VSMEC}} & \multicolumn{1}{c}{\textbf{ViCTSD}} & \multicolumn{1}{c}{\textbf{ViOCD}} & \multicolumn{1}{c}{\textbf{ViHSD}} \\ +\hline +\multicolumn{2}{l}{\textbf{Baseline}} & \makecell{59.74 \\ \cite{uit-vsmec}} & \makecell{78.59 \\ \cite{nguyen2021constructive}} & \makecell{92.164 \\ \cite{nguyen2021vietnamese}} & \makecell{62.69 \\ \cite{luu2021largescale}} \\ +\hline +\multirow{4}{*}{\textbf{Multilingual}} & \textit{mBERT (cased)} & 54.59 & 80.42 & 91.61 & 64.20 \\ +\cline{2-6} + & \textit{mBERT (uncased)} & 53.14 & 78.97 & 92.52 & 62.76 \\ +\cline{2-6} + & \textit{XLM-R} & 62.24 & 80.51 & 94.35 & 63.68 \\ +\cline{2-6} + & \textit{DistilmBERT} & 53.83 & 81.69 & 90.50 & 62.50 \\ +\hline +\multicolumn{1}{c}{\multirow{4}{*}{\textbf{Monolingual}}} & \textit{PhoBERT} & \textbf{65.44} & 83.55 & 94.71 & 66.07 \\ +\cline{2-6} +\multicolumn{1}{c}{} & \textit{viBERT} & 60.68 & 81.27 & 94.53 & 65.06 \\ +\cline{2-6} +\multicolumn{1}{c}{} & \textit{vELECTRA} & 61.29 & 80.24 & \textbf{95.26} & 65.97 \\ +\cline{2-6} +\multicolumn{1}{c}{} & \textit{viBERT4news} & 64.65 & \textbf{84.15} & 94.72 & \textbf{66.43} \\ +\hline +\end{tabular} +} +\end{table*} + +\subsection{Pre-processing Techniques} +We implement our data preprocessing strategies based on the task as well as the characteristics of the dataset. Because each dataset is distinct in terms of vocabulary, origin, and content, we use different preprocessing approaches appropriate for each dataset before feeding data into the models. Table \ref{tab:preprocessing_techniques} contains a list of techniques that we implement for each dataset. + +We almost remove numbers in all tasks except the constructive speech detection task because of \citet{nguyen2021constructive} recommendation. For the VSMEC dataset, we do not remove punctuations, emojis, and emoticons because, in several cases, users often use them in their comments to express emotions, which has a great effect on the performance of the model in predicting emotion labels. Therefore, we keep the punctations, and we then apply the study of \citet{9310495} in data preprocessing, which is transforming emojis and emoticons into Vietnamese text and then obtain a higher performance with the F1-score of 64.40\% (higher than the baseline of the author of the dataset 4.66\%). Because emojis and emoticons are also essential elements influencing the emotions expressed in comments, deleting them causes a harmful effect on the emotion categorization task. + +There are several preprocessing methods with different transfer learning-based models as a recommendation by its authors. For PhoBERT, we employ VnCoreNLP\footnote{https://github.com/vncorenlp/VnCoreNLP} and FAIRSeq\footnote{https://github.com/pytorch/fairseq} for preprocessing data and tokenizing words before applying them to the dataset. + +\subsection{Experiment Settings} +We record and select the most parameters for each task after multiple experiment and show them in Table \ref{tab:parameters}. Additionally, we keep the max sequence length as used in the baseline of each task because they are optimized settings for it. Other parameters are remained and not customized. + +% \usepackage{graphicx} + + +\begin{table}[H] +\centering +\caption{The parameters selected for each task in the SMTCE benchmark. [1] and [2] represent two parameters batch\_size and epochs, respectively.} +\label{tab:parameters} +\resizebox{\linewidth}{!}{% +\begin{tabular}{lcccccccc} +\hline +\multicolumn{1}{c}{} & \multicolumn{2}{c}{\textbf{VSMEC}} & \multicolumn{2}{c}{\textbf{ViOCD}} & \multicolumn{2}{c}{\textbf{ViHSD}} & \multicolumn{2}{c}{\textbf{ViCTSD}} \\ +\hline +\multicolumn{1}{c}{} & \textit{[1]} & \textit{[2]} & \textit{[1]} & \textit{[2]} & \textit{[1]} & \textit{[2]} & \textit{[1]} & \textit{[2]} \\ +\hline +\textit{mBERT (cased)} & 16 & 4 & 16 & 4 & 16 & 4 & 16 & 4 \\ +\textit{mBERT (uncased)} & 16 & 4 & 16 & 4 & 16 & 4 & 16 & 4 \\ +\textit{XLM-R} & 8 & 4 & 16 & 4 & 16 & 4 & 16 & 2 \\ +\textit{DistilmBERT} & 16 & 4 & 16 & 4 & 16 & 4 & 16 & 4 \\ +\hline +PhoBERT & 8 & 2 & 8 & 2 & 16 & 2 & 16 & 2 \\ +\textit{viBERT} & 16 & 4 & 16 & 4 & 16 & 4 & 16 & 4 \\ +\textit{vELECTRA} & 16 & 4 & 16 & 4 & 16 & 4 & 16 & 4 \\ +\textit{viBERT4news} & 8 & 2 & 16 & 2 & 16 & 2 & 16 & 2 \\ +\hline +\end{tabular} +} +\end{table} + +% To achieve the best suitable hyperparameters for the models, we use Population Based Training (PBT) \cite{jaderberg2017population} method. PBT is a training method for neural network-based models. It enables an experimenter to quickly select the optimum hyperparameters and model for the task. PBT trains and optimizes several networks simultaneously, allowing the best configuration to be identified promptly. This can be completed as rapidly as traditional approaches while incurring no costs. + +% %Phan tich phuong phap nay voi cac cong thuc toan hoc - Doc paper + +% One of PBT characteristics is its ease of integration into existing machine learning pipelines. In this research, we use PBT with the Ray Tune\footnote{https://www.ray.io/ray-tune}, which is a Python library for fast hyperparameter tuning at scale. After implementing the PBT method, we find the most optimized sets of parameters of each dataset, listed in detail in Table ... in Appendix. + +\subsection{Evaluation Metric} +In the text classification task, we have different metrics suitable for specific datasets and problems. Because most datasets in this study are imbalanced and according to the choice of dataset authors, we choose the macro-average F1 score to evaluate the performances of models on the datasets. + +To compute the macro-average F1 score, we first calculate the F1 score per class in the dataset by the formula (1). + +\[ +\text{F1 score = }\text{2}*\frac{\text{Precision * Recall}}{\text{Precision + Recall}}\quad(1) +\] + +After achieving the F1 scores of all classes, we compute the macro-average F1 score by calculating the average F1 score as shown in formula (2). + +\[ +\text{Macro F1 score = } \frac{sum(\text{F1 scores})}{\text{Number of classes}}\quad(2) +\] + +Because the macro f1 score gives importance equally to each class, it means the macro f1 algorithm may still produce objective findings on unbalanced datasets since a majority class will participate equally alongside the minority. That is the reason why almost imbalanced dataset authors choose the macro-average f1 score as the primary metric to represent actual model performance despite skewed class sizes. + +\subsection{Experimental Results} + +We begin implementation after selecting appropriate parameters for each model corresponding to each task, and the results obtained by models are presented in Table \ref{tab:results}. + +% \begin{table*} +% \centering +% \caption{Results of multilingual versus monolingual models on Vietnamese social media comments dataset for classification by macro-averaged F1-score (\%).} +% \label{tab:results} +% \resizebox{\textwidth}{!}{% +% \begin{tabular}{llrrrr} +% \hline + +% \multicolumn{2}{c}{\textbf{Model}} & \multicolumn{1}{c}{\textbf{VSMEC}} & \multicolumn{1}{c}{\textbf{ViCTSD}} & \multicolumn{1}{l}{\textbf{ViOCD}} & \multicolumn{1}{l}{\textbf{ViHSD}} \\ +% \hline +% \multicolumn{2}{l}{\textbf{Baseline}} & 59.74 \cite{uit-vsmec} & 78.59 \cite{nguyen2021constructive} & 92.16 \cite{nguyen2021vietnamese} & 62.69 \cite{luu2021largescale} \\ +% \hline +% \multirow{4}{*}{\textbf{Multilingual}} & \textit{mBERT (cased)} & 54.59 & 80.42 & 91.61 & 64.20 \\ +% \cline{2-6} +% & \textit{mBERT (uncased)} & 53.14 & 78.97 & 92.52 & 62.76 \\ +% \cline{2-6} +% & \textit{XLM-R} & 62.24 & 80.51 & 94.35 & 63.68 \\ +% \cline{2-6} +% & \textit{DistilmBERT} & 53.83 & 81.69 & 90.5 & 62.50 \\ +% \hline +% \multicolumn{1}{c}{\multirow{4}{*}{\textbf{Monolingual}}} & \textit{PhoBERT} & \textbf{65.44} & 83.55 & 94.71 & 66.07 \\ +% \cline{2-6} +% \multicolumn{1}{c}{} & \textit{viBERT} & 60.68 & 81.27 & 94.53 & 65.06 \\ +% \cline{2-6} +% \multicolumn{1}{c}{} & \textit{vELECTRA} & 61.29 & 80.24 & \textbf{95.26} & 65.97 \\ +% \cline{2-6} +% \multicolumn{1}{c}{} & \textit{viBERT4news} & 64.65 & \textbf{84.15} & 94.72 & \textbf{66.43} \\ +% \hline +% \end{tabular} +% } +% \end{table*} + +Our experiments outperform the original results of authors on each task in the majority of cases. As a result, we obtain the following outcomes: 65.44\% for VSMEC sentiment classification task (higher than 5.7\%) with PhoBERT model; 84.15\% for ViCTSD identifying constructiveness task (higher than 5.56\%) by viBERT4news model; 95.26\% for ViOCD classifying complaint comments task (higher than 3.1\%) by vELECTRA model; 66.43\% for ViHSD hate speech detection task (higher than 3.74\%) by viBERT4news model. The results on the ViOCD set are significantly higher than on other datasets because its data samples are processed cleanly, and the higher inter-annotator agreement during data labeling is noticeable when building the dataset. + +\subsection{Result Analysis} +After achieving the results, we see that the outcomes of monolingual models are all greater than the baseline result of the author for each dataset. In terms of performance, specific techniques, like XLM-R with VSMEC; all methods with ViCTSD; mBERT cased and uncased, and XLM-R with ViHSD; and mBERT uncased and XLM-R with ViOCD, perform just above the baseline. + +We also discover that the models that produce the most remarkable outcomes in this study for all tasks are monolingual. It demonstrates that monolingual language models outperform multilingual models when dealing with non-English and low-resource languages such as Vietnamese. + +When we implement techniques on the VSMEC dataset, the best model is PhoBERT, which has an F1-score of 65.44\%, more significant than the result of the author of 4.66\%. \citet{huynh-etal-2020-simple} also published a method that gains an F1-score of 65.79\% (higher than ours 0.35\%). However, the approach used in that study is an ensemble classifier that combines multiple Neural Network Models such as CNN, LSTM, and mBERT. This ensemble approach must operate with a majority voting ensemble before providing the final output, which is why it takes longer to execute than a single model. + +% Figure \ref{fig:confusion_matrix} below is the summary of the confusion matrix of the models which achieved the highest F1 score on each task. We see that ... + +% \begin{figure}[H] +% \centering +% \includegraphics[width=1\linewidth]{images/confusion_matrix.png} +% \caption{Confusion matrix of the highest models on each task.} +% \label{fig:confusion_matrix} +% \end{figure} diff --git a/references/2022.arxiv.nguyen/source/sections/6_WhichOneIsTheBetter.tex b/references/2022.arxiv.nguyen/source/sections/6_WhichOneIsTheBetter.tex new file mode 100644 index 0000000000000000000000000000000000000000..a321a17b498cbe5d4ea3138225aaf14535f75c33 --- /dev/null +++ b/references/2022.arxiv.nguyen/source/sections/6_WhichOneIsTheBetter.tex @@ -0,0 +1,21 @@ +\section{Discussions on Vietnamese Social Media Text Mining} + +\subsection{Monolingual versus Multilingual: Which is the better in Vietnamese social media text classification on BERTology?} + +After implementing multiple multilingual and monolingual BERT-based language models for each task in the SCTE benchmark, we discovered that monolingual gets better results in most cases. Furthermore, the results of all current monolingual models outperform the baseline of the author of the dataset. Meanwhile, multilingual models only outperform baseline in a few tasks and mainly stand out with XLM-R. + +Multilingual models do not now provide superior results because these models, except XLM-R, are primarily pre-trained on the Vietnamese Wikipedia corpus. This corpus is limited, with only 1GB of uncompressed data, and the material on Wikipedia is not representative of common language use. Moreover, unlike in English or other widely spoken languages, the space in Vietnamese is solely used to separate syllables, not words. Meanwhile, multilingual BERT-based models are now unaware of this. Monolingual models are pre-trained on larger and higher-quality Vietnamese datasets, such as viBERT4news on AIViVN's comments dataset or PhoBERT on a 20GB word-level Vietnamese corpus \cite{nguyen-tuan-nguyen-2020-phobert}. As a result, monolingual language models outperform bilingual language models on Vietnamese NLP tasks, particularly text classification tasks, as demonstrated by the experiments in this study. Several other tasks such as Vietnamese Aspect Category Detection \cite{dangvthin}, or Vietnamese extractive multi-document summarization \cite{to-etal-2021-monolingual} both have outperformance with monolingual BERT-based models. + +Nonetheless, monolingual language models do not outperform the multilingual in all NLP tasks. More complex tasks like machine reading comprehension \cite{van2022vlsp} achieve better performances on multilingual pre-trained language models. + +\subsection{How do pre-processing techniques help improve social media text mining?} + +Pre-proceeding techniques are essential to improve the machine learning models significantly on social media texts, which were proven in previous studies. \citet{9310495} proposed an approach of pre-processing data before feeding data into the model in the training stage, which was also implemented in this study to pre-process the emotion recognition task. In detail, their study is to transform emojis and emoticons, which are elements to determine the whole feeling of the content but seem to be bypassed in most normal pre-processing methods, into texts to add more detail. Their methods are appropriate pre-processing techniques based on Vietnamese social media characteristics. In our experiment, the performance of the PhoBERT model improves 5.28\% from 60.16\% up to 65.44\% by using their approaches. + +Additionally, carefully in the stage of building the dataset is also an effective way to gain optimum performances of models. The dataset ViOCD proves that the author worked well with strictly annotating and pre-processing data in building data phases. As a result, most models obtain excellent performance in identifying complaints on Vietnamese e-commerce websites. + +Researchers have recently tended to apply various special approaches to optimize model performance. One of those methods is lexical normalization \cite{van-der-goot-etal-2021-multilexnorm}. As a characteristic of social media texts, unstructured content is an actual problem many models face. Most content on social media platforms is written in various formats and up to the users. Therefore, normalizing all of them into the standard in the pre-processing phase makes the model deeper understand the words and then achieve better performance in different natural language processing tasks. + +\subsection{How do imbalanced data impact the social media text mining?} + +Most datasets in social media text mining are unbalanced. Processing techniques to improve machine learning models on imbalanced datasets have recently attracted much attention from NLP researchers. As is used in this study, choosing proper metrics to evaluate models is a good method to obtain an objective view of their performances. Furthermore, if the amount of data is big enough, researchers can use the resampling method to solve the imbalance of data. The re-sampling technique reduces or extends the minority or majority class to achieve a balanced dataset. Besides traditional approaches to dealing with imbalanced data, new methods are developed to boost the model performance. ARCID \cite{10.1007/978-3-319-73117-9_40} and EDA \cite{wei2019eda} are novel methods that can address the imbalance problem in most social media datasets. This approach seeks to extract essential knowledge from unbalanced datasets by highlighting information gathered from minor classes without significantly affecting the classifier prediction performance. \ No newline at end of file diff --git a/references/2022.arxiv.nguyen/source/sections/7_Conclusion.tex b/references/2022.arxiv.nguyen/source/sections/7_Conclusion.tex new file mode 100644 index 0000000000000000000000000000000000000000..a9ae8b5c262d06e02c8a8fd2c4f04ce72cd06d98 --- /dev/null +++ b/references/2022.arxiv.nguyen/source/sections/7_Conclusion.tex @@ -0,0 +1,5 @@ +\section{Conclusion and Future Work} + +This paper described a novel evaluation benchmark for social text classification named SMTCE with four tasks emotion recognition, constructive speech detection, hate speech detection, and complaint comment detection. We implemented various approaches with BERT-based multilingual versus monolingual language models on Vietnamese benchmark datasets for each SMTC task in the SMTCE benchmark. We achieved the state-of-the-art performances: 65.44\%, 84.15\%, 66.43\%, and 95.26\% for VSMEC, ViCTSD, ViHSD, and ViOCD datasets respectively. + +In future, we hope that this study will serve as a standard benchmark to develop new models on Vietnamese social media text classification or mining. Furthermore, it will motivate a range of NLP benchmarks for low-resource languages like Vietnamese. diff --git a/references/2022.paclic.ngan/paper.md b/references/2022.paclic.ngan/paper.md new file mode 100644 index 0000000000000000000000000000000000000000..436607187f2594fe5a4f6e099da4bbda653758cd --- /dev/null +++ b/references/2022.paclic.ngan/paper.md @@ -0,0 +1,14 @@ +--- +title: "{SMTCE" +authors: + - "Thanh Nguyen, Luan and + Van Nguyen, Kiet and + Luu-Thuy Nguyen, Ngan" +year: 2022 +venue: "Proceedings of the 36th Pacific Asia Conference on Language, Information and Computation" +url: "https://aclanthology.org/2022.paclic-1.31" +--- + +# {SMTCE + +*Full text available in paper.pdf* diff --git a/references/2022.paclic.ngan/paper.pdf b/references/2022.paclic.ngan/paper.pdf new file mode 100644 index 0000000000000000000000000000000000000000..7064a49586f57459492e70d74f608faeaa1106f3 --- /dev/null +++ b/references/2022.paclic.ngan/paper.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a06b2544427b41c87646cdd1a3767c5420c419d08c22db27a75609ac4708690b +size 271220 diff --git a/references/2023.arxiv.nguyen/paper.md b/references/2023.arxiv.nguyen/paper.md new file mode 100644 index 0000000000000000000000000000000000000000..31d2f84746a99a53c267c7d07543b314349606a3 --- /dev/null +++ b/references/2023.arxiv.nguyen/paper.md @@ -0,0 +1,651 @@ +--- +title: "ViSoBERT: A Pre-Trained Language Model for Vietnamese Social Media Text Processing" +authors: + - "Quoc-Nam Nguyen" + - "Thang Chau Phan" + - "Duc-Vu Nguyen" + - "Kiet Van Nguyen" +year: 2023 +venue: "arXiv" +url: "https://arxiv.org/abs/2310.11166" +arxiv: "2310.11166" +--- + +\maketitle +\def\thefootnote{*}\footnotetext{ +\raisebox{\baselineskip}[0pt][0pt]{\hypertarget{equally}{}}Equal contribution.}\def\thefootnote{\arabic{footnote}} +\setcounter{footnote}{3} + +\begin{abstract} + +English and Chinese, known as resource-rich languages, have witnessed the strong development of transformer-based language models for natural language processing tasks. Although Vietnam has approximately 100M people speaking Vietnamese, several pre-trained models, e.g., PhoBERT, ViBERT, and vELECTRA, performed well on general Vietnamese NLP tasks, including POS tagging and named entity recognition. These pre-trained language models are still limited to Vietnamese social media tasks. In this paper, we present the first monolingual pre-trained language model for Vietnamese social media texts, ViSoBERT, which is pre-trained on a large-scale corpus of high-quality and diverse Vietnamese social media texts using XLM-R architecture. Moreover, we explored our pre-trained model on five important natural language downstream tasks on Vietnamese social media texts: emotion recognition, hate speech detection, sentiment analysis, spam reviews detection, and hate speech spans detection. Our experiments demonstrate that ViSoBERT, with far fewer parameters, surpasses the previous state-of-the-art models on multiple Vietnamese social media tasks. Our ViSoBERT model is available\footnote{https://huggingface.co/uitnlp/visobert} only for research purposes. + +**Disclaimer**: This paper contains actual comments on social networks that might be construed as abusive, offensive, or obscene. + +\end{abstract} + +# Introduction \label{introduction} +Language models based on transformer architecture [attentionisallyouneed] pre-trained on large-scale datasets have brought about a paradigm shift in natural language processing (NLP), reshaping how we analyze, understand, and generate text. In particular, BERT [bert] and its variants [roberta,xlm-r] have achieved state-of-the-art performance on a wide range of downstream NLP tasks, including but not limited to text classification, sentiment analysis, question answering, and machine translation. English is moving for the rapid development of language models across specific domains such as medical [biobert,rasmy2021med], scientific [beltagy2019scibert], legal [legalbert], political conflict and violence [conflibert], and especially social media [bertweet,bernice,robertuito,twhinbert]. + +Vietnamese is the eighth largest language used over the internet, with around 85 million users across the world\footnote{https://www.internetworldstats.com/stats3.htm}. Despite a large amount of Vietnamese data available over the Internet, the advancement of NLP research in Vietnamese is still slow-moving. This can be attributed to several factors, to name a few: the scattered nature of available datasets, limited documentation, and minimal community engagement. Moreover, most existing pre-trained models for Vietnamese were primarily trained on large-scale corpora sourced from general texts [viBERTandvELECTRA,phobert,viDeBerTa]. While these sources provide broad language coverage, they may not fully represent the sociolinguistic phenomena in Vietnamese social media texts. Social media texts often exhibit different linguistic patterns, informal language usage, non-standard vocabulary, lacking diacritics and emoticons that are not prevalent in formal written texts. The limitations of using language models pre-trained on general corpora become apparent when processing Vietnamese social media texts. The models can struggle to accurately understand and interpret the informal language, using emoji, teencode, and diacritics used in social media discussions. This can lead to suboptimal performance in Vietnamese social media tasks, including emotion recognition, hate speech detection, sentiment analysis, spam reviews detection, and hate speech spans detection. + +We present ViSoBERT, a pre-trained language model designed explicitly for Vietnamese social media texts to address these challenges. ViSoBERT is based on the transformer architecture and trained on a large-scale dataset of Vietnamese posts and comments extracted from well-known social media networks, including Facebook, Tiktok, and Youtube. Our model outperforms existing pre-trained models on various downstream tasks, including emotion recognition, hate speech detection, sentiment analysis, spam reviews detection, and hate speech spans detection, demonstrating its effectiveness in capturing the unique characteristics of Vietnamese social media texts. Our contributions are summarized as follows. + + - We presented ViSoBERT, the first PLM based on the XLM-R architecture and pre-training procedure for Vietnamese social media text processing. ViSoBERT is available publicly for research purposes in Vietnamese social media mining. ViSoBERT can be a strong baseline for Vietnamese social media text processing tasks and their applications. + - ViSoBERT produces SOTA performances on multiple Vietnamese downstream social media tasks, thus illustrating the effectiveness of our PLM on Vietnamese social media texts. + + - To understand our pre-trained language model deeply, we analyze experimental results on the masking rate, examining social media characteristics, including emojis, teencode, and diacritics, and implementing feature-based extraction for task-specific models. + +# Fundamental of Pre-trained Language Models for Social Media Texts \label{fundamental} +Pre-trained Language Models (PLMs) based on transformers [attentionisallyouneed] have become a crucial element in cutting-edge NLP tasks, including text classification and natural language generation. Since then, language models based on transformers related to our study have been reviewed, including PLMs for Vietnamese social media texts. +## Pre-trained Language Models for Vietnamese +Several PLMs have recently been developed for processing Vietnamese texts. These models have varied in their architectures, training data, and evaluation metrics. PhoBERT, developed by [phobert], is the first general pre-trained language model (PLM) created for the Vietnamese language. The model employs the same architecture as BERT [bert] and the same pre-training technique as RoBERTa [roberta] to ensure robust and reliable performance. PhoBERT was trained on a 20GB word-level Vietnamese Wikipedia corpus, which produces SOTA performances on a range of downstream tasks of POS tagging, dependency parsing, NER, and NLI. + +Following the success of PhoBERT, viBERT [viBERTandvELECTRA] and vELECTRA [viBERTandvELECTRA], both monolingual pre-trained language models based on the BERT and ELECTRA architectures, were introduced. They were trained on substantial datasets, with ViBERT using a 10GB corpus and vELECTRA utilizing an even larger 60GB collection of uncompressed Vietnamese text. viBERT4news\footnote{https://github.com/bino282/bert4news} was published by NlpHUST, a Vietnamese version of BERT trained on more than 20 GB of news datasets. For Vietnamese text summarization, BARTpho [BARTpho] is presented as the first large-scale monolingual seq2seq models pre-trained for Vietnamese, based on the seq2seq denoising autoencoder BART. Moreover, ViT5 [ViT5] follows the encoder-decoder architecture proposed by [attentionisallyouneed] and the T5 framework proposed by [raffel2020exploring]. Many language models are designed for general use, while the availability of strong baseline models for domain-specific applications remains limited. Since then, [vihealthbert] introduced ViHealthBERT, the first domain-specific PLM for Vietnamese healthcare. + +## Pre-trained Language Models for Social Media Texts + +Multiple PLMs were introduced for social media for multilingual and monolinguals. BERTweet [bertweet] was presented as the first public large-scale PLM for English Tweets. BERTweet has the same architecture as BERT$_*Base*$ [bert] and is trained using the RoBERTa pre-training procedure [roberta]. [koto-etal-2021-indobertweet] proposed IndoBERTweet, the first large-scale pre-trained model for Indonesian Twitter. IndoBERTweet is trained by extending a monolingually trained Indonesian BERT model with an additive domain-specific vocabulary. RoBERTuito, presented in [robertuito], is a robust transformer model trained on 500 million Spanish tweets. RoBERTuito excels in various language contexts, including multilingual and code-switching scenarios, such as Spanish and English. TWilBert [twilbert] is proposed as a specialization of BERT architecture both for the Spanish language and the Twitter domain to address text classification tasks in Spanish Twitter. + +Bernice, introduced by [bernice], is the first multilingual pre-trained encoder designed exclusively for Twitter data. This model uses a customized tokenizer trained solely on Twitter data and incorporates a larger volume of Twitter data (2.5B tweets) than most BERT-style models. [twhinbert] introduced TwHIN-BERT, a multilingual language model trained on 7 billion Twitter tweets in more than 100 different languages. It is designed to handle short, noisy, user-generated text effectively. Previously, [barbieri-etal-2022-xlm] extended the training of the XLM-R [xlm-r] checkpoint using a data set comprising 198 million multilingual tweets. As a result, XLM-T is adapted to the Twitter domain and was not exclusively trained on data from within that domain. + +# ViSoBERT \label{ViSoBERT} +This section presents the architecture, pre-training data, and our custom tokenizer on Vietnamese social media texts for ViSoBERT. + +## Pre-training Data + +We crawled textual data from Vietnamese public social networks such as Facebook\footnote{https://www.facebook.com/}, Tiktok\footnote{https://www.tiktok.com/}, and YouTube\footnote{https://www.youtube.com/} which are the three most well known social networks in Vietnam, with 52.65, 49.86, and 63.00 million users\footnote{https://datareportal.com/reports/digital-2023-vietnam}, respectively, in early 2023. + +To effectively gather data from these platforms, we harnessed the capabilities of specialized tools provided by each platform. + + - **Facebook**: We crawled comments from Vietnamese-verified pages by Facebook posts via the Facebook Graph API\footnote{https://developers.facebook.com/} between January 2016 and December 2022. + - **TikTok**: We collected comments from Vietnamese-verified channels by TikTok through TikTok Research API\footnote{https://developers.tiktok.com/products/research-api/} between January 2020 and December 2022. + - **YouTube**: We scrapped comments from Vietnam-verified channels' videos by YouTube via YouTube Data API\footnote{https://developers.google.com/youtube/v3} between January 2016 and December 2022. + +**Pre-processing Data:** \label{preprocessing} +Pre-processing is vital for models consuming social media data, which is massively noisy, and has user handles (@username), hashtags, emojis, misspellings, hyperlinks, and other noncanonical texts. We perform the following steps to clean the dataset: removing noncanonical texts, removing comments including links, removing excessively repeated spam and meaningless comments, removing comments including only user handles (@username), and keeping emojis in training data. + +As a result, our pretraining data after crawling and preprocessing contains 1GB of uncompressed text. Our pretraining data is available only for research purposes. + +## Model Architecture \label{architecture} + +Transformers [attentionisallyouneed] have significantly advanced NLP research using trained models in recent years. Although language models [phobert,phonlp] have also proven effective on a range of Vietnamese NLP tasks, their results on Vietnamese social media tasks [smtce] need to be significantly improved. To address this issue, taking into account successful hyperparameters from XLM-R [xlm-r], we proposed ViSoBERT, a transformer-based model in the style of XLM-R architecture with 768 hidden units, 12 self-attention layers, and 12 attention heads, and used a masked language objective (the same as [xlm-r]). + +## The Vietnamese Social Media Tokenizer \label{tokenizer} +To the best of our knowledge, ViSoBERT is the first PLM with a custom tokenizer for Vietnamese social media texts. Bernice [bernice] was the first multilingual model trained from scratch on Twitter\footnote{https://twitter.com/} data with a custom tokenizer; however, Bernice's tokenizer doesn't handle Vietnamese social media text effectively. Moreover, existing Vietnamese pre-trained models' tokenizers perform poorly on social media text because of different domain data training. Therefore, we developed the first custom tokenizer for Vietnamese social media texts. + +Owing to the ability to handle raw texts of SentencePiece [sentencepiece] without any loss compared to Byte-Pair Encoding [xlm-r], we built a custom tokenizer on Vietnamese social media by SentencePiece on the whole training dataset. +A model has better coverage of data than another when *fewer* subwords are needed to represent the text, and the subwords are *longer* [bernice]. Figure~\ref{fig:tokenlen} (in Appendix~\ref{app:socialtok}) displays the mean token length for each considered model and group of tasks. ViSoBERT achieves the shortest representations for all Vietnamese social media downstream tasks compared to other PLMs. + +Emojis and teencode are essential to the “language” on Vietnamese social media platforms. Our custom tokenizer's capability to decode emojis and teencode ensure that their semantic meaning and contextual significance are accurately captured and incorporated into the language representation, thus enhancing the overall quality and comprehensiveness of text analysis and understanding. + +To assess the tokenized ability of Vietnamese social media textual data, we conducted an analysis of several data samples. Table \ref{tab:tokenizer} shows several actual social comments and their tokenizations with the tokenizers of the two pre-trained language models, ViSoBERT and PhoBERT, the best strong baseline. The results show that our custom tokenizer performed better compared to others. + +\begin{table*}[!ht] +\centering +\resizebox{\textwidth}{!}{ +\begin{tabular}{l|l|l} +\hline +**Comments** & \multicolumn{1}{c|}{**ViSoBERT**} & \multicolumn{1}{c}{**PhoBERT**} \\ +\hline +\begin{tabular}[c]{@{}l@{}}concặc cáilồn gìđây\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\\*English*: Wut is dis fuckingd1ck \includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\end{tabular} & \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{},~"conc", "ặc", "cái", "l", "ồn", "gì", "đây", \\"\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}", \textless{}/s\textgreater{}\end{tabular} & \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "c o n @ @", "c @ @", "ặ c", "c á @ @", \\"i l @ @", "ồ n", "g @ @","ì @ @", "đ â y", \\ \textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \textless{}/s\textgreater{}\end{tabular} \\ +\hline +\begin{tabular}[c]{@{}l@{}}e cảmơn anh\includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}\includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}\\*English*: Thankyou \includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}\includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}\end{tabular} & \textless{}s\textgreater{}, "e", "cảm", "ơn", "anh", "\includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}", "\includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}", \textless{}/s\textgreater{} & \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{},~"e", "c ả @ @", "m @ @", "ơ n", "a n h", \\, , \textless{}/s\textgreater{} \end{tabular}\\ +\hline +\begin{tabular}[c]{@{}l@{}}d4y l4 vj du cko mot cau teencode\\*English*: Th1s 1s 4 teencode s3nt3nc3\end{tabular} & \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "d", "4", "y", "l", "4", "vj", "du", "cko", "mot", \\"cau", "teen", "code", \textless{}/s\textgreater{}\end{tabular} & \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "d @ @", "4 @ @", "y", "l @ @", "4", \\"v @ @", "j", "d u", "c k @ @", "o", "m o @ @", \\"t", "c a u"; "t e @ @", "e n @ @", "c o d e", \textless{}/s\textgreater{}\end{tabular} \\ +\hline +\end{tabular}} +\caption{Actual social comments and their tokenizations with the tokenizers of the two pre-trained language models, ViSoBERT and PhoBERT.} +\label{tab:tokenizer} +\end{table*} + +# Experiments and Results +## Experimental Settings +\label{setup} +We accumulate gradients over one step to simulate a batch size of 128. When pretraining from scratch, we train the model for 1.2M steps in 12 epochs. We trained our model for about three days on 2$\times$RTX4090 GPUs (24GB). Each sentence is tokenized and masked dynamically with a probability equal to 30\ + +\begin{table*}[!ht] +\centering +\resizebox{\textwidth}{!}{ +\begin{tabular}{l|ccc|l|c|c} +\hline +**Dataset** & **Train** & **Dev** & **Test** & \multicolumn{1}{c|}{**Task**} & **Evaluation Metrics** & **Classes** \\ +\hline +UIT-VSMEC & 5,548 & 686 & 693 & Emotion Recognition (ER) & \multirow{5}{*}{Acc, WF1, MF1 (\ +UIT-HSD & 24,048 & 2,672 & 6,680 & Hate Speech Detection (HSD) & & 3 \\ +SA-VLSP2016 & 5,100 & - & 1,050 & Sentiment Analysis (SA) & & 3 \\ +ViSpamReviews & 14,306 & 1,590 & 3,974 & Spam Reviews Detection (SRD) & & 4 \\ +ViHOS & 8,844 & 1,106 & 1,106 & Hate Speech Spans Detection (HSSD) & & 3 \\ +\hline +\end{tabular}} +\caption{Statistics and descriptions of Vietnamese social media processing tasks. Acc, WF1, and MF1 denoted Accuracy, weighted F1-score, and macro F1-score metrics, respectively.} +\label{tab:datasets} +\end{table*} + +**Downstream tasks.** To evaluate ViSoBERT, we used five Vietnamese social media datasets available for research purposes, as summarized in Table \ref{tab:datasets}. The downstream tasks include emotion recognition (UIT-VSMEC) [ho2020emotion], hate speech detection (UIT-ViHSD) [vihsd], sentiment analysis (SA-VLSP2016) [nguyen2018vlsp], spam reviews detection (ViSpamReviews) [vispamreviews], and hate speech spans detection (UIT-ViHOS) [vihos]. + +**Fine-tuning.** We conducted empirical fine-tuning for all pre-trained language models using the *simpletransformers*\footnote{https://simpletransformers.ai/ (ver 0.63.11)}. Our fine-tuning process followed standard procedures, most of which are outlined in [bert]. For all tasks mentioned above, we use a batch size of 40, a maximum token length of 128, a learning rate of 2e-5, and AdamW optimizer [AdamW] with an epsilon of 1e-8. We executed a 10-epoch training process and evaluated downstream tasks using the best-performing model from those epochs. Furthermore, none of the pre-processing techniques is applied in all datasets to evaluate our PLM's ability to handle raw texts. + +**Baseline models.** To establish the main baseline models, we utilized several well-known PLMs, including monolingual and multilingual, to support Vietnamese NLP social media tasks. The details of each model are shown in Table \ref{tab:pre-trained}. + + - **Monolingual language models**: viBERT [viBERTandvELECTRA] and vELECTRA [viBERTandvELECTRA] are PLMs for Vietnamese based on BERT and ELECTRA architecture, respectively. PhoBERT, which is based on BERT architecture and RoBERTa pre-training procedure, [phobert] is the first large-scale monolingual language model pre-trained for Vietnamese; PhoBERT obtains state-of-the-art performances on a range of Vietnamese NLP tasks. + + - **Multilingual language models**: Additionally, we incorporated two multilingual PLMs, mBERT [bert] and XLM-R [xlm-r], which were previously shown to have competitive performances to monolingual Vietnamese models. XLM-R, a cross-lingual PLM introduced by [xlm-r], has been trained in 100 languages, among them Vietnamese, utilizing a vast 2.5TB Clean CommonCrawl dataset. XLM-R presents notable improvements in various downstream tasks, surpassing the performance of previously released multilingual models such as mBERT [bert] and XLM [xlm]. + + - **Multilingual social media language models:** To ensure a fair comparison with our PLM, we integrated multiple multilingual social media PLMs, including XLM-T [barbieri-etal-2022-xlm], TwHIN-BERT [twhinbert], and Bernice [bernice]. + +\begin{table*}[!ht] +\centering +\resizebox{\textwidth}{!}{ +\begin{tabular}{l|ccccccccc} +\hline +**Model** & **\#Layers** & **\#Heads** & **\#Steps** & **\#Batch** & **Domain Data** & **\#Params** & **\#Vocab** & **\#MSL** & \multicolumn{1}{l}{**CSMT**} \\ +\hline +viBERT [viBERTandvELECTRA] & 12 & 12 & - & 16 & Vietnamese News & - & 30K & 256 & \textcolor{red}{No} \\ +vELECTRA [viBERTandvELECTRA] & 12 & 3 & - & 16 & NewsCorpus + OscarCorpus & - & 30K & 256 & \textcolor{red}{No} \\ +PhoBERT$_\text{Base}$ [phobert] & 12 & 12 & 540K & 1024 & ViWiki + ViNews & 135M & 64K & 256 & \textcolor{red}{No} \\ +PhoBERT$_\text{Large}$ [phobert] & 24 & 16 & 1.08M & 512 & ViWiki + ViNews & 370M & 64K & 256 & \textcolor{red}{No} \\ +\hline +mBERT [bert] & 12 & 12 & 1M & 256 & BookCorpus + EnWiki & 110M & 30K & 512 & \textcolor{red}{No} \\ +XLM-R$_\text{Base}$ [xlm-r] & 12 & 12 & 1.5M & 8192 & CommonCrawl + Wiki & 270M & 250K & 512 & \textcolor{red}{No} \\ +XLM-R$_\text{Large}$ [xlm-r] & 24 & 16 & 1.5M & 8192 & CommonCrawl + Wiki & 550M & 250K & 512 & \textcolor{red}{No} \\ +XLM-T [barbieri-etal-2022-xlm] & 12 & 12 & - & 8192 & Multilingual Tweets & - & 250k & 512 & \textcolor{red}{No} \\ +TwHIN-BERT$_\text{Base}$ [twhinbert] & 12 & 12 & 500K & 6K & Multilingual Tweets & 135M to 278M & 250K & 128 & \textcolor{red}{No} \\ +TwHIN-BERT$_\text{Large}$ [twhinbert] & 24 & 16 & 500K & 8K & Multilingual Tweets & 550M & 250K & 128 & \textcolor{red}{No} \\ +Bernice [bernice] & 12 & 12 & 405K+ & 8192 & Multilingual Tweets & 270M & 250K & 128 & \textcolor{green}{Yes} \\ +\hdashline +ViSoBERT (Ours) & 12 & 12 & 1.2M & 128 & Vietnamese social media & 97M & 15K & 512 & \textcolor{green}{Yes} \\ +\hline +\end{tabular}} +\caption{Detailed information about baselines and our PLM. \#Layers, \#Heads, \#Batch, \#Params, \#Vocab, \#MSL, and CSMT indicate the number of hidden layers, number of attention heads, batch size value, domain training data, number of total parameters, vocabulary size, max sequence length, and custom social media tokenizer, respectively.} +\label{tab:pre-trained} +\end{table*} + +## Main Results +Table~\ref{mainresult} shows ViSoBERT's scores with the previous highest reported results on other PLMs using the same experimental setup. It is clear that our ViSoBERT produces new SOTA performance results for multiple Vietnamese downstream social media tasks without any pre-processing technique. + +\begin{table*}[!ht] +\centering +\resizebox{\textwidth}{!}{ + +\begin{tabular}{l|c|ccj|ccj|ccj|>{\centering\arraybackslash}m{1.1cm}>{\centering\arraybackslash}m{1.1cm}S[table-format=2.2,table-space-text-pre=~,table-space-text-post=\ddgr,table-column-width=1.1cm]|>{\centering\arraybackslash}m{1.1cm}>{\centering\arraybackslash}m{1.8cm}S[table-format=2.2,table-space-text-pre=~,table-space-text-post=\ddgr,table-column-width=1.1cm]} +\hline +\multicolumn{1}{c|}{\multirow{2}{*}{**Model**}} & \multirow{2}{*}{**Avg**} & \multicolumn{3}{c|}{**Emotion Regconition**} & \multicolumn{3}{c|}{**Hate Speech Detection**} & \multicolumn{3}{c|}{**Sentiment Analysis**} & \multicolumn{3}{c|}{**Spam Reviews Detection**} & \multicolumn{3}{c}{**Hate Speech Spans Detection**} \\ +\cline{3-17} +\multicolumn{1}{c|}{} & & **Acc** & **WF1** & **MF1** & **Acc** & **WF1** & \multicolumn{1}{c|}{**MF1**} & **Acc** & **WF1** & **MF1** & **Acc** & **WF1** & \multicolumn{1}{c|}{**MF1**} & **Acc** & **WF1** & \multicolumn{1}{c}{**MF1**} \\ +\hline +viBERT & 71.57 & 61.91 & 61.98 & 59.70 & 85.34 & 85.01 & 62.07 & 74.85 & 74.73 & 74.73 & 89.93 & 89.79 & 76.80 & 90.42 & 90.45 & 84.55 \\ +vELECTRA & 72.43 & 64.79 & 64.71 & 61.95 & 86.96 & 86.37 & 63.95 & 74.95 & 74.88 & 74.88 & 89.83 & 89.68 & 76.23 & 90.59 & 90.58 & 85.12 \\ +PhoBERT$_\text{Base}$ & 72.81 & 63.49 & 63.36 & 61.41 & 87.12 & 86.81 & 65.01 & 75.72 & 75.52 & 75.52 & 89.83 & 89.75 & 76.18 & 91.32 & 91.38 & 85.92 \\ +PhoBERT$_\text{Large}$ & 73.47 & 64.71 & 64.66 & 62.55 & 87.32 & 86.98 & 65.14 & 76.52 & 76.36 & 76.22 & 90.12 & 90.03 & 76.88 & 91.44 & 91.46 & 86.56 \\ +\hline +mBERT (cased) & 68.07 & 56.27 & 56.17 & 53.48 & 83.55 & 83.99 & 60.62 & 67.14 & 67.16 & 67.16 & 89.05 & 88.89 & 74.52 & 89.88 & 89.87 & 84.57 \\ +mBERT (uncased) & 67.66 & 56.23 & 56.11 & 53.32 & 83.38 & 81.27 & 58.92 & 67.25 & 67.22 & 67.22 & 88.92 & 88.72 & 74.32 & 89.84 & 89.82 & 84.51 \\ +XLM-R$_\text{Base}$ & 72.08 & 60.92 & 61.02 & 58.67 & 86.36 & 86.08 & 63.39 & 76.38 & 76.38 & 76.38 & 90.16 & 89.96 & 76.55 & 90.74 & 90.72 & 85.42 \\ +XLM-R$_\text{Large}$ & 73.40 & 62.44 & 61.37 & 60.25 & 87.15 & 86.86 & 65.13 & **78.28** & **78.21** & **78.21** & 90.36 & 90.31 & 76.75 & 91.52 & 91.50 & 86.66 \\ +XLM-T & 72.23 & 64.64 & 64.37 & \multicolumn{1}{c|}{59.86} & 86.22 & 86.12 & 63.48 & 75.66 & 75.60 & \multicolumn{1}{c|}{75.60} & 90.07 & 90.11 & 76.66 & 90.88 & 90.88 & 85.53 \\ +TwHIN-BERT$_\text{Base}$ & 71.60 & 61.49 & 60.88 & 57.97 & 86.63 & 86.23 & 63.67 & 73.76 & 73.72 & 73.72 & 90.25 & 90.35 & 76.98 & 90.99 & 90.90 & 85.67 \\ +TwHIN-BERT$_\text{Large}$ & 73.42 & 64.21 & 64.29 & 61.12 & 87.23 & 86.78 & 65.23 & 76.92 & 76.83 & 76.83 & 90.47 & 90.42 & 77.28 & 91.45 & 91.47 & 86.65 \\ +Bernice & 72.49 & 64.21 & 64.27 & \multicolumn{1}{c|}{60.68} & 86.12 & 86.48 & 64.32 & 74.57 & 74.90 & \multicolumn{1}{c|}{74.90} & 90.22 & 90.21 & 76.89 & 90.48 & 90.06 & 85.67 \\ +\hdashline +ViSoBERT & **75.65** & **68.10** & **68.37** & \bfseries 65.88\ddgr & **88.51** & **88.31** & \bfseries 68.77\ddgr & 77.83 & 77.75 & 77.75 & **90.99** & **90.92** & \bfseries 79.06\ddgr & **91.62** & **91.57** & **86.80** \\ +\hline +\end{tabular}} +\caption{Performances on downstream Vietnamese social media tasks of previous state-of-the-art monolingual and multilingual PLMs without pre-processing techniques. Avg denoted the average MF1 score of each PLM. \ddgr~denotes that the highest result is statistically significant at $p < \text{0.01}$ compared to the second best, using a paired t-test.} +\label{mainresult} +\end{table*} + +**Emotion Recognition Task**: PhoBERT and TwHIN-BERT archive the previous SOTA performances on monolingual and multilingual models, respectively. ViSoBERT obtains 68.10\ + +**Hate Speech Detection Task**: ViSoBERT achieves significant improvements over previous state-of-the-art models, PhoBERT and TwHIN-BERT, with scores of 88.51\ + +**Sentiment Analysis Task**: XLM-R archived SOTA performance on three evaluation metrics. However, there is no significant increase in performance on this downstream task, for 0.45\ +The SA-VLSP2016 dataset domain is technical article reviews, including TinhTe\footnote{https://tinhte.vn/} and VnExpress\footnote{https://vnexpress.net/}, which are often used as Vietnamese standard data. The reviews or comments in these newspapers are in proper form. While most of the dataset is sourced from articles, it also includes data from Facebook\footnote{https://www.facebook.com/}, a Vietnamese social media platform that accounts for only 12.21\ + +**Spam Reviews Detection Task**: ViSoBERT performed better than the top two baseline models, PhoBERT and TwHIN-BERT. Specifically, it achieved 0.8\ + +**Hate Speech Spans Detection Task**\footnote{For the Hate Speech Spans Detection task, we evaluate the total of spans on each comment rather than spans of each word in [vihos] to retain the context of each comment.}: Our pre-trained ViSoBERT boosted the results up to 91.62\ + +**Multilingual social media PLMs**: The results show that ViSoBERT consistently outperforms XLM-T and Bernice in five Vietnamese social media tasks. It's worth noting that XLM-T, TwHIN-BERT, and Bernice were all exclusively trained on data from the Twitter platform. However, this approach has limitations when applied to the Vietnamese context. The training data from this source may not capture the intricate linguistic and contextual nuances prevalent in Vietnamese social media because Twitter is not widely used in Vietnam. + +# Result Analysis and Discussion +In this section, we consider the improvement of our PLM more compared to powerful others, including PhoBERT and TwHIN-BERT, in terms of different aspects. Firstly, we investigate the effects of masking rate on our pre-trained model performance (see Section \ref{impactofmasking}). Additionally, we examine the influence of social media characteristics on the model's ability to process and understand the language used in these social contexts (see Section \ref{characteristics}). Lastly, we employed feature-based extraction techniques on task-specific models to verify the potential of leveraging social media textual data to enhance word representations (see Section \ref{feature-based}). + +## Impact of Masking Rate on Vietnamese Social Media PLM\label{impactofmasking} +For the first time presenting the Masked Language Model, [bert] consciously utilized a random masking rate of 15\ + +We experiment with masking rates ranging from 10\ + +However, the optimal masking rate also depends on the specific task. For instance, in the hate speech detection task, we found that a masking rate of 50\ + +Considering the overall performance across multiple tasks, we determined that a masking rate of 30\ + +\begin{figure}[ht] + \centering + + \includegraphics[width=\columnwidth]{f1_macro_masked_rate1111-crop.pdf} + \caption{Impact of masking rate on our pre-trained ViSoBERT in terms of MF1.} + \label{maskingrate} +\end{figure} + +## Impact of Vietnamese Social Media Characteristics \label{characteristics} +Emojis, teencode, and diacritics are essential features of social media, especially Vietnamese social media. The ability of the tokenizer to decode emojis and the ability of the model to understand the context of teencode and diacritics are crucial. Hence, to evaluate the performance of ViSoBERT on social media characteristics, comprehensive experiments were conducted among several strong PLMs: PM4ViSMT, PhoBERT, and TwHIN-BERT. + +**Impact of Emoji on PLMs:** +\begin{table*}[!ht] +\centering +\resizebox{\textwidth}{!}{ +\begin{tabular}{c|www|www|www|www|S[table-column-width=1.1cm,table-format=2.2,table-space-text-pre=\dwnarr,color=black]S[table-column-width=1.8cm,table-format=2.2,table-space-text-pre=\dwnarr,color=black]S[table-column-width=1.1cm,table-format=2.2,table-space-text-pre=\dwnarr,color=black]} +\hline +\multirow{2}{*}{**Model**} & \multicolumn{3}{c|}{**Emotion Regconition**} & \multicolumn{3}{c|}{**Hate Speech Detection**} & \multicolumn{3}{c|}{**Sentiment Analysis**} & \multicolumn{3}{c|}{**Spam Reviews Detection**} & \multicolumn{3}{c}{**Hate Speech Spans Detection**} \\ +\cline{2-16} + & **Acc** & **WF1** & **MF1** & **Acc** & **WF1** & **MF1** & **Acc** & **WF1** & **MF1** & **Acc** & **WF1** & **MF1** & **Acc** & **WF1** & **MF1** \\ +\hline +\multicolumn{16}{c}{***Converting emojis to text***} \\ +\hline +\multicolumn{1}{l|}{PhoBERT$_\text{Large}$} & 66.08 & 66.15 & 63.35 & 87.43 & 87.22 & 65.32 & 76.73 & 76.48 & 76.48 & 90.35 & 90.11 & 77.02 & 92.16 & 91.98 & 86.72 \\ +$\Delta$ & \multicolumn{1}{y}{\uparr 1.37} & \multicolumn{1}{y}{\uparr 1.49} & \multicolumn{1}{y|}{\uparr 0.80} & \multicolumn{1}{y}{\uparr 0.11} & \multicolumn{1}{y}{\uparr 0.24} & \multicolumn{1}{y|}{\uparr 0.18} & \multicolumn{1}{z}{\dwnarr 0.21} & \multicolumn{1}{z}{\dwnarr 0.12} & \multicolumn{1}{z|}{\dwnarr 0.12} & \multicolumn{1}{y}{\uparr 0.23} & \multicolumn{1}{y}{\uparr 0.08} & \multicolumn{1}{y|}{\uparr 0.14} & \multicolumn{1}{y}{\uparr 0.72} & \multicolumn{1}{y}{\uparr 0.52} & \multicolumn{1}{y}{\uparr 0.16} \\ +\multicolumn{1}{l|}{TwHIN-BERT$_\text{Large}$} & 64.82 & 64.42 & 61.33 & 86.03 & 85.52 & 63.52 & 75.42 & 75.95 & 75.95 & 90.55 & 90.47 & 77.32 & 92.21 & 92.01 & 86.84 \\ +$\Delta$ & \multicolumn{1}{y}{\uparr 0.61} & \multicolumn{1}{y}{\uparr 0.13} & \multicolumn{1}{y|}{\uparr 0.21} & \multicolumn{1}{z}{\dwnarr 1.20} & \multicolumn{1}{z}{\dwnarr 1.26} & \multicolumn{1}{z|}{\dwnarr 1.71} & \multicolumn{1}{z}{\dwnarr 1.50} & \multicolumn{1}{z}{\dwnarr 0.88} & \multicolumn{1}{z|}{\dwnarr 0.88} & \multicolumn{1}{y}{\uparr 0.08} & \multicolumn{1}{y}{\uparr 0.05} & \multicolumn{1}{y|}{\uparr 0.04} & \multicolumn{1}{y}{\uparr 0.76} & \multicolumn{1}{y}{\uparr 0.54} & \multicolumn{1}{y}{\uparr 0.19} \\ +\hdashline +\multicolumn{1}{l|}{ViSoBERT $[\clubsuit]$} & 67.53 & 67.93 & 65.42 & 87.82 & 87.88 & 67.25 & 76.95 & 76.85 & 76.85 & 90.22 & 90.18 & 78.25 & 92.42 & 92.11 & 87.01 \\ +$\Delta$ & \multicolumn{1}{z}{\dwnarr 0.57} & \multicolumn{1}{z}{\dwnarr 0.44} & \multicolumn{1}{z|}{\dwnarr 0.46} & \multicolumn{1}{z}{\dwnarr 0.69} & \multicolumn{1}{z}{\dwnarr 0.41} & \multicolumn{1}{z|}{\dwnarr 1.49} & \multicolumn{1}{z}{\dwnarr 0.88} & \multicolumn{1}{z}{\dwnarr 0.90} & \multicolumn{1}{z|}{\dwnarr 0.90} & \multicolumn{1}{z}{\dwnarr 0.77} & \multicolumn{1}{z}{\dwnarr 0.74} & \multicolumn{1}{z|}{\dwnarr 0.81} & \multicolumn{1}{y}{\uparr 0.80} & \multicolumn{1}{y}{\uparr 0.54} & \multicolumn{1}{y}{\uparr 0.21} \\ +\hline +\multicolumn{16}{c}{***Removing emojis***} \\ +\hline +\multicolumn{1}{l|}{PhoBERT$_\text{Large}$} & 65.21 & 65.14 & 62.81 & 87.25 & 86.72 & 64.85 & 76.72 & 76.48 & 76.48 & 90.21 & 90.09 & 77.02 & 91.53 & 91.51 & 86.62 \\ +$\Delta$ & \multicolumn{1}{y}{\uparr 0.50} & \multicolumn{1}{y}{\uparr 0.48} & \multicolumn{1}{y|}{\uparr 0.26} & \multicolumn{1}{z}{\dwnarr 0.07} & \multicolumn{1}{z}{\dwnarr 0.26} & \multicolumn{1}{z|}{\dwnarr 0.29} & \multicolumn{1}{y}{\uparr 0.20} & \multicolumn{1}{y}{\uparr 0.12} & \multicolumn{1}{y|}{\uparr 0.12} & \multicolumn{1}{y}{\uparr 0.09} & \multicolumn{1}{y}{\uparr 0.06} & \multicolumn{1}{y|}{\uparr 0.10} & \multicolumn{1}{y}{\uparr 0.09} & \multicolumn{1}{y}{\uparr 0.05} & \multicolumn{1}{y}{\uparr 0.09} \\ +\multicolumn{1}{l|}{TwHIN-BERT$_\text{Large}$} & 62.03 & 62.14 & 59.25 & 86.98 & 86.32 & 64.22 & 75.00 & 75.11 & 75.11 & 89.83 & 89.75 & 76.85 & 91.32 & 91.33 & 86.42 \\ +$\Delta$ & \multicolumn{1}{z}{\dwnarr 2.18} & \multicolumn{1}{z}{\dwnarr 1.15} & \multicolumn{1}{z|}{\dwnarr 1.87} & \multicolumn{1}{z}{\dwnarr 0.25} & \multicolumn{1}{z}{\dwnarr 0.46} & \multicolumn{1}{z|}{\dwnarr 1.01} & \multicolumn{1}{z}{\dwnarr 1.92} & \multicolumn{1}{z}{\dwnarr 1.72} & \multicolumn{1}{z|}{\dwnarr 1.72} & \multicolumn{1}{z}{\dwnarr 0.64} & \multicolumn{1}{z}{\dwnarr 0.67} & \multicolumn{1}{z|}{\dwnarr 0.43} & \multicolumn{1}{z}{\dwnarr 0.13} & \multicolumn{1}{z}{\dwnarr 0.14} & \multicolumn{1}{z}{\dwnarr 0.23} \\ +\hdashline +\multicolumn{1}{l|}{ViSoBERT $[\vardiamondsuit]$} & 66.52 & 67.02 & 64.55 & 87.32 & 87.12 & 66.98 & 76.25 & 75.98 & 75.98 & 89.72 & 89.69 & 77.95 & 91.58 & 91.53 & 86.72 \\ +$\Delta$ & \multicolumn{1}{z}{\dwnarr 1.58} & \multicolumn{1}{z}{\dwnarr 1.35} & \multicolumn{1}{z|}{\dwnarr 1.33} & \multicolumn{1}{z}{\dwnarr 1.19} & \multicolumn{1}{z}{\dwnarr 1.19} & \multicolumn{1}{z|}{\dwnarr 1.79} & \multicolumn{1}{z}{\dwnarr 1.58} & \multicolumn{1}{z}{\dwnarr 1.77} & \multicolumn{1}{z|}{\dwnarr 1.77} & \multicolumn{1}{z}{\dwnarr 1.27} & \multicolumn{1}{z}{\dwnarr 1.23} & \multicolumn{1}{z|}{\dwnarr 1.11} & \multicolumn{1}{z}{\dwnarr 0.04} & \multicolumn{1}{z}{\dwnarr 0.04} & \multicolumn{1}{z}{\dwnarr 0.08} \\ +\hline +\multicolumn{1}{l|}{ViSoBERT $[\spadesuit]$} & \bfseries 68.10 & \bfseries 68.37 & \bfseries 65.88 & \bfseries 88.51 & \bfseries 88.31 & \bfseries 68.77 & \bfseries 77.83 & \bfseries 77.75 & \bfseries 77.75 & \bfseries 90.99 & \bfseries 90.92 & \bfseries 79.06 & \bfseries 91.62 & \bfseries 91.57 & \bfseries 86.80 \\ +\hline +\end{tabular}} +\caption{Performances of pre-trained models on downstream Vietnamese social media tasks by applying two emojis pre-processing techniques. $[\clubsuit]$, $[\vardiamondsuit]$, and $[\spadesuit]$ denoted our pre-trained language model ViSoBERT converting emoji to text, removing emojis and without applying any pre-processing techniques, respectively. $\Delta$ denoted the increase ($\uparrow$) and the decrease ($\downarrow$) in performances of the PLMs compared to their competitors without applying any pre-processing techniques.} +\label{tab:emoji} +\end{table*} + +We conducted two experimental procedures to comprehensively investigate the importance of emojis, including converting emojis to general text and removing emojis. + +Table~\ref{tab:emoji} shows our detailed setting and experimental results on downstream tasks and pre-trained models. +The results indicate a moderate reduction in performance across all downstream tasks when emojis are removed or converted to text in our pre-trained ViSoBERT model. Our pre-trained decreases 0.62\ + +This trend is also observed in the TwHIN-BERT model, specifically designed for social media processing. However, TwHIN-BERT slightly improves emotion recognition and spam reviews detection tasks compared to its competitors when operating on raw texts. Nevertheless, this improvement is marginal and insignificant, as indicated by the small increments of 0.61\ + +In contrast, there is a general trend of improved performance across a range of downstream tasks when removing or converting emojis to text on PhoBERT, the Vietnamese SOTA pre-trained language model. PhoBERT is a PLM on a general text (Vietnamese Wikipedia) dataset containing no emojis; therefore, when PhoBERT encounters an emoji, it treats it as an unknown token (see Table~\ref{tab:tokenizer} Appendix~\ref{app:exp}). Therefore, while applying emoji pre-processing techniques, including converting emoijs to text and removing emojis, PhoBERT produces better performances compared to raw text. + +Our pre-trained model ViSoBERT on raw texts outperformed PhoBERT and TwHIN-BERT even when applying two pre-processing emojis techniques. This claims our pre-trained model's ability to handle Vietnamese social media raw texts. + +**Impact of Teencode on PLMs:** + +Due to informal and casual communication, social media texts often lead to common linguistic errors, such as misspellings and teencode. For example, the phrase ``ăng kơmmmmm'' should be ``ăn cơm'' (``Eat rice'' in English), and ``ko'' should be ``không'' (``No'' in English). To address this challenge, [nguyen2020exploiting] presented several rules to standardize social media texts. Building upon the previous work, [phobertcnn] proposed a strict and efficient pre-processing technique to clean comments on Vietnamese social media. + +Table~\ref{tab:teencode} (in Appendix~\ref{appendix:pre-process}) shows the results with and without standardizing teencode on social media texts. There is an uptrend across PhoBERT, TwHIN-BERT, and ViSoBERT while applying standardized pre-processing techniques. ViSoBERT, with standardized pre-processing techniques, outperforms almost downstream tasks but spam reviews detection. The possible reason is that the ViSpamReviews dataset contains samples in which users use the word with duplicated characters to improve the comment length while standardizing teencodes leads to misunderstanding. + +Experimental results strongly suggest that the improvement achieved by applying complex pre-processing techniques to pre-trained models in the context of Vietnamese social media text is relatively insignificant. Despite the considerable time and effort invested in designing and implementing these techniques, the actual gains in PLMs performance are not substantial and unstable. + +**Impact of Vietnamese Diacritics on PLMs:** +Vietnamese words are created from 29 letters, including seven letters using four diacritics (ă, â-ê-ô, ơ-ư, and đ) and five diacritics used to designate tone (as in à, á, ả, ã, and ạ) [ngo2020vietnamese]. These diacritics create meaningful words by combining syllables [diacritics]. For instance, the syllable ``ngu'' can be combined with five different diacritic marks, resulting in five distinct syllables: ``ngú'', ``ngù'', ``ngụ'', ``ngủ'', and ``ngũ''. Each of these syllables functions as a standalone word. + +However, social media text does not always adhere to proper writing conventions. Due to various reasons, many users write text without diacritic marks when commenting on social media platforms. Consequently, effectively handling diacritics in Vietnamese social media becomes a critical challenge. To evaluate the PLMs' capability to address this challenge, we experimented by removing all diacritic marks from the datasets of five downstream tasks. This experiment aimed to assess the model's performance in processing text without diacritics and determine its ability to understand Vietnamese social media content in such cases. + +Table~\ref{tab:diacritics} (in Appendix~\ref{appendix:pre-process}) presents the results of the two best baselines compared to our pre-trained diacritics experiments. The experimental results reveal that the performance of all pre-trained models, including ours, exhibited a significant decrease when dealing with social media comments lacking diacritics. This decline in performance can be attributed to the loss of contextual information caused by the removal of diacritics. The lower the percentage of diacritic removal in each comment, the more significant the performance improvement in all PLMs. However, our ViSoBERT demonstrated a relatively minor reduction in performance across all downstream tasks. This suggests that our model possesses a certain level of robustness and adaptability in comprehending and analyzing Vietnamese social media content without diacritics. We attribute this to the efficiency of the in-domain pre-training data of ViSoBERT. + +In contrast, PhoBERT and TwHIN-BERT experienced a substantial drop in performance across the benchmark datasets. These PLMs struggled to cope with the absence of diacritics in Vietnamese social media comments. The main reason is that the tokenizer of PhoBERT can not encode non-diacritics comments due to not including those in pre-training data. Several tokenized examples of the three best PLMs are presented in Table~\ref{tab:diacriticscomments} (in Appendix~\ref{removingdiacritics}). Thus, the significant decrease in its performance highlights the challenge of handling diacritics on Vietnamese social media. While handling diacritics remains challenging, ViSoBERT demonstrates promising performance, suggesting the potential for specialized language models tailored for Vietnamese social media analysis. + +## Impact of Feature-based Extraction to Task-Specific Models \label{feature-based} + +In task-specific models, the contextualized word embeddings from PLMs are typically employed as input features. We aim to assess the quality of contextualized word embeddings generated by PhoBERT, TwHIN-BERT, and ViSoBERT to verify whether social media data can enhance word representation. These contextualized word embeddings are applied as embedding features to BiLSTM, and BiGRU is randomly initialized before the classification layer. We append a linear prediction layer to the last transformer layer of each PLM regarding the first subword of each word token, which is similar to [bert]. + +Our experiment results (see Table~\ref{tab:feature-based} in Appendix~\ref{appendix:pre-process}) demonstrate that the word embeddings generated by our pre-trained language model ViSoBERT outperform other pre-trained embeddings when utilized with BiLSTM and BiGRU for all downstream tasks. The experimental results indicate the significant impact of leveraging social media text data for enriching word embeddings. Furthermore, this finding underscores the effectiveness of our model in capturing the linguistic characteristics prevalent in Vietnamese social media texts. + +Figure~\ref{fig:perepoch} (in Appendix~\ref{plm-based}) presents the performances of the PLMs as input features to BiLSTM and BiGRU on the dev set per epoch in terms of MF1. The results demonstrate that ViSoBERT reaches its peak MF1 score in only 1 to 3 epochs, whereas other PLMs typically require an average of 8 to 10 epochs to achieve on-par performance. This suggests that ViSoBERT has a superior capability to extract Vietnamese social media information compared to other models. + +# Conclusion and Future Work +We presented ViSoBERT, a novel large-scale monolingual pre-trained language model on Vietnamese social media texts. We illustrated that ViSoBERT with fewer parameters outperforms recent strong pre-trained language models such as viBERT, vELECTRA, PhoBERT, XLM-R, XLM-T, TwHIN-BERT, and Bernice and achieves state-of-the-art performances for multiple downstream Vietnamese social media tasks, including emotion recognition, hate speech detection, spam reviews detection, and hate speech spans detection. We conducted extensive analyses to demonstrate the efficiency of ViSoBERT on various Vietnamese social media characteristics, including emojis, teencodes, and diacritics. Furthermore, our pre-trained language model ViSoBERT also shows the potential of leveraging Vietnamese social media text to enhance word representations compared to other PLMs. We hope the widespread use of our open-source ViSoBERT pre-trained language model will advance current NLP social media tasks and applications for Vietnamese. Other low-resource languages can adopt how to create PLMs for enhancing their current NLP social media tasks and relevant applications. + +\newpage +# Limitations +While we have demonstrated that ViSoBERT can perform state-of-the-art on a range of NLP social media tasks for Vietnamese, we think additional analyses and experiments are necessary to fully comprehend what aspects of ViSoBERT were responsible for its success and what understanding of Vietnamese social media texts ViSoBERT captures. We leave these additional investigations to future research. Moreover, future work aims to explore a broader range of Vietnamese social media downstream tasks that this paper may not cover. Also, we chose to train the base-size transformer model instead of the *Large* variant because base models are more accessible due to their lower computational requirements. For PhoBERT, XLM-R, and TwHIN-BERT, we implemented two versions *Base* and *Large* for all Vietnamese social media downstream tasks. However, it is not a fair comparison due to their significantly larger model configurations. Moreover, regular updates and expansions of the pre-training data are essential to keep up with the rapid evolution of social media. This allows the pre-trained model to adapt effectively to the dynamic linguistic patterns and trends in Vietnamese social media. + +# Ethics Statement +The authors introduce ViSoBERT, a pre-trained language model for investigating social language phenomena in social media in Vietnamese. ViSoBERT is based on pre-training an existing pre-trained language model (i.e., XLM-R), which lessens the influence of its construction on the environment. ViSoBERT makes use of a large-scale corpus of posts and comments from social communities that have been found to express harassment, bullying, incitement of violence, hate, offense, and abuse, as defined by the content policies of social media platforms, including Facebook, YouTube, and TikTok. + +# Acknowledgement +This research was supported by The VNUHCM-University of Information Technology’s Scientific Research Support Fund. We thank the anonymous EMNLP reviewers for their time and helpful suggestions that improved the quality of the paper. + +\bibliography{anthology} +\bibliographystyle{acl_natbib} + +\newpage + +\onecolumn +\appendix + +# Tokenizations of the PLMs on Social Comments +\label{app:socialtok} + + +We conducted an analysis of average token length by tasks of Pre-trained Language Models to provide insights into how different PLMs perform regarding token length across various Vietnamese social media downstream tasks. Figure~\ref{fig:tokenlen} shows the average token length by Vietnamese social media downstream tasks of baseline PLMs and ours. + +\begin{figure*}[!h] + \centering + \includegraphics[width=\textwidth]{token_length11111-crop.pdf} + \caption{Average token length by tasks of PLMs.} + \label{fig:tokenlen} +\end{figure*} + +# Experimental Settings +\label{app:exp} + +Following the hyperparameters in Table \ref{tab:hyperparameters}, we train our pre-trained language model ViSoBERT for Vietnamese social media texts. +\begin{table}[!ht] +\centering +\begin{tabular}{llr} +\hline +\multirow{7}{*}{Optimizer} & Algorithm & Adam \\ + & Learning rate & 5e-5 \\ + & Epsilon & 1e-8 \\ + & LR scheduler & linear decay and warmup \\ + & Warmup steps & 1000 \\ + & Betas & 0.9 and 0.99 \\ + & Weight decay & 0.01 \\ +\hline +\multirow{3}{*}{Batch} & Sequence length & 128 \\ + & Batch size & 128 \\ + & Vocab size & 15002 \\ +\hline +\multirow{2}{*}{Misc} & Dropout & 0.1 \\ + & Attention dropout & 0.1 \\ +\hline +\end{tabular} +\caption{All hyperparameters established for training ViSoBERT.} +\label{tab:hyperparameters} +\end{table} +\newpage + +# PLMs with Pre-processing Techniques +\label{appendix:pre-process} + +For an in-depth understanding of the impact of social media texts on PLMs, we conducted an analysis of the test results on various processing aspects. Table~\ref{tab:teencode} presents performances of the pre-trained language models on downstream Vietnamese social media tasks by applying word standardizing pre-processing techniques, while Table~\ref{tab:diacritics} presents performances of the pre-trained language models on downstream Vietnamese social media tasks by removing diacritics in all datasets. + +\begin{table*}[!ht] +\centering +\resizebox{\textwidth}{!}{ +\begin{tabular}{c|www|www|www|www|S[table-column-width=1.1cm,table-format=2.2,table-space-text-pre=\dwnarr,color=black]S[table-column-width=1.8cm,table-format=2.2,table-space-text-pre=\dwnarr,color=black]S[table-column-width=1.1cm,table-format=2.2,table-space-text-pre=\dwnarr,color=black]} +\hline +\multirow{2}{*}{**Model**} & \multicolumn{3}{c|}{**Emotion Recognition**} & \multicolumn{3}{c|}{**Hate Speech Detection**} & \multicolumn{3}{c|}{**Sentiment Analysis**} & \multicolumn{3}{c|}{**Spam Reviews Detection**} & \multicolumn{3}{c}{**Hate Speech Spans Detection**} \\ +\cline{2-16} + & **Acc** & **WF1** & **MF1** & **Acc** & **WF1** & **MF1** & **Acc** & **WF1** & **MF1** & **Acc** & **WF1** & **MF1** & **Acc** & **WF1** & **MF1** \\ +\hline +\multicolumn{1}{l|}{PhoBERT$_\text{Large}$} & 64.94 & 64.85 & 62.71 & 87.68 & 87.25 & 65.41 & 76.80 & 76.61 & 76.61 & 89.47 & 89.41 & 76.12 & 91.73 & 91.62 & 86.59 \\ +$\Delta$ & \multicolumn{1}{y}{\uparr 0.23} & \multicolumn{1}{y}{\uparr 0.19} & \multicolumn{1}{y|}{\uparr 0.16} & \multicolumn{1}{y}{\uparr 0.36} & \multicolumn{1}{y}{\uparr 0.27} & \multicolumn{1}{y|}{\uparr 0.27} & \multicolumn{1}{y}{\uparr 0.28} & \multicolumn{1}{y}{\uparr 0.25} & \multicolumn{1}{y|}{\uparr 0.25} & \multicolumn{1}{z}{\dwnarr 0.65} & \multicolumn{1}{z}{\dwnarr 0.62} & \multicolumn{1}{z|}{\dwnarr 0.76} & \multicolumn{1}{y}{\uparr 0.29} & \multicolumn{1}{y}{\uparr 0.16} & \multicolumn{1}{y}{\uparr 0.03} \\ +\multicolumn{1}{l|}{TwHIN-BERT$_\text{Large}$} & 64.42 & 64.46 & 61.28 & 87.82 & 87.28 & 65.68 & 77.17 & 76.94 & 76.94 & 89.49 & 89.43 & 76.35 & 91.74 & 91.64 & 86.67 \\ +$\Delta$ & \multicolumn{1}{y}{\uparr 0.21} & \multicolumn{1}{y}{\uparr 0.17} & \multicolumn{1}{y|}{\uparr 0.16} & \multicolumn{1}{y}{\uparr 0.59} & \multicolumn{1}{y}{\uparr 0.50} & \multicolumn{1}{y|}{\uparr 0.45} & \multicolumn{1}{y}{\uparr 0.25} & \multicolumn{1}{y}{\uparr 0.11} & \multicolumn{1}{y|}{\uparr 0.11} & \multicolumn{1}{z}{\dwnarr 0.98} & \multicolumn{1}{z}{\dwnarr 0.99} & \multicolumn{1}{z|}{\dwnarr 0.93} & \multicolumn{1}{y}{\uparr 0.29} & \multicolumn{1}{y}{\uparr 0.17} & \multicolumn{1}{y}{\uparr 0.02} \\ +\hdashline +\multicolumn{1}{l|}{ViSoBERT [$\clubsuit$]} & \bfseries 68.25 & \bfseries 68.52 & \bfseries 65.94 & \bfseries 88.53 & \bfseries 88.33 & \bfseries 68.82 & \bfseries 78.01 & \bfseries 77.88 & \bfseries 77.88 & 90.83 & 90.75 & 78.77 & \bfseries 91.89 & \bfseries 91.82 & \bfseries 86.93 \\ +$\Delta$ & \multicolumn{1}{y}{\uparr 0.15} & \multicolumn{1}{y}{\uparr 0.15} & \multicolumn{1}{y|}{\uparr 0.06} & \multicolumn{1}{y}{\uparr 0.02} & \multicolumn{1}{y}{\uparr 0.02} & \multicolumn{1}{y|}{\uparr 0.08} & \multicolumn{1}{y}{\uparr 0.18} & \multicolumn{1}{y}{\uparr 0.13} & \multicolumn{1}{y|}{\uparr 0.13} & \multicolumn{1}{z}{\dwnarr 0.16} & \multicolumn{1}{z}{\dwnarr 0.17} & \multicolumn{1}{z|}{\dwnarr 0.29} & \multicolumn{1}{y}{\uparr 0.27} & \multicolumn{1}{y}{\uparr 0.25} & \multicolumn{1}{y}{\uparr 0.13} \\ +\hline +\multicolumn{1}{l|}{ViSoBERT [$\vardiamondsuit$]} & 68.10 & 68.37 & 65.88 & 88.51 & 88.31 & 68.74 & 77.83 & 77.75 & 77.75 & \bfseries 90.99 & \bfseries 90.92 & \bfseries 79.06 & 91.62 & 91.57 & 86.80 \\ +\hline +\end{tabular}} +\caption{Performances of the pre-trained language models on downstream Vietnamese social media tasks by applying word standardizing pre-processing techniques. $[\clubsuit] \text{ and } [\vardiamondsuit]$ denoted with and without standardizing word technique, respectively. $\Delta$ denoted the increase ($\uparrow$) and the decrease ($\downarrow$) in performances of the pre-trained language models compared to its competitors without normalizing teencodes.} +\label{tab:teencode} +\end{table*} + +To emphasize the essentials of diacritics, we conducted an analysis on several data samples by removing 100\ + +\begin{table*}[!ht] +\centering +\resizebox{\textwidth}{!}{ +\begin{tabular}{c|zzz|zzz|zzz|zzz|S[table-column-width=1.1cm,table-format=2.2,table-space-text-pre=\dwnarr,color=red]S[table-column-width=1.8cm,table-format=2.2,table-space-text-pre=\dwnarr,color=red]S[table-column-width=1.1cm,table-format=2.2,table-space-text-pre=\dwnarr,color=red]} +\hline +\multirow{2}{*}{**Model**} & \multicolumn{3}{c|}{**Emotion Regconition**} & \multicolumn{3}{c|}{**Hate Speech Detection**} & \multicolumn{3}{c|}{**Sentiment Analysis**} & \multicolumn{3}{c|}{**Spam Reviews Detection**} & \multicolumn{3}{c}{**Hate Speech Spans Detection**} \\ +\cline{2-16} + & **Acc** & **WF1** & **MF1** & **Acc** & **WF1** & **MF1** & **Acc** & **WF1** & **MF1** & **Acc** & **WF1** & **MF1** & **Acc** & **WF1** & **MF1** \\ +\hline +\multicolumn{16}{c}{***Removing 100\ +\hline +\multicolumn{1**{l|*{PhoBERT$_\text{Large}$} & \multicolumn{1}{w}{49.35} & \multicolumn{1}{w}{49.18} & \multicolumn{1}{w|}{43.95} & \multicolumn{1}{w}{81.25} & \multicolumn{1}{w}{81.42} & \multicolumn{1}{w|}{55.43} & \multicolumn{1}{w}{62.38} & \multicolumn{1}{w}{62.36} & \multicolumn{1}{w|}{62.36} & \multicolumn{1}{w}{87.68} & \multicolumn{1}{w}{87.56} & \multicolumn{1}{w|}{71.89} & \multicolumn{1}{w}{91.32} & \multicolumn{1}{w}{91.37} & \multicolumn{1}{w}{86.43} \\ +$\Delta$ & \dwnarr 15.36 & \dwnarr 15.48 & \dwnarr 18.60 & \dwnarr \color{red} 6.07 & \dwnarr 5.56 & \dwnarr 9.71 & \dwnarr 14.14 & \dwnarr 14.00 & \dwnarr 13.86 & \dwnarr 2.44 & \dwnarr 2.47 & \dwnarr 4.99 & \dwnarr 0.12 & \dwnarr 0.09 & \dwnarr 0.13 \\ +\multicolumn{1}{l|}{TwHIN-BERT$_\text{Large}$} & \multicolumn{1}{w}{49.32} & \multicolumn{1}{w}{49.15} & \multicolumn{1}{w|}{43.52} & \multicolumn{1}{w}{84.25} & \multicolumn{1}{w}{78.48} & \multicolumn{1}{w|}{51.32} & \multicolumn{1}{w}{66.66} & \multicolumn{1}{w}{66.68} & \multicolumn{1}{w|}{66.68} & \multicolumn{1}{w}{89.45} & \multicolumn{1}{w}{89.26} & \multicolumn{1}{w|}{74.59} & \multicolumn{1}{w}{91.12} & \multicolumn{1}{w}{91.22} & \multicolumn{1}{w}{86.33} \\ +$\Delta$ & \dwnarr 14.89 & \dwnarr 15.14 & \dwnarr 17.60 & \dwnarr 2.98 & \dwnarr 8.30 & \dwnarr 12.99 & \dwnarr 10.26 & \dwnarr 10.15 & \dwnarr 10.15 & \dwnarr 1.02 & \dwnarr 1.16 & \dwnarr 2.69 & \dwnarr 0.33 & \dwnarr 0.25 & \dwnarr 0.32 \\ \hdashline +\multicolumn{1}{l|}{ViSoBERT $[\clubsuit]$} & \multicolumn{1}{w}{61.96} & \multicolumn{1}{w}{62.05} & \multicolumn{1}{w|}{58.48} & \multicolumn{1}{w}{87.29} & \multicolumn{1}{w}{86.76} & \multicolumn{1}{w|}{64.87} & \multicolumn{1}{w}{72.95} & \multicolumn{1}{w}{72.91} & \multicolumn{1}{w|}{72.91} & \multicolumn{1}{w}{89.75} & \multicolumn{1}{w}{89.72} & \multicolumn{1}{w|}{76.12} & \multicolumn{1}{w}{91.48} & \multicolumn{1}{w}{91.42} & \multicolumn{1}{w}{86.69} \\ +$\Delta$ & \dwnarr 6.14 & \dwnarr 6.32 & \dwnarr 7.40 & \dwnarr 1.22 & \dwnarr 1.55 & \dwnarr 3.90 & \dwnarr 4.88 & \dwnarr 4.84 & \dwnarr 4.84 & \dwnarr 1.24 & \dwnarr 1.20 & \dwnarr 2.94 & \dwnarr 0.14 & \dwnarr 0.15 & \dwnarr 0.11 \\ +\hline +\multicolumn{16}{c}{***Removing 75\ +\hline +\multicolumn{1**{l|*{PhoBERT$_\text{Large}$} & \multicolumn{1}{w}{51.94} & \multicolumn{1}{w}{51.79} & \multicolumn{1}{w|}{47.79} & \multicolumn{1}{w}{84.74} & \multicolumn{1}{w}{84.03} & \multicolumn{1}{w|}{58.37} & \multicolumn{1}{w}{66.00} & \multicolumn{1}{w}{65.98} & \multicolumn{1}{w|}{65.98} & \multicolumn{1}{w}{88.23} & \multicolumn{1}{w}{88.12} & \multicolumn{1}{w|}{72.42} & \multicolumn{1}{w}{90.38} & \multicolumn{1}{w}{90.23} & \multicolumn{1}{w}{85.42} \\ +$\Delta$ & \dwnarr 12.77 & \dwnarr 12.87 & \dwnarr 14.76 & \dwnarr 2.58 & \dwnarr 2.95 & \dwnarr 6.77 & \dwnarr 10.52 & \dwnarr 10.38 & \dwnarr 10.24 & \dwnarr 1.89 & \dwnarr 1.91 & \dwnarr 4.46 & \dwnarr 1.06 & \dwnarr 1.23 & \dwnarr 1.14 \\ +\multicolumn{1}{l|}{TwHIN-BERT$_\text{Large}$} & \multicolumn{1}{w}{51.32} & \multicolumn{1}{w}{51.17} & \multicolumn{1}{w|}{44.63} & \multicolumn{1}{w}{83.22} & \multicolumn{1}{w}{81.42} & \multicolumn{1}{w|}{52.24} & \multicolumn{1}{w}{67.23} & \multicolumn{1}{w}{67.32} & \multicolumn{1}{w|}{67.32} & \multicolumn{1}{w}{89.12} & \multicolumn{1}{w}{88.95} & \multicolumn{1}{w|}{75.20} & \multicolumn{1}{w}{90.62} & \multicolumn{1}{w}{89.93} & \multicolumn{1}{w}{85.81} \\ +$\Delta$ & \dwnarr 12.89 & \dwnarr 13.12 & \dwnarr 16.49 & \dwnarr 4.01 & \dwnarr 5.36 & \dwnarr 12.99 & \dwnarr 9.69 & \dwnarr 9.51 & \dwnarr 9.51 & \dwnarr 1.35 & \dwnarr 1.47 & \dwnarr 2.08 & \dwnarr 0.83 & \dwnarr 1.54 & \dwnarr 0.84 \\ +\hdashline +\multicolumn{1}{l|}{ViSoBERT $[\spadesuit]$} & \multicolumn{1}{w}{62.34} & \multicolumn{1}{w}{62.26} & \multicolumn{1}{w|}{58.13} & \multicolumn{1}{w}{87.35} & \multicolumn{1}{w}{86.88} & \multicolumn{1}{w|}{65.12} & \multicolumn{1}{w}{73.90} & \multicolumn{1}{w}{73.97} & \multicolumn{1}{w|}{73.97} & \multicolumn{1}{w}{90.41} & \multicolumn{1}{w}{90.31} & \multicolumn{1}{w|}{76.17} & \multicolumn{1}{w}{91.02} & \multicolumn{1}{w}{91.17} & \multicolumn{1}{w}{86.02} \\ +$\Delta$ & \dwnarr 5.76 & \dwnarr 6.11 & \dwnarr 7.75 & \dwnarr 1.16 & \dwnarr 1.43 & \dwnarr 3.65 & \dwnarr 3.93 & \dwnarr 3.78 & \dwnarr 3.78 & \dwnarr 0.58 & \dwnarr 0.61 & \dwnarr 2.89 & \dwnarr 0.60 & \dwnarr 0.40 & \dwnarr 0.78 \\ +\hline +\multicolumn{16}{c}{***Removing 50\ +\hline +\multicolumn{1**{l|*{PhoBERT$_\text{Large}$} & \multicolumn{1}{w}{57.28} & \multicolumn{1}{w}{57.36} & \multicolumn{1}{w|}{54.02} & \multicolumn{1}{w}{85.29} & \multicolumn{1}{w}{84.71} & \multicolumn{1}{w|}{59.40} & \multicolumn{1}{w}{66.57} & \multicolumn{1}{w}{66.46} & \multicolumn{1}{w|}{66.46} & \multicolumn{1}{w}{89.02} & \multicolumn{1}{w}{88.81} & \multicolumn{1}{w|}{73.10} & \multicolumn{1}{w}{90.42} & \multicolumn{1}{w}{90.47} & \multicolumn{1}{w}{85.62} \\ +$\Delta$ & \dwnarr 7.43 & \dwnarr 7.30 & \dwnarr 8.53 & \dwnarr 2.03 & \dwnarr 2.27 & \dwnarr 5.74 & \dwnarr 9.95 & \dwnarr 9.90 & \dwnarr 9.76 & \dwnarr 1.10 & \dwnarr 1.22 & \dwnarr 3.78 & \dwnarr 1.02 & \dwnarr 0.99 & \dwnarr 0.94 \\ +\multicolumn{1}{l|}{TwHIN-BERT$_\text{Large}$} & \multicolumn{1}{w}{53.70} & \multicolumn{1}{w}{53.39} & \multicolumn{1}{w|}{49.55} & \multicolumn{1}{w}{83.41} & \multicolumn{1}{w}{83.31} & \multicolumn{1}{w|}{55.22} & \multicolumn{1}{w}{70.42} & \multicolumn{1}{w}{70.53} & \multicolumn{1}{w|}{70.53} & \multicolumn{1}{w}{89.33} & \multicolumn{1}{w}{89.05} & \multicolumn{1}{w|}{75.32} & \multicolumn{1}{w}{90.73} & \multicolumn{1}{w}{90.12} & \multicolumn{1}{w}{85.92} \\ +$\Delta$ & \dwnarr 10.51 & \dwnarr 10.90 & \dwnarr 11.57 & \dwnarr 3.82 & \dwnarr 3.47 & \dwnarr 10.01 & \dwnarr 6.50 & \dwnarr 6.30 & \dwnarr 6.30 & \dwnarr 1.14 & \dwnarr 1.37 & \dwnarr 1.96 & \dwnarr 0.72 & \dwnarr 1.35 & \dwnarr 0.73 \\ +\hdashline +\multicolumn{1}{l|}{ViSoBERT $[\varheartsuit]$} & \multicolumn{1}{w}{62.96} & \multicolumn{1}{w}{62.87} & \multicolumn{1}{w|}{60.55} & \multicolumn{1}{w}{87.44} & \multicolumn{1}{w}{87.10} & \multicolumn{1}{w|}{65.25} & \multicolumn{1}{w}{74.76} & \multicolumn{1}{w}{74.72} & \multicolumn{1}{w|}{74.72} & \multicolumn{1}{w}{90.41} & \multicolumn{1}{w}{90.35} & \multicolumn{1}{w|}{77.31} & \multicolumn{1}{w}{91.12} & \multicolumn{1}{w}{91.24} & \multicolumn{1}{w}{86.22} \\ +$\Delta$ & \dwnarr 5.14 & \dwnarr 5.50 & \dwnarr 5.33 & \dwnarr 1.07 & \dwnarr 1.21 & \dwnarr 3.52 & \dwnarr 3.07 & \dwnarr 3.03 & \dwnarr 3.03 & \dwnarr 0.58 & \dwnarr 0.57 & \dwnarr 1.75 & \dwnarr 0.50 & \dwnarr 0.33 & \dwnarr 0.58 \\ +\hline +\multicolumn{16}{c}{***Removing 25\ +\hline +\multicolumn{1**{l|*{PhoBERT$_\text{Large}$} & \multicolumn{1}{w}{61.03} & \multicolumn{1}{w}{60.80} & \multicolumn{1}{w|}{57.87} & \multicolumn{1}{w}{85.97} & \multicolumn{1}{w}{85.51} & \multicolumn{1}{w|}{61.96} & \multicolumn{1}{w}{73.42} & \multicolumn{1}{w}{73.28} & \multicolumn{1}{w|}{73.28} & \multicolumn{1}{w}{89.80} & \multicolumn{1}{w}{89.59} & \multicolumn{1}{w|}{75.53} & \multicolumn{1}{w}{90.63} & \multicolumn{1}{w}{90.69} & \multicolumn{1}{w}{85.76} \\ +$\Delta$ & \dwnarr 3.68 & \dwnarr 3.86 & \dwnarr 4.68 & \dwnarr 1.35 & \dwnarr 1.47 & \dwnarr 3.18 & \dwnarr 3.10 & \dwnarr 3.08 & \dwnarr 2.94 & \dwnarr 0.32 & \dwnarr 0.44 & \dwnarr 1.35 & \dwnarr 0.81 & \dwnarr 0.77 & \dwnarr 0.80 \\ +\multicolumn{1}{l|}{TwHIN-BERT$_\text{Large}$} & \multicolumn{1}{w}{61.18} & \multicolumn{1}{w}{60.98} & \multicolumn{1}{w|}{57.42} & \multicolumn{1}{w}{86.85} & \multicolumn{1}{w}{86.13} & \multicolumn{1}{w|}{63.14} & \multicolumn{1}{w}{73.21} & \multicolumn{1}{w}{73.11} & \multicolumn{1}{w|}{73.11} & \multicolumn{1}{w}{89.91} & \multicolumn{1}{w}{89.43} & \multicolumn{1}{w|}{76.32} & \multicolumn{1}{w}{91.09} & \multicolumn{1}{w}{90.72} & \multicolumn{1}{w}{86.02} \\ +$\Delta$ & \dwnarr 3.03 & \dwnarr 3.31 & \dwnarr 3.70 & \dwnarr 0.38 & \dwnarr 0.65 & \dwnarr 2.09 & \dwnarr 3.71 & \dwnarr 3.72 & \dwnarr 3.72 & \dwnarr 0.56 & \dwnarr 0.99 & \dwnarr 0.96 & \dwnarr 0.36 & \dwnarr 0.75 & \dwnarr 0.63 \\ +\hdashline +\multicolumn{1}{l|}{ViSoBERT $[\maltese]$} & \multicolumn{1}{w}{64.64} & \multicolumn{1}{w}{64.53} & \multicolumn{1}{w|}{61.29} & \multicolumn{1}{w}{87.85} & \multicolumn{1}{w}{87.56} & \multicolumn{1}{w|}{66.54} & \multicolumn{1}{w}{75.42} & \multicolumn{1}{w}{75.44} & \multicolumn{1}{w|}{75.44} & \multicolumn{1}{w}{90.76} & \multicolumn{1}{w}{90.64} & \multicolumn{1}{w|}{78.15} & \multicolumn{1}{w}{91.22} & \multicolumn{1}{w}{91.24} & \multicolumn{1}{w}{86.47} \\ +$\Delta$ & \dwnarr 3.43 & \dwnarr 3.84 & \dwnarr 4.59 & \dwnarr 0.66 & \dwnarr 0.75 & \dwnarr 2.23 & \dwnarr 2.41 & \dwnarr 2.31 & \dwnarr 2.31 & \dwnarr 0.23 & \dwnarr 0.28 & \dwnarr 0.91 & \dwnarr 0.40 & \dwnarr 0.33 & \dwnarr 0.33 \\ +\hline +\multicolumn{1}{l|}{ViSoBERT $[\vardiamondsuit]$} & \multicolumn{1}{w}{\bfseries 68.10} & \multicolumn{1}{w}{\bfseries 68.37} & \multicolumn{1}{w|}{\bfseries 65.88} & \multicolumn{1}{w}{\bfseries 88.51} & \multicolumn{1}{w}{\bfseries 88.31} & \multicolumn{1}{w|}{\bfseries 68.77} & \multicolumn{1}{w}{\bfseries 77.83} & \multicolumn{1}{w}{\bfseries 77.75} & \multicolumn{1}{w|}{\bfseries 77.75} & \multicolumn{1}{w}{\bfseries 90.99} & \multicolumn{1}{w}{\bfseries 90.92} & \multicolumn{1}{w|}{\bfseries 79.06} & \multicolumn{1}{w}{\bfseries 91.62} & \multicolumn{1}{w}{\bfseries 91.57} & \multicolumn{1}{w}{\bfseries 86.80} \\ +\hline +\end{tabular}} +\caption{Performances of the pre-trained language models on downstream Vietnamese social media tasks by removing diacritics in all datasets. $[\clubsuit]$, $[\spadesuit]$, $[\varheartsuit]$, $[\maltese]$ and $[\vardiamondsuit]$ denoted the performances of our pre-trained on removing 100\ +\label{tab:diacritics} +\end{table*} + +\newpage +# PLM-based Features for BiLSTM and BiGRU +\label{plm-based} + +We conduct experiments with various models, including BiLSTM and BiGRU, to understand better the word embedding feature extracted from the pre-trained language models. Table \ref{tab:feature-based} shows performances of the pre-trained language model as input features to BiLSTM and BiGRU on downstream Vietnamese social media tasks. + +\begin{table*}[!ht] +\centering +\resizebox{\textwidth}{!}{ +\begin{tabular}{l|ccc|ccc|ccc|>{\centering\arraybackslash}m{1.1cm}>{\centering\arraybackslash}m{1.1cm}>{\centering\arraybackslash}m{1.1cm}|>{\centering\arraybackslash}m{1.1cm}>{\centering\arraybackslash}m{1.8cm}>{\centering\arraybackslash}m{1.1cm}} +\hline +\multicolumn{1}{c|}{\multirow{2}{*}{**Model**}} & \multicolumn{3}{c|}{**Emotion Regconition**} & \multicolumn{3}{c|}{**Hate Speech Detection**} & \multicolumn{3}{c|}{**Sentiment Analysis**} & \multicolumn{3}{c|}{**Spam Reviews Detection**} & \multicolumn{3}{c}{**Hate Speech Spans Detection**} \\ +\cline{2-16} +\multicolumn{1}{c|}{} & **Acc** & **WF1** & **MF1** & **Acc** & **WF1** & **MF1** & **Acc** & **WF1** & **MF1** & **Acc** & **WF1** & **MF1** & **Acc** & **WF1** & **MF1** \\ +\hline +\multicolumn{16}{c}{***BiLSTM***} \\ +\hline +PhoBERT$_\text{Large}$ & 57.58 & 56.65 & 50.55 & 86.11 & 84.04 & 56.03 & 69.71 & 69.70 & 69.70 & 87.80 & 87.10 & 68.95 & 84.01 & 80.70 & 74.35 \\ +TwHIN-BERT$_\text{Large}$ & 61.47 & 61.31 & 56.73 & 83.14 & 82.72 & 55.84 & 64.76 & 64.82 & 64.82 & 88.73 & 88.23 & 72.18 & 85.92 & 84.43 & 78.28 \\ +\hdashline +ViSoBERT & **63.06** & **62.36** & **59.16** & **87.62** & **86.81** & **64.82** & **73.52** & **73.50** & **73.50** & **90.11** & **89.79** & **75.71** & **88.37** & **87.87** & **82.18** \\ +\hline +\multicolumn{16}{c}{***BiGRU***} \\ +\hline +PhoBERT$_\text{Large}$ & 55.12 & 54.53 & 49.59 & 85.21 & 83.23 & 54.59 & 70.01 & 70.01 & 70.01 & 86.06 & 84.89 & 62.54 & 84.23 & 81.01 & 74.57 \\ +TwHIN-BERT$_\text{Large}$ & 60.46 & 60.30 & 55.23 & 85.73 & 83.45 & 54.74 & 63.11 & 61.39 & 61.39 & 87.67 & 86.38 & 66.83 & 86.10 & 84.52 & 78.49 \\ +\hdashline +ViSoBERT & **63.20** & **63.25** & **60.73** & **87.02** & **86.25** & **63.36** & **70.48** & **70.53** & **70.53** & **89.33** & **88.98** & **76.57** & **88.88** & **88.19** & **82.63** \\ +\hline +\end{tabular}} +\caption{Performances of the pre-trained language models as input features to BiLSTM and BiGRU on downstream Vietnamese social media tasks.} +\label{tab:feature-based} +\end{table*} + +We implemented various PLMs when used as input features in combination with BiLSTM and BiGRU models to verify the ability to extract Vietnamese social media texts. The evaluation is conducted on the dev set, and the performance is measured per epoch for downstream tasks. Table~\ref{tab:feature-based} shows performances of the PLMs as input features to BiLSTM and BiGRU on the dev set per epoch. +\begin{figure}[!ht] + \centering + \begin{subfigure}[a]{0.323\textwidth} + \centering + \includegraphics[width=\textwidth]{vsmec.pdf} + \caption{Emotion Regconition} + \label{fig:ER} + \end{subfigure} + \hfill + \begin{subfigure}[a]{0.323\textwidth} + \centering + \includegraphics[width=\textwidth]{vihsd.pdf} + \caption{Hate Speech Detection} + \label{fig:HSD} + \end{subfigure} + \hfill + \begin{subfigure}[a]{0.323\textwidth} + \centering + \includegraphics[width=\textwidth]{vlsp.pdf} + \caption{Sentiment Analysis} + \label{fig:SA} + \end{subfigure} + \hfill + \begin{subfigure}[a]{0.323\textwidth} + \centering + \includegraphics[width=\textwidth]{vispam.pdf} + \caption{Spam Reviews Detection} + \label{fig:SRD} + \end{subfigure} + + \begin{subfigure}[a]{0.323\textwidth} + \centering + \includegraphics[width=\textwidth]{vihos.pdf} + \caption{Hate Speech Spans Detection} + \label{fig:HSSD} + \end{subfigure} + \begin{subfigure}[c]{0.323\textwidth} + \vspace*{-25pt} + \hspace*{40pt}\includegraphics[scale=0.430]{legend.pdf} + \end{subfigure} + +\caption{Performances of the PLMs as input features to BiLSTM and BiGRU on the dev set per epoch on Vietnamese social media downstream tasks. *Large* versions of PhoBERT and TwHIN-BERT are implemented for these experiments.} +\label{fig:perepoch} +\end{figure} + +\newpage + +# Updating New Spans of Hate Speech Span Detection Samples with Pre-processing Techniques +\label{app:updatingViHOS} + +Due to performing pre-processing techniques, the span positions on the data samples can be changed. Therefore, we present Algorithm \ref{agrt:updatenewspans}, which shows how to update new span positions of samples applied with pre-processing techniques in the Hate Speech Spans Detection task (UIT-ViHOS dataset). This algorithm takes as input a comment and its spans and returns the pre-processed comment and its span along with pre-processing techniques. + +\begin{algorithm}[H] +\begin{algorithmic}[1] + +\Procedure{Algorithm}{$\text{comment}$, $\text{label}$, $\text{delete}$}\caption{Updating new spans of samples applied with pre-processing techniques in Hate Speech Spans Detection task (UIT-ViHOS dataset).}\label{agrt:updatenewspans} + \State **assert** $\text{len(comment)}$ == $\text{len(label)}$ + \State $\text{new\_comment} \gets []$, $\text{new\_label} \gets []$ + + \For{$i \gets 0$ **to** $\text{len(comment)}$} + \State $\text{check} \gets 0$ + \If{$\text{comment}[i]$ **in** $\text{emoji\_to\_word.keys()}$} + \If{$\text{delete}$} + + \State **continue** + \EndIf + \For{$j \gets 0$ **to** $\text{len(emoji\_to\_word[comment[i]].split(` '))}$} + \If{$\text{label}[i]$ == `B-T'} + \If{$\text{check}$ == $0$} + \State $\text{check} \gets \text{check} + 1$, $\text{new\_label.append(label[i])}$ + + \Else + \State $\text{new\_label.append(`I-T')}$ + \EndIf + \Else + \State $\text{new\_label.append(label[i])}$ + \EndIf + \State $\text{new\_comment.append(emoji\_to\_word[comment[i]].split(` ')[j])}$ + \EndFor + \Else + \State $\text{new\_sentence.append(comment[i])}$ + \State $\text{new\_label.append(label[i])}$ + \EndIf + \State **assert** $\text{len(new\_comment)}$ == $\text{len(new\_label)}$ + \EndFor + \State **return** \text{new\_comment}, \text{new\_label} +\EndProcedure + +\end{algorithmic} +\end{algorithm} + +# Tokenizations of the PLMs on Removing Diacritics Social Comments\label{removingdiacritics} +We analyze several data samples to see the tokenized ability of Vietnamese social media textual data while removing diacritics on comments. Table~\ref{tab:diacriticscomments} shows several non-diacritics Vietnamese social comments and their tokenizations with the tokenizers of the three best pre-trained language models, ViSoBERT (ours), PhoBERT, and TwHIN-BERT. + +\begin{table}[!ht] +\resizebox{\textwidth}{!}{ +\begin{tabular}{lll} +\hline +\multicolumn{1}{l|}{**Model**} & + \multicolumn{1}{c|}{**Example 1**} & + \multicolumn{1}{c}{**Example 2**} \\ \hline +\multicolumn{1}{l|}{Raw comment} & \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}cái con đồ chơi đó mua ở đâu nhỉ . cười đéo nhặt được \\mồm \includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png} +\\*English*: where did you buy that toy . LMAO \includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\end{tabular}} & \begin{tabular}[c]{@{}l@{}}Ôi bố cái lũ thanh niên hãm lol. Đẹp mặt quá \includegraphics[width=12pt]{unamused-face_1f612.png}\includegraphics[width=12pt]{unamused-face_1f612.png}\\*English*:~Oh my god damn teenagers, lol. So deserved \includegraphics[width=12pt]{unamused-face_1f612.png}\includegraphics[width=12pt]{unamused-face_1f612.png}\end{tabular} \\ +\hline +\multicolumn{3}{c}{***Removing 100\ +\multicolumn{1**{l|*{Comment} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}cai con do choi do mua o dau nhi . cuoi deo nhat duoc \\mom . \includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\end{tabular}} & + Oi bo cai lu thanh nien ham lol. Dep mat qua \includegraphics[width=12pt]{unamused-face_1f612.png}\includegraphics[width=12pt]{unamused-face_1f612.png} \\ \hline +\multicolumn{1}{l|}{PhoBERT} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "c a i", "c o n", "d o", "c h o @ @", "i", "d o", "m u a", \\ "o", "d @ @", "a u", "n h i", ".", "c u @ @", "o i", \\"d @ @", "e o", "n h @ @", "a t", "d u @ @", "o c", \\"m o m", ".", \textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \textless{}/s\textgreater{}\end{tabular}} & + \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "O @ @", "i", "b o", "c a i", "l u", "t h a n h", "n i @ @",\\"e n", "h a m", "l o @ @", "l", ".", "D e @ @", "p", "m a t",\\"q u a", \textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \textless{}/s\textgreater{}\end{tabular} \\ \hline +\multicolumn{1}{l|}{TwHIN-BERT} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "cai", "con", "do", "cho", "i", "do", "mua", "o", "dau", \\"nhi", "", ".", "cu", "oi", "de", "o", "nha", "t", "du", "oc", \\"mom", "", ".", "", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", \textless{}/s\textgreater{}\end{tabular}} & + \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "Oi", "bo", "cai", "lu", "thanh", "nie", "n", "ham", "lol", \\".", "De", "p", "mat", "qua", "", "\includegraphics[width=12pt]{unamused-face_1f612.png}", "\includegraphics[width=12pt]{unamused-face_1f612.png}", \textless{}/s\textgreater{}\end{tabular} \\ \hline +\multicolumn{1}{l|}{ViSoBERT} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "cai", "con", "do", "choi", "do", "mua", "o", "dau", "nhi", \\".","cu", "oi", "d", "eo", "nhat", "duoc", "m", "om", ".", \\"\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", \textless{}/s\textgreater{}\end{tabular}} & + \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "O", "i", "bo", "cai", "lu", "thanh", "ni", "en", "h", "am", \\"lol", ".", "D", "ep", "mat", "qua", "\includegraphics[width=12pt]{unamused-face_1f612.png}\includegraphics[width=12pt]{unamused-face_1f612.png}", \textless{}/s\textgreater{}\end{tabular} \\ \hline +\multicolumn{3}{c}{***Removing 75\ +\multicolumn{1**{l|*{Comment} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}cai con do chơi do mua o đâu nhi . cười deo nhat duoc \\mom . \includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\end{tabular}} & + Ôi bo cai lu thanh niên hãm lol. Dep mat qua \includegraphics[width=12pt]{unamused-face_1f612.png}\includegraphics[width=12pt]{unamused-face_1f612.png} \\ \hline +\multicolumn{1}{l|}{PhoBERT} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "c a i", "c o n", "d o", "c h ơ i", "d o", "m u a", "o", \\"đ â u", "n h i", ".", "c ư ờ i", "d @ @", "e o", "n h @ @", \\"a t", "d u @ @", "o c", "m o m", ".",\textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \\\textless{}unk\textgreater{}, \textless{}/s\textgreater{}\end{tabular}} & + \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "Ô i", "b o", "c a i", "l u", "t h a n h \_ n i ê n", "h ã m", \\"l o @ @", "l", ".", "D e @ @", "p", "m a t", "q u a", \textless{}unk\textgreater{}, \\\textless{}unk\textgreater{}, \textless{}/s\textgreater{}\end{tabular} \\ \hline +\multicolumn{1}{l|}{TwHIN-BERT} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "cai", "con", "do", "chơi", "do", "mua", "o", "đâu", \\"nhi", "", ".", "cười", "de", "o", "nha", "t", "du", "oc", \\"mom", "", ".", "", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", \textless{}/s\textgreater{}\end{tabular}} & + \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "Ô", "i", "bo", "cai", "lu", "thanh", "niên", "", "hã", "m", \\"lol", ".", "De", "p", "mat", "qua", "", "\includegraphics[width=12pt]{unamused-face_1f612.png}", "\includegraphics[width=12pt]{unamused-face_1f612.png}", \textless{}/s\textgreater{}\end{tabular} \\ \hline +\multicolumn{1}{l|}{ViSoBERT} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "cai", "con", "do", "chơi", "do", "mua", "o", "đâu", \\"nhi", ".", "cười", "d", "eo", "nhat", "duoc", "m", "om", \\".", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", \textless{}/s\textgreater{}\end{tabular}} & + \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "Ôi", "bo", "cai", "lu", "thanh", "n", "iên", "hã", "m", \\"lol", ".", "D", "ep", "mat", "qua", "\includegraphics[width=12pt]{unamused-face_1f612.png}\includegraphics[width=12pt]{unamused-face_1f612.png}", \textless{}/s\textgreater{}\end{tabular} \\ \hline +\multicolumn{3}{c}{***Removing 50\ +\multicolumn{1**{l|*{Comment} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}cai con do chơi do mua o đâu nhỉ . cười đéo nhặt duoc \\mom . \includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\end{tabular}} & + Ôi bo cai lu thanh niên hãm lol. Dep mặt quá \includegraphics[width=12pt]{unamused-face_1f612.png}\includegraphics[width=12pt]{unamused-face_1f612.png} \\ \hline +\multicolumn{1}{l|}{PhoBERT} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "c a i", "c o n", "d o", "c h ơ i", "d o", "m u a", "o", \\"đ â u", "n h ỉ", ".", "c ư ờ i", "đ @ @", "é o", "n h ặ t", \\"d u @ @", "o c", "m o m", ".", \textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \textless{}/s\textgreater{}\end{tabular}} & + \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "Ô i", "b o", "c a i", "l u", "t h a n h \_ n i ê n", "h ã m", \\"l o @ @", "l", ".", "D e @ @", "p", "m ặ t", "q u á", \textless{}unk\textgreater{}, \\\textless{}unk\textgreater{}, \textless{}/s\textgreater{}\end{tabular} \\ \hline +\multicolumn{1}{l|}{TwHIN-BERT} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "cai", "con", "do", "chơi", "do", "mua", "o", "đâu", "nhỉ", \\"", ".", "cười", "đ", "é", "o", "nh", "ặt", "du", "oc", "mom", \\"", ".", "", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", \textless{}/s\textgreater{}\end{tabular}} & + \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "Ô", "i", "bo", "cai", "lu", "thanh", "niên", "", "hã", "m", \\"lol", ".", "De", "p", "mặt", "quá", "", "\includegraphics[width=12pt]{unamused-face_1f612.png}", "\includegraphics[width=12pt]{unamused-face_1f612.png}", \textless{}/s\textgreater{}\end{tabular} \\ \hline +\multicolumn{1}{l|}{ViSoBERT} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "cai", "con", "do", "chơi", "do", "mua", "o", "đâu", \\"nhỉ", ".", "cười", "đéo", "nh", "ặt", "duoc", "m", "om", \\".", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", \textless{}/s\textgreater{}\end{tabular}} & + \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "Ôi", "bo", "cai", "lu", "thanh", "n", "iên", "hã", "m", \\"lol", ".", "D", "ep", "mặt", "quá", "\includegraphics[width=12pt]{unamused-face_1f612.png}\includegraphics[width=12pt]{unamused-face_1f612.png}", \textless{}/s\textgreater{}\end{tabular} \\ \hline +\multicolumn{3}{c}{***Removing 25\ +\multicolumn{1**{l|*{Comment} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}cai con do chơi đó mua ở đâu nhỉ . cười đéo nhặt duoc \\mồm . \includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\end{tabular}} & + Ôi bo cai lu thanh niên hãm lol. Đep mặt quá \includegraphics[width=12pt]{unamused-face_1f612.png}\includegraphics[width=12pt]{unamused-face_1f612.png} \\ \hline +\multicolumn{1}{l|}{PhoBERT} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "c a i", "c o n", "d o", "c h ơ i", "đ ó", "m u a", "ở", \\"đ â u", "n h ỉ", ".", "c ư ờ i", "đ @ @", "é o", "n h ặ t", \\"d u @ @", "o c", "m ồ m", ".", \textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \textless{}/s\textgreater{}\end{tabular}} & + \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "Ô i", "b o", "c a i", "l u", "t h a n h \_ n i ê n", "h ã m", \\"l o @ @", "l", ".", "Đ e p \_ @ @", "m ặ t", "q u á", \textless{}unk\textgreater{}, \\\textless{}unk\textgreater{}, \textless{}/s\textgreater{}\end{tabular} \\ \hline +\multicolumn{1}{l|}{TwHIN-BERT} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "cai", "con", "do", "chơi", "do", "mua", "o", "đâu", \\"nhỉ", "", ".", "cười", "đ", "é", "o", "nh", "ặt", "du", "oc", \\"mom", "", ".", "", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", \textless{}/s\textgreater{}\end{tabular}} & + \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "Ô", "i", "bo", "cai", "lu", "thanh", "niên", "", "hã", "m", \\"lol", ".", "Đep", "mặt", "quá", "", "\includegraphics[width=12pt]{unamused-face_1f612.png}", "\includegraphics[width=12pt]{unamused-face_1f612.png}", \textless{}/s\textgreater{}\end{tabular} \\ \hline +\multicolumn{1}{l|}{ViSoBERT} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "cai", "con", "do", "chơi", "đó", "mua", "ở", "đâu", \\"nhỉ", ".", "cười", "đéo", "nh", "ặt", "duoc", "mồm", ".", \\"\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", \textless{}/s\textgreater{}\end{tabular}} & + \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "Ôi", "bo", "cai", "lu", "thanh", "n", "iên", "hã", "m", \\"lol", ".", "Đep", "mặt", "quá", "\includegraphics[width=12pt]{unamused-face_1f612.png}\includegraphics[width=12pt]{unamused-face_1f612.png}", \textless{}/s\textgreater{}\end{tabular} \\ \hline +\end{tabular} +} +\caption{Actual social comments and their tokenizations with the tokenizers of the three pre-trained language models, including PhoBERT, TwHIN-BERT, and ViSoBERT, on removing diacritics of social comments.} +\label{tab:diacriticscomments} +\end{table} \ No newline at end of file diff --git a/references/2023.arxiv.nguyen/paper.pdf b/references/2023.arxiv.nguyen/paper.pdf new file mode 100644 index 0000000000000000000000000000000000000000..c14c51d1787dafe89966fd2a784736f222a762b0 --- /dev/null +++ b/references/2023.arxiv.nguyen/paper.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f6f474098fbd4c0ab37d6ae5b38ee708baddb687538f45c579b37c5976162181 +size 616958 diff --git a/references/2023.arxiv.nguyen/paper.tex b/references/2023.arxiv.nguyen/paper.tex new file mode 100644 index 0000000000000000000000000000000000000000..2a6ed5da6e6f4f5a9cc6c141826a4e3a2f4c6ea1 --- /dev/null +++ b/references/2023.arxiv.nguyen/paper.tex @@ -0,0 +1,984 @@ + % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended. +\pdfoutput=1 +% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines. + +\documentclass[11pt]{article} + +% Remove the "review" option to generate the final version. +\usepackage[final]{EMNLP2023} +% \usepackage[]{EMNLP2023} + +% Standard package includes +\usepackage{times} +\usepackage{latexsym} + +% For proper rendering and hyphenation of words containing Latin characters (including in bib files) +\usepackage[T1]{fontenc} +% For Vietnamese characters +\usepackage[T5]{fontenc} +% See https://www.latex-project.org/help/documentation/encguide.pdf for other character sets + +% This assumes your files are encoded as UTF8 +\usepackage[utf8]{inputenc} + +% This is not strictly necessary and may be commented out. +% However, it will improve the layout of the manuscript, +% and will typically save some space. +\usepackage{microtype} + +% This is also not strictly necessary and may be commented out. +% However, it will improve the aesthetics of text in +% the typewriter font. +\usepackage{array} +\usepackage{inconsolata} +\usepackage{amsmath} +\usepackage{multirow} +\usepackage{graphicx} +\usepackage{arydshln} +\usepackage{fdsymbol} +\usepackage{ifsym} +\usepackage{tablefootnote} +\usepackage{tikz} +\usepackage{wasysym} +\usepackage{booktabs} +\usepackage{algorithm} +\usepackage[noend]{algpseudocode} +\usepackage{longtable} +\usepackage{subcaption} +\usepackage{etoolbox} +% \usepackage{subfigure} +\usepackage{caption} +\usepackage{siunitx} +\sisetup{table-format=2.2,table-number-alignment=center,table-space-text-pre=\textuparrow,detect-weight=true,mode=text} +\usepackage{fdsymbol} +\newcommand{\dgr}{\raise0.65ex\hbox{\tiny\dagger}} +\newcommand{\ddgr}{\raise0.65ex\hbox{\tiny\ddagger}} +\newcommand{\dwnarr}{\hbox{\textcolor{red}{$\downarrow$}}} +\newcommand{\uparr}{\hbox{\textcolor{blue}{$\uparrow$}}} + + +\newcolumntype{j}{S[table-format=2.2,table-space-text-pre=~,table-space-text-post=\ddgr]} +\newcolumntype{z}{S[table-format=2.2, table-space-text-pre=\dwnarr,color=red]} +\newcolumntype{y}{S[table-format=2.2, table-space-text-pre=\uparr,color=blue]} +\newcolumntype{w}{S[table-format=2.2,table-space-text-pre=\dwnarr,color=black]} + + +%\usepackage{colortbl} +%\usepackage{xfrac} +%\usepackage{tcolorbox} +% \usepackage{hwemoji} +% \usepackage{fontawesome5} +% \setemojifont{Apple Color Emoji} +% \usepackage[perpage]{footmisc} +% \usepackage[symbol]{footmisc} + +\setcounter{footnote}{0} +% \captionsetup[table]{position=bottom} +% \renewcommand{\thefootnote}{\fnsymbol{footnote}} + + +% If the title and author information does not fit in the area allocated, uncomment the following +% +\setlength\titlebox{6.25cm} +% +% and set to something 5cm or larger. + +%\title{: Pre-trained Language Models for Vietnamese Social Media Texts} +\title{ViSoBERT: A Pre-Trained Language Model for Vietnamese Social Media Text Processing} + +% Author information can be set in various styles: +% For several authors from the same institution: +% \author{Author 1 \and ... \and Author n \\ +% Address line \\ ... \\ Address line} +% if the names do not fit well on one line use +% Author 1 \\ {\bf Author 2} \\ ... \\ {\bf Author n} \\ +% For authors from different institutions: +% \author{Author 1 \\ Address line \\ ... \\ Address line +% \And ... \And +% Author n \\ Address line \\ ... \\ Address line} +% To start a separate ``row'' of authors use \AND, as in +% \author{Author 1 \\ Address line \\ ... \\ Address line +% \AND +% Author 2 \\ Address line \\ ... \\ Address line \And +% Author 3 \\ Address line \\ ... \\ Address line} + + + + +\author{Quoc-Nam Nguyen\textsuperscript{1, 3, \protect\hyperlink{equally}{*}}, Thang Chau Phan\textsuperscript{1, 3, \protect\hyperlink{equally}{*}}, Duc-Vu Nguyen\textsuperscript{2, 3}, Kiet Van Nguyen\textsuperscript{1, 3} \\ +\textsuperscript{1}Faculty of Information Science and Engineering, University of Information Technology,\\Ho Chi Minh City, Vietnam\\ +\textsuperscript{2}Multimedia Communications Laboratory, University of Information Technology,\\Ho Chi Minh City, Vietnam\\ +\textsuperscript{3}Vietnam National University, Ho Chi Minh City, Vietnam \\ +\texttt{\{20520644, 20520929\}@gm.uit.edu.vn} \\ +\texttt{\{vund, kietnv\}@uit.edu.vn}} + + + +\begin{document} + +\maketitle +\def\thefootnote{*}\footnotetext{ +\raisebox{\baselineskip}[0pt][0pt]{\hypertarget{equally}{}}Equal contribution.}\def\thefootnote{\arabic{footnote}} +\setcounter{footnote}{3} + + +\begin{abstract} + +%Lát tui viết, giờ bận xem seminar +% da thay +%Bài này thế nào tụi nó cũng mời Mr. Dat Review :D nên để vào abstract cho mát dạ :D +% dạ thầy :v, bài này đấu trực teiesp với phobert luôn :v +%:D cũng nhẹ nhàng mà :D +% dạ bài này mình đánh low resource vs pre-trained là đúng cái contribution năm nay của emnlp luôn thầy :v. Chắc em dạo 1 vòng bài coi thêm được mấy cái ý liên quan đến 2 cái đóđó rồi thethêm zô thầy :v +%OK, mở rộng ra mấy cái pre-train khác nhau, xem cách nó lập luận +% dạ, bài này này còn phần feature nữa là xong bản draft á thầy +% em dang them may cai vi du vao` thay :v + + +% da thay oi, phan algorithm cua minh, minh co can phai elaborate cai algorithm ra khong thay + +English and Chinese, known as resource-rich languages, have witnessed the strong development of transformer-based language models for natural language processing tasks. Although Vietnam has approximately 100M people speaking Vietnamese, several pre-trained models, e.g., PhoBERT, ViBERT, and vELECTRA, performed well on general Vietnamese NLP tasks, including POS tagging and named entity recognition. These pre-trained language models are still limited to Vietnamese social media tasks. In this paper, we present the first monolingual pre-trained language model for Vietnamese social media texts, ViSoBERT, which is pre-trained on a large-scale corpus of high-quality and diverse Vietnamese social media texts using XLM-R architecture. Moreover, we explored our pre-trained model on five important natural language downstream tasks on Vietnamese social media texts: emotion recognition, hate speech detection, sentiment analysis, spam reviews detection, and hate speech spans detection. Our experiments demonstrate that ViSoBERT, with far fewer parameters, surpasses the previous state-of-the-art models on multiple Vietnamese social media tasks. Our ViSoBERT model is available\footnote{\url{https://huggingface.co/uitnlp/visobert}} only for research purposes. + +\textbf{Disclaimer}: This paper contains actual comments on social networks that might be construed as abusive, offensive, or obscene. + +\end{abstract} + + +% \input{emnlp2023-latex/Sections/1-Intro} +% \input{emnlp2023-latex/Sections/2-Related-Work} +% \input{emnlp2023-latex/Sections/3-ViSoBERT} +% \input{emnlp2023-latex/Sections/4-Experimental-results} +% \input{emnlp2023-latex/Sections/5-Discussion} +% \input{emnlp2023-latex/Sections/6-Conclusion} +\section{Introduction} \label{introduction} +Language models based on transformer architecture \cite{attentionisallyouneed} pre-trained on large-scale datasets have brought about a paradigm shift in natural language processing (NLP), reshaping how we analyze, understand, and generate text. In particular, BERT \cite{bert} and its variants \cite{roberta,xlm-r} have achieved state-of-the-art performance on a wide range of downstream NLP tasks, including but not limited to text classification, sentiment analysis, question answering, and machine translation. English is moving for the rapid development of language models across specific domains such as medical \cite{biobert,rasmy2021med}, scientific \cite{beltagy2019scibert}, legal \cite{legalbert}, political conflict and violence \cite{conflibert}, and especially social media \cite{bertweet,bernice,robertuito,twhinbert}. + +%By learning from vast amounts of data, pre-trained language models have acquired a deep understanding of the underlying linguistic structures and patterns, enabling them to generate coherent and meaningful text. + + +% These models are trained on massive amounts of text data and learn to represent the underlying linguistic structures and patterns of the language, enabling them to generate coherent and meaningful text. + +% dạ này để em viết cho thầy :D, em có viết mấy phần ở dưới á thầy, thầy check giúp tụi em với +%Phần introduction rất quan trọng đó, phải thể hiện rõ motivation nha +% Dạ thầy, từ giờ đến chiều chắc sẽ xong ạ + +%There are two primary reasons to experiment with Vietnamese social media texts. Nêu 1 số lý do mình thực hiện cho pre-trained social media texts for Vietnamese + +Vietnamese is the eighth largest language used over the internet, with around 85 million users across the world\footnote{\url{https://www.internetworldstats.com/stats3.htm}}. Despite a large amount of Vietnamese data available over the Internet, the advancement of NLP research in Vietnamese is still slow-moving. This can be attributed to several factors, to name a few: the scattered nature of available datasets, limited documentation, and minimal community engagement. Moreover, most existing pre-trained models for Vietnamese were primarily trained on large-scale corpora sourced from general texts \cite{viBERTandvELECTRA,phobert,viDeBerTa}. While these sources provide broad language coverage, they may not fully represent the sociolinguistic phenomena in Vietnamese social media texts. Social media texts often exhibit different linguistic patterns, informal language usage, non-standard vocabulary, lacking diacritics and emoticons that are not prevalent in formal written texts. The limitations of using language models pre-trained on general corpora become apparent when processing Vietnamese social media texts. The models can struggle to accurately understand and interpret the informal language, using emoji, teencode, and diacritics used in social media discussions. This can lead to suboptimal performance in Vietnamese social media tasks, including emotion recognition, hate speech detection, sentiment analysis, spam reviews detection, and hate speech spans detection. + +We present ViSoBERT, a pre-trained language model designed explicitly for Vietnamese social media texts to address these challenges. ViSoBERT is based on the transformer architecture and trained on a large-scale dataset of Vietnamese posts and comments extracted from well-known social media networks, including Facebook, Tiktok, and Youtube. Our model outperforms existing pre-trained models on various downstream tasks, including emotion recognition, hate speech detection, sentiment analysis, spam reviews detection, and hate speech spans detection, demonstrating its effectiveness in capturing the unique characteristics of Vietnamese social media texts. Our contributions are summarized as follows. +\begin{itemize} + \item We presented ViSoBERT, the first PLM based on the XLM-R architecture and pre-training procedure for Vietnamese social media text processing. ViSoBERT is available publicly for research purposes in Vietnamese social media mining. ViSoBERT can be a strong baseline for Vietnamese social media text processing tasks and their applications. + \item ViSoBERT produces SOTA performances on multiple Vietnamese downstream social media tasks, thus illustrating the effectiveness of our PLM on Vietnamese social media texts. + % : emotion recognition, hate speech detection, sentiment analysis, spam reviews detection, and hate speech span detection, thus illustrating the effectiveness of our model on Vietnamese social media texts. + \item To understand our pre-trained language model deeply, we analyze experimental results on the masking rate, examining social media characteristics, including emojis, teencode, and diacritics, and implementing feature-based extraction for task-specific models. +\end{itemize} + +\section{Fundamental of Pre-trained Language Models for Social Media Texts} \label{fundamental} +Pre-trained Language Models (PLMs) based on transformers \cite{attentionisallyouneed} have become a crucial element in cutting-edge NLP tasks, including text classification and natural language generation. Since then, language models based on transformers related to our study have been reviewed, including PLMs for Vietnamese social media texts. +\subsection{Pre-trained Language Models for Vietnamese} +Several PLMs have recently been developed for processing Vietnamese texts. These models have varied in their architectures, training data, and evaluation metrics. PhoBERT, developed by \citet{phobert}, is the first general pre-trained language model (PLM) created for the Vietnamese language. The model employs the same architecture as BERT \cite{bert} and the same pre-training technique as RoBERTa \cite{roberta} to ensure robust and reliable performance. PhoBERT was trained on a 20GB word-level Vietnamese Wikipedia corpus, which produces SOTA performances on a range of downstream tasks of POS tagging, dependency parsing, NER, and NLI. + +Following the success of PhoBERT, viBERT \cite{viBERTandvELECTRA} and vELECTRA \cite{viBERTandvELECTRA}, both monolingual pre-trained language models based on the BERT and ELECTRA architectures, were introduced. They were trained on substantial datasets, with ViBERT using a 10GB corpus and vELECTRA utilizing an even larger 60GB collection of uncompressed Vietnamese text. viBERT4news\footnote{\url{https://github.com/bino282/bert4news}} was published by NlpHUST, a Vietnamese version of BERT trained on more than 20 GB of news datasets. For Vietnamese text summarization, BARTpho \cite{BARTpho} is presented as the first large-scale monolingual seq2seq models pre-trained for Vietnamese, based on the seq2seq denoising autoencoder BART. Moreover, ViT5 \cite{ViT5} follows the encoder-decoder architecture proposed by \citet{attentionisallyouneed} and the T5 framework proposed by \citet{raffel2020exploring}. Many language models are designed for general use, while the availability of strong baseline models for domain-specific applications remains limited. Since then, \citet{vihealthbert} introduced ViHealthBERT, the first domain-specific PLM for Vietnamese healthcare. + +\subsection{Pre-trained Language Models for Social Media Texts} + +Multiple PLMs were introduced for social media for multilingual and monolinguals. BERTweet \cite{bertweet} was presented as the first public large-scale PLM for English Tweets. BERTweet has the same architecture as BERT$_\textit{Base}$ \cite{bert} and is trained using the RoBERTa pre-training procedure \cite{roberta}. \citet{koto-etal-2021-indobertweet} proposed IndoBERTweet, the first large-scale pre-trained model for Indonesian Twitter. IndoBERTweet is trained by extending a monolingually trained Indonesian BERT model with an additive domain-specific vocabulary. RoBERTuito, presented in \citet{robertuito}, is a robust transformer model trained on 500 million Spanish tweets. RoBERTuito excels in various language contexts, including multilingual and code-switching scenarios, such as Spanish and English. TWilBert \cite{twilbert} is proposed as a specialization of BERT architecture both for the Spanish language and the Twitter domain to address text classification tasks in Spanish Twitter. + +Bernice, introduced by \citet{bernice}, is the first multilingual pre-trained encoder designed exclusively for Twitter data. This model uses a customized tokenizer trained solely on Twitter data and incorporates a larger volume of Twitter data (2.5B tweets) than most BERT-style models. \citet{twhinbert} introduced TwHIN-BERT, a multilingual language model trained on 7 billion Twitter tweets in more than 100 different languages. It is designed to handle short, noisy, user-generated text effectively. Previously, \cite{barbieri-etal-2022-xlm} extended the training of the XLM-R \cite{xlm-r} checkpoint using a data set comprising 198 million multilingual tweets. As a result, XLM-T is adapted to the Twitter domain and was not exclusively trained on data from within that domain. + +% AlBERTo \cite{alberto} + +% TweetBERT \cite{tweetbert} + +% Transformer based contextualization of pre-trained word embeddings for irony detection in Twitter \cite{GONZALEZ2020102262} + +% \subsection{Vietnamese Social Media Texts tasks} +% There has been growing interest in developing NLP models specifically for processing Vietnamese social media texts in recent years. + +% % Constructive Speech Detection Task (UIT-ViCTSD) \cite{victsd} + +% Emotion Recognition Task (UIT-VSMEC) \cite{vsmec} + +% Hate Speech Detection Task (UIT-ViHSD) \cite{vihsd} + +% Complaint Comment Detection Task (UIT-ViOCD) \cite{viocd} + +% Spam Reviews Detection Task (ViSpamReviews) \cite{vispamreviews} + +% Hate Speech Spans Detection Task (UIT-ViHOS) \cite{vihos} + +\section{ViSoBERT} \label{ViSoBERT} +This section presents the architecture, pre-training data, and our custom tokenizer on Vietnamese social media texts for ViSoBERT. + +\subsection{Pre-training Data} +% This work uses a large corpus of 01 GB of uncompressed texts as a pre-training dataset. +% Updates to Google’s advertising resources indicate that YouTube had 63.00 million users in Vietnam in early 2023. Figures published in ByteDance’s advertising resources indicate that in Vietnam, TikTok had 49.86 million users aged 18 and above in early 2023. Data published in Meta’s advertising resources indicate that ads on Facebook Messenger reached 52.65 million users in Vietnam in early 2023. + +We crawled textual data from Vietnamese public social networks such as Facebook\footnote{\url{https://www.facebook.com/}}, Tiktok\footnote{\url{https://www.tiktok.com/}}, and YouTube\footnote{\url{https://www.youtube.com/}} which are the three most well known social networks in Vietnam, with 52.65, 49.86, and 63.00 million users\footnote{\url{https://datareportal.com/reports/digital-2023-vietnam}}, respectively, in early 2023. + +To effectively gather data from these platforms, we harnessed the capabilities of specialized tools provided by each platform. + +\begin{enumerate} + \item \textbf{Facebook}: We crawled comments from Vietnamese-verified pages by Facebook posts via the Facebook Graph API\footnote{\url{https://developers.facebook.com/}} between January 2016 and December 2022. + \item \textbf{TikTok}: We collected comments from Vietnamese-verified channels by TikTok through TikTok Research API\footnote{\url{https://developers.tiktok.com/products/research-api/}} between January 2020 and December 2022. + \item \textbf{YouTube}: We scrapped comments from Vietnam-verified channels' videos by YouTube via YouTube Data API\footnote{\url{https://developers.google.com/youtube/v3}} between January 2016 and December 2022. +\end{enumerate} + +% We crawled Vietnamese social media textual data using the official Facebook\footnote{\url{https://developers.facebook.com/}}, Tiktok\footnote{\url{https://developers.tiktok.com/products/research-api/}}, and YouTube\footnote{\url{https://developers.google.com/youtube/v3}}. + +%Therefore, we crawled Vietnamese social media textual data using the official Facebook\footnote{\url{https://developers.facebook.com/}}, Tiktok\footnote{\url{https://developers.tiktok.com/products/research-api/}}, and YouTube\footnote{\url{https://developers.google.com/youtube/v3}} API on these to capture all Vietnamese social media contexts as much as possible. + +\textbf{Pre-processing Data:} \label{preprocessing} +Pre-processing is vital for models consuming social media data, which is massively noisy, and has user handles (@username), hashtags, emojis, misspellings, hyperlinks, and other noncanonical texts. We perform the following steps to clean the dataset: removing noncanonical texts, removing comments including links, removing excessively repeated spam and meaningless comments, removing comments including only user handles (@username), and keeping emojis in training data. + +As a result, our pretraining data after crawling and preprocessing contains 1GB of uncompressed text. Our pretraining data is available only for research purposes. + + + +% xoá bỏ các ký tự lạ (noncanonical text) +% Spam: +% xoá bỏ các câu có link +% xoá bỏ các câu có chữ "vay vốn" +% xoá bỏ các câu có chữ "để lại sđt" +% "phật" +% "boom" +% "tri ân" + +% giữ các emoji +% xoá bỏ các câu có tag tên + +\subsection{Model Architecture} \label{architecture} + +Transformers \cite{attentionisallyouneed} have significantly advanced NLP research using trained models in recent years. Although language models \cite{phobert,phonlp} have also proven effective on a range of Vietnamese NLP tasks, their results on Vietnamese social media tasks \cite{smtce} need to be significantly improved. To address this issue, taking into account successful hyperparameters from XLM-R \cite{xlm-r}, we proposed ViSoBERT, a transformer-based model in the style of XLM-R architecture with 768 hidden units, 12 self-attention layers, and 12 attention heads, and used a masked language objective (the same as \citet{xlm-r}). + +\subsection{The Vietnamese Social Media Tokenizer} \label{tokenizer} +To the best of our knowledge, ViSoBERT is the first PLM with a custom tokenizer for Vietnamese social media texts. Bernice \cite{bernice} was the first multilingual model trained from scratch on Twitter\footnote{\url{https://twitter.com/}} data with a custom tokenizer; however, Bernice's tokenizer doesn't handle Vietnamese social media text effectively. Moreover, existing Vietnamese pre-trained models' tokenizers perform poorly on social media text because of different domain data training. Therefore, we developed the first custom tokenizer for Vietnamese social media texts. + +Owing to the ability to handle raw texts of SentencePiece \cite{sentencepiece} without any loss compared to Byte-Pair Encoding \cite{xlm-r}, we built a custom tokenizer on Vietnamese social media by SentencePiece on the whole training dataset. +A model has better coverage of data than another when \textit{fewer} subwords are needed to represent the text, and the subwords are \textit{longer} \cite{bernice}. Figure~\ref{fig:tokenlen} (in Appendix~\ref{app:socialtok}) displays the mean token length for each considered model and group of tasks. ViSoBERT achieves the shortest representations for all Vietnamese social media downstream tasks compared to other PLMs. + +Emojis and teencode are essential to the “language” on Vietnamese social media platforms. Our custom tokenizer's capability to decode emojis and teencode ensure that their semantic meaning and contextual significance are accurately captured and incorporated into the language representation, thus enhancing the overall quality and comprehensiveness of text analysis and understanding. + +% Table~\ref{tab:tokenizer} (in Appendix \ref{app:socialtok}) shows several examples of our tokenizer compared to the previous Vietnamese state-of-the-art PLM PhoBERT on Vietnamese social media text. + +To assess the tokenized ability of Vietnamese social media textual data, we conducted an analysis of several data samples. Table \ref{tab:tokenizer} shows several actual social comments and their tokenizations with the tokenizers of the two pre-trained language models, ViSoBERT and PhoBERT, the best strong baseline. The results show that our custom tokenizer performed better compared to others. + +\begin{table*}[!ht] +\centering +\resizebox{\textwidth}{!}{ +\begin{tabular}{l|l|l} +\hline +\textbf{Comments} & \multicolumn{1}{c|}{\textbf{ViSoBERT}} & \multicolumn{1}{c}{\textbf{PhoBERT}} \\ +\hline +\begin{tabular}[c]{@{}l@{}}concặc cáilồn gìđây\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\\\textit{English}: Wut is dis fuckingd1ck \includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\end{tabular} & \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{},~"conc", "ặc", "cái", "l", "ồn", "gì", "đây", \\"\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}", \textless{}/s\textgreater{}\end{tabular} & \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "c o n @ @", "c @ @", "ặ c", "c á @ @", \\"i l @ @", "ồ n", "g @ @","ì @ @", "đ â y", \\ \textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \textless{}/s\textgreater{}\end{tabular} \\ +\hline +\begin{tabular}[c]{@{}l@{}}e cảmơn anh\includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}\includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}\\\textit{English}: Thankyou \includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}\includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}\end{tabular} & \textless{}s\textgreater{}, "e", "cảm", "ơn", "anh", "\includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}", "\includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}", \textless{}/s\textgreater{} & \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{},~"e", "c ả @ @", "m @ @", "ơ n", "a n h", \\, , \textless{}/s\textgreater{} \end{tabular}\\ +\hline +\begin{tabular}[c]{@{}l@{}}d4y l4 vj du cko mot cau teencode\\\textit{English}: Th1s 1s 4 teencode s3nt3nc3\end{tabular} & \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "d", "4", "y", "l", "4", "vj", "du", "cko", "mot", \\"cau", "teen", "code", \textless{}/s\textgreater{}\end{tabular} & \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "d @ @", "4 @ @", "y", "l @ @", "4", \\"v @ @", "j", "d u", "c k @ @", "o", "m o @ @", \\"t", "c a u"; "t e @ @", "e n @ @", "c o d e", \textless{}/s\textgreater{}\end{tabular} \\ +\hline +\end{tabular}} +\caption{Actual social comments and their tokenizations with the tokenizers of the two pre-trained language models, ViSoBERT and PhoBERT.} +\label{tab:tokenizer} +\end{table*} + +% \begin{table*} +% \centering +% \caption{Actual social comments and their tokenizations with the tokenizers of the two pre-trained models, ViSoBERT and PhoBERT.} +% \label{tab:tokenizer} +% \resizebox{\textwidth}{!}{ +% \begin{tabular}{l|l|l} +% \hline +% \textbf{Comments} & \multicolumn{1}{c|}{\textbf{ViSoBERT}} & \multicolumn{1}{c}{\textbf{PhoBERT}} \\ +% \hline +% \begin{tabular}[c]{@{}l@{}}concặc cáilồn gìđây\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\\\textit{English}: Wut is dis fuckingd1ck \includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\end{tabular} & \begin{tabular}[c]{@{}l@{}}{[}\textless{}s\textgreater{},~"con", "c", "ặc", "cái", "l", "ồn", "gì", "đ", "ây", \\"\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}", "\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}", "\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}", \textless{}/s\textgreater{}]\end{tabular} & \begin{tabular}[c]{@{}l@{}}{[}\textless{}s\textgreater{}, "c o n @ @", "c @ @", "ặ c", "c á @ @", "i l @ @", "ồ n", \\"g @ @","ì @ @", "đ â y", \textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \textless{}/s\textgreater{}]\end{tabular} \\ +% \hline +% \begin{tabular}[c]{@{}l@{}}e cảmơn anh\includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}\includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}\\\textit{English}: Thankyou \includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}\includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}\end{tabular} & {[}\textless{}s\textgreater{}, "e", "cảm", "ơn", "anh", "\includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}", "\includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}", \textless{}/s\textgreater{}] & {[}\textless{}s\textgreater{},~"e", "c ả @ @", "m @ @", "ơ n", "a n h", , , \textless{}/s\textgreater{}] \\ +% \hline +% \begin{tabular}[c]{@{}l@{}}d4y l4 vj du cko mot cau teencode\\\textit{English}: Th1s 1s 4 teencode s3nt3nc3\end{tabular} & \begin{tabular}[c]{@{}l@{}}{[}\textless{}s\textgreater{}, "d4y", "l4", "v", "j", "du", "cko", "mot", "c", \\"au", "te", "en", "co", "de", \textless{}/s\textgreater{}]\end{tabular} & \begin{tabular}[c]{@{}l@{}}{[}\textless{}s\textgreater{}, "d @ @", "4 @ @", "y", "l @ @", "4", "v @ @", \\"j", "d u", "c k @ @", "o", "m o @ @", "t", "c a u"; "t e @ @", \\"e n @ @", "c o d e", \textless{}/s\textgreater{}]\end{tabular} \\ +% \hline +% \end{tabular}} +% \end{table*} + +\section{Experiments and Results} +\subsection{Experimental Settings} +\label{setup} +We accumulate gradients over one step to simulate a batch size of 128. When pretraining from scratch, we train the model for 1.2M steps in 12 epochs. We trained our model for about three days on 2$\times$RTX4090 GPUs (24GB). Each sentence is tokenized and masked dynamically with a probability equal to 30\% (which is extensively experimented on Section~\ref{impactofmasking} to explore the optimal value). Further details on hyperparameters and training can be found in Table~\ref{tab:hyperparameters} of Appendix \ref{app:exp}. + +%\begin{table}[!ht] +%\centering +%\caption{ All hyperparameters established for training ViSoBERT.} +%\label{tab:hyperparameters} +%\resizebox{\columnwidth}{!}{ +%\begin{tabular}{llr} +%\hline +%\multirow{7}{*}{Optimizer} & Algorithm & Adam \\ +% & Learning rate & 5e-5 \\ +% & Epsilon & 1e-8 \\ +% & LR scheduler & linear decay and warmup \\ +% & Warmup steps & 1000 \\ +% & Betas & 0.9 and 0.99 \\ +% & Weight decay & 0.01 \\ +%\hline +%\multirow{3}{*}{Batch} & Sequence length & 128 \\ +% & Batch size & 128 \\ +% & Vocab size & 15002 \\ +%\hline +%\multirow{2}{*}{Misc} & Dropout & 0.1 \\ +% & Attention dropout & 0.1 \\ +%\hline +%\end{tabular}} +%\end{table} + +\begin{table*}[!ht] +\centering +\resizebox{\textwidth}{!}{% +\begin{tabular}{l|ccc|l|c|c} +\hline +\textbf{Dataset} & \textbf{Train} & \textbf{Dev} & \textbf{Test} & \multicolumn{1}{c|}{\textbf{Task}} & \textbf{Evaluation Metrics} & \textbf{Classes} \\ +\hline +UIT-VSMEC & 5,548 & 686 & 693 & Emotion Recognition (ER) & \multirow{5}{*}{Acc, WF1, MF1 (\%)} & 7 \\ +UIT-HSD & 24,048 & 2,672 & 6,680 & Hate Speech Detection (HSD) & & 3 \\ +SA-VLSP2016 & 5,100 & - & 1,050 & Sentiment Analysis (SA) & & 3 \\ +ViSpamReviews & 14,306 & 1,590 & 3,974 & Spam Reviews Detection (SRD) & & 4 \\ +ViHOS & 8,844 & 1,106 & 1,106 & Hate Speech Spans Detection (HSSD) & & 3 \\ +\hline +\end{tabular}} +\caption{Statistics and descriptions of Vietnamese social media processing tasks. Acc, WF1, and MF1 denoted Accuracy, weighted F1-score, and macro F1-score metrics, respectively.} +\label{tab:datasets} +\end{table*} +% According to previous works \cite{smtce, phobertcnn}, these downstream tasks are still challenging because of the limitation of available models or data pre-processing techniques. +\textbf{Downstream tasks.} To evaluate ViSoBERT, we used five Vietnamese social media datasets available for research purposes, as summarized in Table \ref{tab:datasets}. The downstream tasks include emotion recognition (UIT-VSMEC) \cite{ho2020emotion}, hate speech detection (UIT-ViHSD) \cite{vihsd}, sentiment analysis (SA-VLSP2016) \cite{nguyen2018vlsp}, spam reviews detection (ViSpamReviews) \cite{vispamreviews}, and hate speech spans detection (UIT-ViHOS) \cite{vihos}. + +% We empirically fine-tuned all pre-trained language models using \textit{simpletransformers}\footnote{\url{https://simpletransformers.ai/} (ver 0.63.11)}. We followed fairly standard practices for fine-tuning, most of which are described in \cite{bert}. + +\textbf{Fine-tuning.} We conducted empirical fine-tuning for all pre-trained language models using the \textit{simpletransformers}\footnote{\url{https://simpletransformers.ai/} (ver 0.63.11)}. Our fine-tuning process followed standard procedures, most of which are outlined in \cite{bert}. For all tasks mentioned above, we use a batch size of 40, a maximum token length of 128, a learning rate of 2e-5, and AdamW optimizer \cite{AdamW} with an epsilon of 1e-8. We executed a 10-epoch training process and evaluated downstream tasks using the best-performing model from those epochs. Furthermore, none of the pre-processing techniques is applied in all datasets to evaluate our PLM's ability to handle raw texts. + +\textbf{Baseline models.} To establish the main baseline models, we utilized several well-known PLMs, including monolingual and multilingual, to support Vietnamese NLP social media tasks. The details of each model are shown in Table \ref{tab:pre-trained}. + +\begin{itemize} + \item \textbf{Monolingual language models}: viBERT \citep{viBERTandvELECTRA} and vELECTRA \citep{viBERTandvELECTRA} are PLMs for Vietnamese based on BERT and ELECTRA architecture, respectively. PhoBERT, which is based on BERT architecture and RoBERTa pre-training procedure, \citep{phobert} is the first large-scale monolingual language model pre-trained for Vietnamese; PhoBERT obtains state-of-the-art performances on a range of Vietnamese NLP tasks. + + \item \textbf{Multilingual language models}: Additionally, we incorporated two multilingual PLMs, mBERT \citep{bert} and XLM-R \citep{xlm-r}, which were previously shown to have competitive performances to monolingual Vietnamese models. XLM-R, a cross-lingual PLM introduced by \citet{xlm-r}, has been trained in 100 languages, among them Vietnamese, utilizing a vast 2.5TB Clean CommonCrawl dataset. XLM-R presents notable improvements in various downstream tasks, surpassing the performance of previously released multilingual models such as mBERT \cite{bert} and XLM \cite{xlm}. + + \item \textbf{Multilingual social media language models:} To ensure a fair comparison with our PLM, we integrated multiple multilingual social media PLMs, including XLM-T \cite{barbieri-etal-2022-xlm}, TwHIN-BERT \cite{twhinbert}, and Bernice \cite{bernice}. + +\end{itemize} + +% For PhoBERT, XLM-R, and TwHIN-BERT, we implemented two versions \textit{Base} and \textit{Large} for all tasks, although it is not a fair comparison due to their significantly larger model configurations. + +\begin{table*}[!ht] +\centering +\resizebox{\textwidth}{!}{ +\begin{tabular}{l|ccccccccc} +\hline +\textbf{Model} & \textbf{\#Layers} & \textbf{\#Heads} & \textbf{\#Steps} & \textbf{\#Batch} & \textbf{Domain Data} & \textbf{\#Params} & \textbf{\#Vocab} & \textbf{\#MSL} & \multicolumn{1}{l}{\textbf{CSMT}} \\ +\hline +viBERT \cite{viBERTandvELECTRA} & 12 & 12 & - & 16 & Vietnamese News & - & 30K & 256 & \textcolor{red}{No} \\ +vELECTRA \cite{viBERTandvELECTRA} & 12 & 3 & - & 16 & NewsCorpus + OscarCorpus & - & 30K & 256 & \textcolor{red}{No} \\ +PhoBERT$_\text{Base}$ \cite{phobert} & 12 & 12 & 540K & 1024 & ViWiki + ViNews & 135M & 64K & 256 & \textcolor{red}{No} \\ +PhoBERT$_\text{Large}$ \cite{phobert} & 24 & 16 & 1.08M & 512 & ViWiki + ViNews & 370M & 64K & 256 & \textcolor{red}{No} \\ +\hline +mBERT \cite{bert} & 12 & 12 & 1M & 256 & BookCorpus + EnWiki & 110M & 30K & 512 & \textcolor{red}{No} \\ +XLM-R$_\text{Base}$ \cite{xlm-r} & 12 & 12 & 1.5M & 8192 & CommonCrawl + Wiki & 270M & 250K & 512 & \textcolor{red}{No} \\ +XLM-R$_\text{Large}$ \cite{xlm-r} & 24 & 16 & 1.5M & 8192 & CommonCrawl + Wiki & 550M & 250K & 512 & \textcolor{red}{No} \\ +XLM-T \cite{barbieri-etal-2022-xlm} & 12 & 12 & - & 8192 & Multilingual Tweets & - & 250k & 512 & \textcolor{red}{No} \\ +TwHIN-BERT$_\text{Base}$ \cite{twhinbert} & 12 & 12 & 500K & 6K & Multilingual Tweets & 135M to 278M & 250K & 128 & \textcolor{red}{No} \\ +TwHIN-BERT$_\text{Large}$ \cite{twhinbert} & 24 & 16 & 500K & 8K & Multilingual Tweets & 550M & 250K & 128 & \textcolor{red}{No} \\ +Bernice \cite{bernice} & 12 & 12 & 405K+ & 8192 & Multilingual Tweets & 270M & 250K & 128 & \textcolor{green}{Yes} \\ +\hdashline +ViSoBERT (Ours) & 12 & 12 & 1.2M & 128 & Vietnamese social media & 97M & 15K & 512 & \textcolor{green}{Yes} \\ +\hline +\end{tabular}} +\caption{Detailed information about baselines and our PLM. \#Layers, \#Heads, \#Batch, \#Params, \#Vocab, \#MSL, and CSMT indicate the number of hidden layers, number of attention heads, batch size value, domain training data, number of total parameters, vocabulary size, max sequence length, and custom social media tokenizer, respectively.} +\label{tab:pre-trained} +\end{table*} + +\subsection{Main Results} +Table~\ref{mainresult} shows ViSoBERT's scores with the previous highest reported results on other PLMs using the same experimental setup. It is clear that our ViSoBERT produces new SOTA performance results for multiple Vietnamese downstream social media tasks without any pre-processing technique. + +\begin{table*}[!ht] +\centering +\resizebox{\textwidth}{!}{% +% \setlength{\tabcolsep}{6pt} % Default value: 6pt +% \renewcommand{\arraystretch}{1} +\begin{tabular}{l|c|ccj|ccj|ccj|>{\centering\arraybackslash}m{1.1cm}>{\centering\arraybackslash}m{1.1cm}S[table-format=2.2,table-space-text-pre=~,table-space-text-post=\ddgr,table-column-width=1.1cm]|>{\centering\arraybackslash}m{1.1cm}>{\centering\arraybackslash}m{1.8cm}S[table-format=2.2,table-space-text-pre=~,table-space-text-post=\ddgr,table-column-width=1.1cm]} +\hline +\multicolumn{1}{c|}{\multirow{2}{*}{\textbf{Model}}} & \multirow{2}{*}{\textbf{Avg}} & \multicolumn{3}{c|}{\textbf{Emotion Regconition}} & \multicolumn{3}{c|}{\textbf{Hate Speech Detection}} & \multicolumn{3}{c|}{\textbf{Sentiment Analysis}} & \multicolumn{3}{c|}{\textbf{Spam Reviews Detection}} & \multicolumn{3}{c}{\textbf{Hate Speech Spans Detection}} \\ +\cline{3-17} +\multicolumn{1}{c|}{} & & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} & \textbf{Acc} & \textbf{WF1} & \multicolumn{1}{c|}{\textbf{MF1}} & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} & \textbf{Acc} & \textbf{WF1} & \multicolumn{1}{c|}{\textbf{MF1}} & \textbf{Acc} & \textbf{WF1} & \multicolumn{1}{c}{\textbf{MF1}} \\ +\hline +viBERT & 71.57 & 61.91 & 61.98 & 59.70 & 85.34 & 85.01 & 62.07 & 74.85 & 74.73 & 74.73 & 89.93 & 89.79 & 76.80 & 90.42 & 90.45 & 84.55 \\ +vELECTRA & 72.43 & 64.79 & 64.71 & 61.95 & 86.96 & 86.37 & 63.95 & 74.95 & 74.88 & 74.88 & 89.83 & 89.68 & 76.23 & 90.59 & 90.58 & 85.12 \\ +PhoBERT$_\text{Base}$ & 72.81 & 63.49 & 63.36 & 61.41 & 87.12 & 86.81 & 65.01 & 75.72 & 75.52 & 75.52 & 89.83 & 89.75 & 76.18 & 91.32 & 91.38 & 85.92 \\ +PhoBERT$_\text{Large}$ & 73.47 & 64.71 & 64.66 & 62.55 & 87.32 & 86.98 & 65.14 & 76.52 & 76.36 & 76.22 & 90.12 & 90.03 & 76.88 & 91.44 & 91.46 & 86.56 \\ +\hline +mBERT (cased) & 68.07 & 56.27 & 56.17 & 53.48 & 83.55 & 83.99 & 60.62 & 67.14 & 67.16 & 67.16 & 89.05 & 88.89 & 74.52 & 89.88 & 89.87 & 84.57 \\ +mBERT (uncased) & 67.66 & 56.23 & 56.11 & 53.32 & 83.38 & 81.27 & 58.92 & 67.25 & 67.22 & 67.22 & 88.92 & 88.72 & 74.32 & 89.84 & 89.82 & 84.51 \\ +XLM-R$_\text{Base}$ & 72.08 & 60.92 & 61.02 & 58.67 & 86.36 & 86.08 & 63.39 & 76.38 & 76.38 & 76.38 & 90.16 & 89.96 & 76.55 & 90.74 & 90.72 & 85.42 \\ +XLM-R$_\text{Large}$ & 73.40 & 62.44 & 61.37 & 60.25 & 87.15 & 86.86 & 65.13 & \textbf{78.28} & \textbf{78.21} & \textbf{78.21} & 90.36 & 90.31 & 76.75 & 91.52 & 91.50 & 86.66 \\ +XLM-T & 72.23 & 64.64 & 64.37 & \multicolumn{1}{c|}{59.86} & 86.22 & 86.12 & 63.48 & 75.66 & 75.60 & \multicolumn{1}{c|}{75.60} & 90.07 & 90.11 & 76.66 & 90.88 & 90.88 & 85.53 \\ +TwHIN-BERT$_\text{Base}$ & 71.60 & 61.49 & 60.88 & 57.97 & 86.63 & 86.23 & 63.67 & 73.76 & 73.72 & 73.72 & 90.25 & 90.35 & 76.98 & 90.99 & 90.90 & 85.67 \\ +TwHIN-BERT$_\text{Large}$ & 73.42 & 64.21 & 64.29 & 61.12 & 87.23 & 86.78 & 65.23 & 76.92 & 76.83 & 76.83 & 90.47 & 90.42 & 77.28 & 91.45 & 91.47 & 86.65 \\ +Bernice & 72.49 & 64.21 & 64.27 & \multicolumn{1}{c|}{60.68} & 86.12 & 86.48 & 64.32 & 74.57 & 74.90 & \multicolumn{1}{c|}{74.90} & 90.22 & 90.21 & 76.89 & 90.48 & 90.06 & 85.67 \\ +\hdashline +ViSoBERT & \textbf{75.65} & \textbf{68.10} & \textbf{68.37} & \bfseries 65.88\ddgr & \textbf{88.51} & \textbf{88.31} & \bfseries 68.77\ddgr & 77.83 & 77.75 & 77.75 & \textbf{90.99} & \textbf{90.92} & \bfseries 79.06\ddgr & \textbf{91.62} & \textbf{91.57} & \textbf{86.80} \\ +\hline +\end{tabular}} +\caption{Performances on downstream Vietnamese social media tasks of previous state-of-the-art monolingual and multilingual PLMs without pre-processing techniques. Avg denoted the average MF1 score of each PLM. \ddgr~denotes that the highest result is statistically significant at $p < \text{0.01}$ compared to the second best, using a paired t-test.} +\label{mainresult} +\end{table*} + +\textbf{Emotion Recognition Task}: PhoBERT and TwHIN-BERT archive the previous SOTA performances on monolingual and multilingual models, respectively. ViSoBERT obtains 68.10\%, 68.37\%, and 65.88\% of Acc, WF1, and MF1, respectively, significantly higher than these PhoBERT and TwHIN-BERT models. + +\textbf{Hate Speech Detection Task}: ViSoBERT achieves significant improvements over previous state-of-the-art models, PhoBERT and TwHIN-BERT, with scores of 88.51\%, 88.31\%, and 68.77\% in Acc, WF1, and MF1, respectively. Notably, these achievements are made despite the presence of bias within the dataset\footnote{UIT-HSD is massively imbalanced, included 19,886; 1,606; and 2,556 of CLEAN, OFFENSIVE, and HATE class.}. + +\textbf{Sentiment Analysis Task}: XLM-R archived SOTA performance on three evaluation metrics. However, there is no significant increase in performance on this downstream task, for 0.45\%, 0.46\%, and 0.46\% higher on Acc, WF1, and MF1 compared to our pre-trained language model, PhoBERT$_\text{Large}$. +The SA-VLSP2016 dataset domain is technical article reviews, including TinhTe\footnote{\url{https://tinhte.vn/}} and VnExpress\footnote{\url{https://vnexpress.net/}}, which are often used as Vietnamese standard data. The reviews or comments in these newspapers are in proper form. While most of the dataset is sourced from articles, it also includes data from Facebook\footnote{\url{https://www.facebook.com/}}, a Vietnamese social media platform that accounts for only 12.21\% of the dataset. Therefore, the dataset does not fully capture Vietnamese social media platforms' diverse characteristics and informal language. However, ViSoBERT still surpassed other baselines by obtaining 1.31\%/0.91\% Acc, 1.39\%/0.92\% WF1, and 1.53\%/0.92\% MF1 compared to PhoBERT/TwHIN-BERT. + +% Therefore, the SA-VLSP2016 dataset does not contain many social media characteristics. +%The SA-VLSP2016 dataset also contains data from Facebook\footnote{\url{https://www.facebook.com/}}, but the percentage of Facebook data is minor (12.21\% of the dataset). + +% Unlike general texts, social media texts frequently update and change. The SA-VLSP2016 dataset, collected in 2016, does not align with the current Vietnamese social media texts used to train ViSoBERT. There is a contextual discrepancy that can result in information leakage in comments. Furthermore, the SA-VLSP2016 dataset primarily consists of data from TheGioiDiDong\footnote{\url{https://www.thegioididong.com/}}, a Vietnamese website focused on phones, making it more domain-specific to technical topics than social media content. However, ViSoBERT outperformed PhoBERT and TwHIN-BERT by obtaining 1.31\% Acc, 1.39\% WF1, and 1.53\% MF1 compared to PhoBERT, and 0.91\%, 0.92\%, and 0.92\% compared to TwHIN-BERT. + +\textbf{Spam Reviews Detection Task}: ViSoBERT performed better than the top two baseline models, PhoBERT and TwHIN-BERT. Specifically, it achieved 0.8\%, 0.9\%, and 2.18\% higher scores in accuracy (Acc), weighted F1 (WF1), and micro F1 (MF1) compared to PhoBERT. When compared to TwHIN-BERT, ViSoBERT outperformed it with 0.52\%, 0.50\%, and 1.78\% higher scores in Acc, WF1, and MF1, respectively. + +\textbf{Hate Speech Spans Detection Task}\footnote{For the Hate Speech Spans Detection task, we evaluate the total of spans on each comment rather than spans of each word in \citet{vihos} to retain the context of each comment.}: Our pre-trained ViSoBERT boosted the results up to 91.62\%, 91.57\%, and 86.80\% on Acc, WF1, and MF1, respectively. While the difference is insignificant, ViSoBERT indicates an outstanding ability to capture Vietnamese social media information compared to other PLMs (see Section~\ref{feature-based}). + +\textbf{Multilingual social media PLMs}: The results show that ViSoBERT consistently outperforms XLM-T and Bernice in five Vietnamese social media tasks. It's worth noting that XLM-T, TwHIN-BERT, and Bernice were all exclusively trained on data from the Twitter platform. However, this approach has limitations when applied to the Vietnamese context. The training data from this source may not capture the intricate linguistic and contextual nuances prevalent in Vietnamese social media because Twitter is not widely used in Vietnam. + +\section{Result Analysis and Discussion} +In this section, we consider the improvement of our PLM more compared to powerful others, including PhoBERT and TwHIN-BERT, in terms of different aspects. Firstly, we investigate the effects of masking rate on our pre-trained model performance (see Section \ref{impactofmasking}). Additionally, we examine the influence of social media characteristics on the model's ability to process and understand the language used in these social contexts (see Section \ref{characteristics}). Lastly, we employed feature-based extraction techniques on task-specific models to verify the potential of leveraging social media textual data to enhance word representations (see Section \ref{feature-based}). + +%We delve into the impact of various factors on our Vietnamese social media PLMs. + +% \subsubsection{A Cheaper and Faster Model} +% ViSoBERT was much cheaper and faster to train than its closest competitors. +% We argue this to the efficiency of in-domain pre-training data. Despite using orders of magnitude significantly fewer data, we have enough Vietnamese social media data to learn a better model than through adaptation. +\subsection{Impact of Masking Rate on Vietnamese Social Media PLM}\label{impactofmasking} +For the first time presenting the Masked Language Model, \citet{bert} consciously utilized a random masking rate of 15\%. The authors believed masking too many tokens could lead to losing crucial contextual information required to decode them accurately. Additionally, the authors felt that masking too few tokens would harm the training process and make it less effective. However, according to \citet{shouldyoumask}, 15\% is not universally optimal for model and training data. + +We experiment with masking rates ranging from 10\% to 50\% and evaluate the model's performance on five downstream Vietnamese social media tasks. Figure~\ref{maskingrate} illustrates the results obtained from our experiments with six different masking rates. Interestingly, our pre-trained ViSoBERT achieved the highest performance when using a masking rate of 30\%. This suggests a delicate balance between the amount of contextual information retained and the efficiency of the training process, and an optimal masking rate can be found within this range. + +However, the optimal masking rate also depends on the specific task. For instance, in the hate speech detection task, we found that a masking rate of 50\% yielded the best results, surpassing other masking rate values. This implies that the optimal masking rate may vary depending on the nature and requirements of different tasks. + +Considering the overall performance across multiple tasks, we determined that a masking rate of 30\% produced the optimal balance for our pre-trained ViSoBERT model. Consequently, we adopted this masking rate for ViSoBERT, ensuring efficient and effective utilization of contextual information during training. + +\begin{figure}[ht] + \centering + % da no bi out 8 trang thay :v, em dang fix + \includegraphics[width=\columnwidth]{f1_macro_masked_rate1111-crop.pdf} + \caption{Impact of masking rate on our pre-trained ViSoBERT in terms of MF1.} + \label{maskingrate} +\end{figure} + +% \subsubsection{Vocabulary and In-domain Pretraining Data Efficiency} +% % Vocabulary +% Figure~\ref{token} shows the average length of tokens per comment of baselines and our pre-trained model. ViSoBERT has more compact representations than the 3 best baselines, including PhoBERT for monolingual, XLM-R for multilingual, and TwHIN-BERT for social media domain. ViSoBERT has a slightly higher mean length compared to +% % In-domain +% PLM4 +% \begin{figure}[ht] +% \centering +% \includegraphics[width=\columnwidth]{emnlp2023-latex/Figures/token.png} +% \caption{Distribution of the average number of tokens per task of each pre-trained.} +% \label{token} +% \end{figure} + +\subsection{Impact of Vietnamese Social Media Characteristics} \label{characteristics} +Emojis, teencode, and diacritics are essential features of social media, especially Vietnamese social media. The ability of the tokenizer to decode emojis and the ability of the model to understand the context of teencode and diacritics are crucial. Hence, to evaluate the performance of ViSoBERT on social media characteristics, comprehensive experiments were conducted among several strong PLMs: PM4ViSMT, PhoBERT, and TwHIN-BERT. + +\textbf{Impact of Emoji on PLMs:} +\begin{table*}[!ht] +\centering +\resizebox{\textwidth}{!}{ +\begin{tabular}{c|www|www|www|www|S[table-column-width=1.1cm,table-format=2.2,table-space-text-pre=\dwnarr,color=black]S[table-column-width=1.8cm,table-format=2.2,table-space-text-pre=\dwnarr,color=black]S[table-column-width=1.1cm,table-format=2.2,table-space-text-pre=\dwnarr,color=black]} +\hline +\multirow{2}{*}{\textbf{Model}} & \multicolumn{3}{c|}{\textbf{Emotion Regconition}} & \multicolumn{3}{c|}{\textbf{Hate Speech Detection}} & \multicolumn{3}{c|}{\textbf{Sentiment Analysis}} & \multicolumn{3}{c|}{\textbf{Spam Reviews Detection}} & \multicolumn{3}{c}{\textbf{Hate Speech Spans Detection}} \\ +\cline{2-16} + & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} \\ +\hline +\multicolumn{16}{c}{\textit{\textbf{Converting emojis to text}}} \\ +\hline +\multicolumn{1}{l|}{PhoBERT$_\text{Large}$} & 66.08 & 66.15 & 63.35 & 87.43 & 87.22 & 65.32 & 76.73 & 76.48 & 76.48 & 90.35 & 90.11 & 77.02 & 92.16 & 91.98 & 86.72 \\ +$\Delta$ & \multicolumn{1}{y}{\uparr 1.37} & \multicolumn{1}{y}{\uparr 1.49} & \multicolumn{1}{y|}{\uparr 0.80} & \multicolumn{1}{y}{\uparr 0.11} & \multicolumn{1}{y}{\uparr 0.24} & \multicolumn{1}{y|}{\uparr 0.18} & \multicolumn{1}{z}{\dwnarr 0.21} & \multicolumn{1}{z}{\dwnarr 0.12} & \multicolumn{1}{z|}{\dwnarr 0.12} & \multicolumn{1}{y}{\uparr 0.23} & \multicolumn{1}{y}{\uparr 0.08} & \multicolumn{1}{y|}{\uparr 0.14} & \multicolumn{1}{y}{\uparr 0.72} & \multicolumn{1}{y}{\uparr 0.52} & \multicolumn{1}{y}{\uparr 0.16} \\ +\multicolumn{1}{l|}{TwHIN-BERT$_\text{Large}$} & 64.82 & 64.42 & 61.33 & 86.03 & 85.52 & 63.52 & 75.42 & 75.95 & 75.95 & 90.55 & 90.47 & 77.32 & 92.21 & 92.01 & 86.84 \\ +$\Delta$ & \multicolumn{1}{y}{\uparr 0.61} & \multicolumn{1}{y}{\uparr 0.13} & \multicolumn{1}{y|}{\uparr 0.21} & \multicolumn{1}{z}{\dwnarr 1.20} & \multicolumn{1}{z}{\dwnarr 1.26} & \multicolumn{1}{z|}{\dwnarr 1.71} & \multicolumn{1}{z}{\dwnarr 1.50} & \multicolumn{1}{z}{\dwnarr 0.88} & \multicolumn{1}{z|}{\dwnarr 0.88} & \multicolumn{1}{y}{\uparr 0.08} & \multicolumn{1}{y}{\uparr 0.05} & \multicolumn{1}{y|}{\uparr 0.04} & \multicolumn{1}{y}{\uparr 0.76} & \multicolumn{1}{y}{\uparr 0.54} & \multicolumn{1}{y}{\uparr 0.19} \\ +\hdashline +\multicolumn{1}{l|}{ViSoBERT $[\clubsuit]$} & 67.53 & 67.93 & 65.42 & 87.82 & 87.88 & 67.25 & 76.95 & 76.85 & 76.85 & 90.22 & 90.18 & 78.25 & 92.42 & 92.11 & 87.01 \\ +$\Delta$ & \multicolumn{1}{z}{\dwnarr 0.57} & \multicolumn{1}{z}{\dwnarr 0.44} & \multicolumn{1}{z|}{\dwnarr 0.46} & \multicolumn{1}{z}{\dwnarr 0.69} & \multicolumn{1}{z}{\dwnarr 0.41} & \multicolumn{1}{z|}{\dwnarr 1.49} & \multicolumn{1}{z}{\dwnarr 0.88} & \multicolumn{1}{z}{\dwnarr 0.90} & \multicolumn{1}{z|}{\dwnarr 0.90} & \multicolumn{1}{z}{\dwnarr 0.77} & \multicolumn{1}{z}{\dwnarr 0.74} & \multicolumn{1}{z|}{\dwnarr 0.81} & \multicolumn{1}{y}{\uparr 0.80} & \multicolumn{1}{y}{\uparr 0.54} & \multicolumn{1}{y}{\uparr 0.21} \\ +\hline +\multicolumn{16}{c}{\textit{\textbf{Removing emojis}}} \\ +\hline +\multicolumn{1}{l|}{PhoBERT$_\text{Large}$} & 65.21 & 65.14 & 62.81 & 87.25 & 86.72 & 64.85 & 76.72 & 76.48 & 76.48 & 90.21 & 90.09 & 77.02 & 91.53 & 91.51 & 86.62 \\ +$\Delta$ & \multicolumn{1}{y}{\uparr 0.50} & \multicolumn{1}{y}{\uparr 0.48} & \multicolumn{1}{y|}{\uparr 0.26} & \multicolumn{1}{z}{\dwnarr 0.07} & \multicolumn{1}{z}{\dwnarr 0.26} & \multicolumn{1}{z|}{\dwnarr 0.29} & \multicolumn{1}{y}{\uparr 0.20} & \multicolumn{1}{y}{\uparr 0.12} & \multicolumn{1}{y|}{\uparr 0.12} & \multicolumn{1}{y}{\uparr 0.09} & \multicolumn{1}{y}{\uparr 0.06} & \multicolumn{1}{y|}{\uparr 0.10} & \multicolumn{1}{y}{\uparr 0.09} & \multicolumn{1}{y}{\uparr 0.05} & \multicolumn{1}{y}{\uparr 0.09} \\ +\multicolumn{1}{l|}{TwHIN-BERT$_\text{Large}$} & 62.03 & 62.14 & 59.25 & 86.98 & 86.32 & 64.22 & 75.00 & 75.11 & 75.11 & 89.83 & 89.75 & 76.85 & 91.32 & 91.33 & 86.42 \\ +$\Delta$ & \multicolumn{1}{z}{\dwnarr 2.18} & \multicolumn{1}{z}{\dwnarr 1.15} & \multicolumn{1}{z|}{\dwnarr 1.87} & \multicolumn{1}{z}{\dwnarr 0.25} & \multicolumn{1}{z}{\dwnarr 0.46} & \multicolumn{1}{z|}{\dwnarr 1.01} & \multicolumn{1}{z}{\dwnarr 1.92} & \multicolumn{1}{z}{\dwnarr 1.72} & \multicolumn{1}{z|}{\dwnarr 1.72} & \multicolumn{1}{z}{\dwnarr 0.64} & \multicolumn{1}{z}{\dwnarr 0.67} & \multicolumn{1}{z|}{\dwnarr 0.43} & \multicolumn{1}{z}{\dwnarr 0.13} & \multicolumn{1}{z}{\dwnarr 0.14} & \multicolumn{1}{z}{\dwnarr 0.23} \\ +\hdashline +\multicolumn{1}{l|}{ViSoBERT $[\vardiamondsuit]$} & 66.52 & 67.02 & 64.55 & 87.32 & 87.12 & 66.98 & 76.25 & 75.98 & 75.98 & 89.72 & 89.69 & 77.95 & 91.58 & 91.53 & 86.72 \\ +$\Delta$ & \multicolumn{1}{z}{\dwnarr 1.58} & \multicolumn{1}{z}{\dwnarr 1.35} & \multicolumn{1}{z|}{\dwnarr 1.33} & \multicolumn{1}{z}{\dwnarr 1.19} & \multicolumn{1}{z}{\dwnarr 1.19} & \multicolumn{1}{z|}{\dwnarr 1.79} & \multicolumn{1}{z}{\dwnarr 1.58} & \multicolumn{1}{z}{\dwnarr 1.77} & \multicolumn{1}{z|}{\dwnarr 1.77} & \multicolumn{1}{z}{\dwnarr 1.27} & \multicolumn{1}{z}{\dwnarr 1.23} & \multicolumn{1}{z|}{\dwnarr 1.11} & \multicolumn{1}{z}{\dwnarr 0.04} & \multicolumn{1}{z}{\dwnarr 0.04} & \multicolumn{1}{z}{\dwnarr 0.08} \\ +\hline +\multicolumn{1}{l|}{ViSoBERT $[\spadesuit]$} & \bfseries 68.10 & \bfseries 68.37 & \bfseries 65.88 & \bfseries 88.51 & \bfseries 88.31 & \bfseries 68.77 & \bfseries 77.83 & \bfseries 77.75 & \bfseries 77.75 & \bfseries 90.99 & \bfseries 90.92 & \bfseries 79.06 & \bfseries 91.62 & \bfseries 91.57 & \bfseries 86.80 \\ +\hline +\end{tabular}} +\caption{Performances of pre-trained models on downstream Vietnamese social media tasks by applying two emojis pre-processing techniques. $[\clubsuit]$, $[\vardiamondsuit]$, and $[\spadesuit]$ denoted our pre-trained language model ViSoBERT converting emoji to text, removing emojis and without applying any pre-processing techniques, respectively. $\Delta$ denoted the increase ($\uparrow$) and the decrease ($\downarrow$) in performances of the PLMs compared to their competitors without applying any pre-processing techniques.} +\label{tab:emoji} +\end{table*} +% dạ phần emojis, hbua mình nhận xét đại ý là: Emojis quan trọng cho dữ liệu social media, tuy nhiên việc không decode được Emojis của PhoBERT làm giảm hiệu xuất của mô hình (do Emojis thì PhoBERT decode là token). Do đó khi convert về text hoặc xoá luôn emojis, PhoBERT produces better performances. Ngược lại, những mô hình decode được emojis thì reduce in performances. +% Giờ chạy lại em nhìn nhìn thì thấy có một số ý để nhận xét nữa thầy. Dạ là, khi so sánh performance khi convert về text vs xoá hẳn emojis thì đa phần là PhoBERT tốt hơn khi convert (này là do convert về text thì PhoBERT hiểu được ngữ cảnh của comment tốt hơn). +We conducted two experimental procedures to comprehensively investigate the importance of emojis, including converting emojis to general text and removing emojis. + +Table~\ref{tab:emoji} shows our detailed setting and experimental results on downstream tasks and pre-trained models. +The results indicate a moderate reduction in performance across all downstream tasks when emojis are removed or converted to text in our pre-trained ViSoBERT model. Our pre-trained decreases 0.62\% Acc, 0.55\% WF1, and 0.78\% MF1 on Average for downstream tasks while converting emojis to text. In addition, an average reduction of 1.33\% Acc, 1.32\% WF1, and 1.42\% MF1 can be seen in our pre-trained model while removing all emojis in each comment. This is because when emojis are converted to text, the context of the comment is preserved, while removing all emojis results in the loss of that context. + +This trend is also observed in the TwHIN-BERT model, specifically designed for social media processing. However, TwHIN-BERT slightly improves emotion recognition and spam reviews detection tasks compared to its competitors when operating on raw texts. Nevertheless, this improvement is marginal and insignificant, as indicated by the small increments of 0.61\%, 0.13\%, and 0.21\% in Acc, WF1, and MF1 on the emotion recognition task, respectively, and 0.08\% Acc, 0.05\% WF1, and 0.04\% MF1 on spam reviews detection task. One potential reason for this phenomenon is that TwHIN-BERT and ViSoBERT are PLMs trained on emojis datasets. Consequently, these models can comprehend the contextual meaning conveyed by emojis. This finding underscores the importance of emojis in social media texts. + +In contrast, there is a general trend of improved performance across a range of downstream tasks when removing or converting emojis to text on PhoBERT, the Vietnamese SOTA pre-trained language model. PhoBERT is a PLM on a general text (Vietnamese Wikipedia) dataset containing no emojis; therefore, when PhoBERT encounters an emoji, it treats it as an unknown token (see Table~\ref{tab:tokenizer} Appendix~\ref{app:exp}). Therefore, while applying emoji pre-processing techniques, including converting emoijs to text and removing emojis, PhoBERT produces better performances compared to raw text. + +% However, in the case of the sentiment analysis and hate speech spans detection tasks, PhoBERT exhibits a slight reduction in performance. Nonetheless, this reduction is minor, with decrements of 0.29\%/0.19\%, 0.28\%/0.54\%, and 0.14\%/0.21\% observed on Acc, WF1, and MF1, respectively. We argue this is because PhoBERT is a PLM on a general text (Vietnamese Wikipedia\footnote{\url{https://vi.wikipedia.org/wiki/}}) dataset containing no emojis. This leads to the fact that when PhoBERT encounters an emoji, it treats it as an unknown token (see Table~\ref{tab:tokenizer} Appendix~\ref{app:exp}). + +Our pre-trained model ViSoBERT on raw texts outperformed PhoBERT and TwHIN-BERT even when applying two pre-processing emojis techniques. This claims our pre-trained model's ability to handle Vietnamese social media raw texts. + +\textbf{Impact of Teencode on PLMs:} +% Để tiền xử lý được tốt, đòi hỏi người dùng phải thiết kế các rules phù hợp và tốn nhiều thời gian, để tăng một lượng % không đáng kể. +Due to informal and casual communication, social media texts often lead to common linguistic errors, such as misspellings and teencode. For example, the phrase ``ăng kơmmmmm'' should be ``ăn cơm'' (``Eat rice'' in English), and ``ko'' should be ``không'' (``No'' in English). To address this challenge, \citet{nguyen2020exploiting} presented several rules to standardize social media texts. Building upon the previous work, \citet{phobertcnn} proposed a strict and efficient pre-processing technique to clean comments on Vietnamese social media. + +Table~\ref{tab:teencode} (in Appendix~\ref{appendix:pre-process}) shows the results with and without standardizing teencode on social media texts. There is an uptrend across PhoBERT, TwHIN-BERT, and ViSoBERT while applying standardized pre-processing techniques. ViSoBERT, with standardized pre-processing techniques, outperforms almost downstream tasks but spam reviews detection. The possible reason is that the ViSpamReviews dataset contains samples in which users use the word with duplicated characters to improve the comment length while standardizing teencodes leads to misunderstanding. + +% The performances of PhoBERT and TwHIN-BERT increase while applying +%viet them xiu +Experimental results strongly suggest that the improvement achieved by applying complex pre-processing techniques to pre-trained models in the context of Vietnamese social media text is relatively insignificant. Despite the considerable time and effort invested in designing and implementing these techniques, the actual gains in PLMs performance are not substantial and unstable. + + +% See Appendix Table~\ref{tab:teencode} for more detailed experimental results. + +%The Vietnamese alphabet (Vietnamese: chữ Quốc ngữ, lit.'script of the National language') is the modern Latin writing script or writing system for Vietnamese. +% da tui em zia` nha thay :v +%OK + +\textbf{Impact of Vietnamese Diacritics on PLMs:} +Vietnamese words are created from 29 letters, including seven letters using four diacritics (ă, â-ê-ô, ơ-ư, and đ) and five diacritics used to designate tone (as in à, á, ả, ã, and ạ) \cite{ngo2020vietnamese}. These diacritics create meaningful words by combining syllables \cite{diacritics}. For instance, the syllable ``ngu'' can be combined with five different diacritic marks, resulting in five distinct syllables: ``ngú'', ``ngù'', ``ngụ'', ``ngủ'', and ``ngũ''. Each of these syllables functions as a standalone word. + +However, social media text does not always adhere to proper writing conventions. Due to various reasons, many users write text without diacritic marks when commenting on social media platforms. Consequently, effectively handling diacritics in Vietnamese social media becomes a critical challenge. To evaluate the PLMs' capability to address this challenge, we experimented by removing all diacritic marks from the datasets of five downstream tasks. This experiment aimed to assess the model's performance in processing text without diacritics and determine its ability to understand Vietnamese social media content in such cases. + +Table~\ref{tab:diacritics} (in Appendix~\ref{appendix:pre-process}) presents the results of the two best baselines compared to our pre-trained diacritics experiments. The experimental results reveal that the performance of all pre-trained models, including ours, exhibited a significant decrease when dealing with social media comments lacking diacritics. This decline in performance can be attributed to the loss of contextual information caused by the removal of diacritics. The lower the percentage of diacritic removal in each comment, the more significant the performance improvement in all PLMs. However, our ViSoBERT demonstrated a relatively minor reduction in performance across all downstream tasks. This suggests that our model possesses a certain level of robustness and adaptability in comprehending and analyzing Vietnamese social media content without diacritics. We attribute this to the efficiency of the in-domain pre-training data of ViSoBERT. + +In contrast, PhoBERT and TwHIN-BERT experienced a substantial drop in performance across the benchmark datasets. These PLMs struggled to cope with the absence of diacritics in Vietnamese social media comments. The main reason is that the tokenizer of PhoBERT can not encode non-diacritics comments due to not including those in pre-training data. Several tokenized examples of the three best PLMs are presented in Table~\ref{tab:diacriticscomments} (in Appendix~\ref{removingdiacritics}). Thus, the significant decrease in its performance highlights the challenge of handling diacritics on Vietnamese social media. While handling diacritics remains challenging, ViSoBERT demonstrates promising performance, suggesting the potential for specialized language models tailored for Vietnamese social media analysis. + +% 22h toi viet sau :v +%Viết đoạn bình thường thôi, ko cần tiêu đề +%\textbf{Cai nay em muon conclusion phan social media characteristics nhung ma khong biet dat ten gi:} +%In summary, despite the extensive efforts dedicated to pre-processing techniques for Vietnamese social media text, the improvements achieved by these techniques on existing Vietnamese PLMs are limited. Furthermore, the performance of these models, even with pre-processing, falls short of the performance exhibited by our pre-trained ViSoBERT model implemented directly to raw Vietnamese social media texts. This substantiates the superiority of our model in effectively handling and interpreting Vietnamese social media texts, positioning it as a reliable and powerful PLM for various NLP Vietnamese social media tasks. + +\subsection{Impact of Feature-based Extraction to Task-Specific Models} \label{feature-based} + +In task-specific models, the contextualized word embeddings from PLMs are typically employed as input features. We aim to assess the quality of contextualized word embeddings generated by PhoBERT, TwHIN-BERT, and ViSoBERT to verify whether social media data can enhance word representation. These contextualized word embeddings are applied as embedding features to BiLSTM, and BiGRU is randomly initialized before the classification layer. We append a linear prediction layer to the last transformer layer of each PLM regarding the first subword of each word token, which is similar to \citet{bert}. + +% The results are reported in Table~\ref{tab:feature-based} (in Appendix~\ref{appendix:pre-process}). +Our experiment results (see Table~\ref{tab:feature-based} in Appendix~\ref{appendix:pre-process}) demonstrate that the word embeddings generated by our pre-trained language model ViSoBERT outperform other pre-trained embeddings when utilized with BiLSTM and BiGRU for all downstream tasks. The experimental results indicate the significant impact of leveraging social media text data for enriching word embeddings. Furthermore, this finding underscores the effectiveness of our model in capturing the linguistic characteristics prevalent in Vietnamese social media texts. + +Figure~\ref{fig:perepoch} (in Appendix~\ref{plm-based}) presents the performances of the PLMs as input features to BiLSTM and BiGRU on the dev set per epoch in terms of MF1. The results demonstrate that ViSoBERT reaches its peak MF1 score in only 1 to 3 epochs, whereas other PLMs typically require an average of 8 to 10 epochs to achieve on-par performance. This suggests that ViSoBERT has a superior capability to extract Vietnamese social media information compared to other models. + +% For the Hate Speech Spans Detection task, we implemented BiLSTM-CRF and BiGRU-CRF. Following \cite{bert}, for Social Span Detection task, we append a Linear prediction layer to the last Transformer layer of ViSoBERT regarding the first subword of each word token. + +%Các công trình cho thấy kiến trúc CNN tốt hơn LSTM trên dữ liệu Vietnamese social media text +%https://arxiv.org/pdf/1911.09339.pdf +%https://link.springer.com/article/10.1007/s00521-022-07745-w +%https://aclanthology.org/2020.paclic-1.48.pdf + +\section{Conclusion and Future Work} +We presented ViSoBERT, a novel large-scale monolingual pre-trained language model on Vietnamese social media texts. We illustrated that ViSoBERT with fewer parameters outperforms recent strong pre-trained language models such as viBERT, vELECTRA, PhoBERT, XLM-R, XLM-T, TwHIN-BERT, and Bernice and achieves state-of-the-art performances for multiple downstream Vietnamese social media tasks, including emotion recognition, hate speech detection, spam reviews detection, and hate speech spans detection. We conducted extensive analyses to demonstrate the efficiency of ViSoBERT on various Vietnamese social media characteristics, including emojis, teencodes, and diacritics. Furthermore, our pre-trained language model ViSoBERT also shows the potential of leveraging Vietnamese social media text to enhance word representations compared to other PLMs. We hope the widespread use of our open-source ViSoBERT pre-trained language model will advance current NLP social media tasks and applications for Vietnamese. Other low-resource languages can adopt how to create PLMs for enhancing their current NLP social media tasks and relevant applications. + +\newpage +\section*{Limitations} +While we have demonstrated that ViSoBERT can perform state-of-the-art on a range of NLP social media tasks for Vietnamese, we think additional analyses and experiments are necessary to fully comprehend what aspects of ViSoBERT were responsible for its success and what understanding of Vietnamese social media texts ViSoBERT captures. We leave these additional investigations to future research. Moreover, future work aims to explore a broader range of Vietnamese social media downstream tasks that this paper may not cover. Also, we chose to train the base-size transformer model instead of the \textit{Large} variant because base models are more accessible due to their lower computational requirements. For PhoBERT, XLM-R, and TwHIN-BERT, we implemented two versions \textit{Base} and \textit{Large} for all Vietnamese social media downstream tasks. However, it is not a fair comparison due to their significantly larger model configurations. Moreover, regular updates and expansions of the pre-training data are essential to keep up with the rapid evolution of social media. This allows the pre-trained model to adapt effectively to the dynamic linguistic patterns and trends in Vietnamese social media. + +% limit on the range of social media tasks +% extend pre-training data + +\section*{Ethics Statement} +The authors introduce ViSoBERT, a pre-trained language model for investigating social language phenomena in social media in Vietnamese. ViSoBERT is based on pre-training an existing pre-trained language model (i.e., XLM-R), which lessens the influence of its construction on the environment. ViSoBERT makes use of a large-scale corpus of posts and comments from social communities that have been found to express harassment, bullying, incitement of violence, hate, offense, and abuse, as defined by the content policies of social media platforms, including Facebook, YouTube, and TikTok. + +%Sau khi accept mới open ra +\section*{Acknowledgement} +This research was supported by The VNUHCM-University of Information Technology’s Scientific Research Support Fund. We thank the anonymous EMNLP reviewers for their time and helpful suggestions that improved the quality of the paper. +%\section*{Acknowledgement} +%This research was supported by The VNUHCM-University of Information Technology's Scientific Research Support Fund. + + + + +\bibliography{anthology} +\bibliographystyle{acl_natbib} + +\newpage +% \input{emnlp2023-latex/Sections/7-Appendix} +\onecolumn +\appendix + +\section{Tokenizations of the PLMs on Social Comments} +\label{app:socialtok} + +% To assess the tokenized ability of Vietnamese social media textual data, we conducted an analysis on several data samples. Table \ref{tab:tokenizer} shows several actual social comments and their tokenizations with the tokenizers of the two pre-trained language models, ViSoBERT (ours) and PhoBERT (considered as the best strong baseline). + +% \begin{table*}[!ht] +% \centering +% \resizebox{\textwidth}{!}{ +% \begin{tabular}{l|l|l} +% \hline +% \textbf{Comments} & \multicolumn{1}{c|}{\textbf{ViSoBERT}} & \multicolumn{1}{c}{\textbf{PhoBERT}} \\ +% \hline +% \begin{tabular}[c]{@{}l@{}}concặc cáilồn gìđây\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\\\textit{English}: Wut is dis fuckingd1ck \includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\end{tabular} & \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{},~"conc", "ặc", "cái", "l", "ồn", "gì", "đây", \\"\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}", \textless{}/s\textgreater{}\end{tabular} & \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "c o n @ @", "c @ @", "ặ c", "c á @ @", \\"i l @ @", "ồ n", "g @ @","ì @ @", "đ â y", \\ \textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \textless{}/s\textgreater{}\end{tabular} \\ +% \hline +% \begin{tabular}[c]{@{}l@{}}e cảmơn anh\includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}\includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}\\\textit{English}: Thankyou \includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}\includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}\end{tabular} & \textless{}s\textgreater{}, "e", "cảm", "ơn", "anh", "\includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}", "\includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}", \textless{}/s\textgreater{} & \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{},~"e", "c ả @ @", "m @ @", "ơ n", "a n h", \\, , \textless{}/s\textgreater{} \end{tabular}\\ +% \hline +% \begin{tabular}[c]{@{}l@{}}d4y l4 vj du cko mot cau teencode\\\textit{English}: Th1s 1s 4 teencode s3nt3nc3\end{tabular} & \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "d", "4", "y", "l", "4", "vj", "du", "cko", "mot", \\"cau", "teen", "code", \textless{}/s\textgreater{}\end{tabular} & \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "d @ @", "4 @ @", "y", "l @ @", "4", \\"v @ @", "j", "d u", "c k @ @", "o", "m o @ @", \\"t", "c a u"; "t e @ @", "e n @ @", "c o d e", \textless{}/s\textgreater{}\end{tabular} \\ +% \hline +% \end{tabular}} +% \caption{Actual social comments and their tokenizations with the tokenizers of the two pre-trained language models, ViSoBERT and PhoBERT. \textbf{Disclaimer}: This table contains actual comments on social networks that might be construed as abusive, offensive, or obscene.} +% \label{tab:tokenizer} +% \end{table*} + + + + +We conducted an analysis of average token length by tasks of Pre-trained Language Models to provide insights into how different PLMs perform regarding token length across various Vietnamese social media downstream tasks. Figure~\ref{fig:tokenlen} shows the average token length by Vietnamese social media downstream tasks of baseline PLMs and ours. + +\begin{figure*}[!h] + \centering + \includegraphics[width=\textwidth]{token_length11111-crop.pdf} + \caption{Average token length by tasks of PLMs.} + \label{fig:tokenlen} +\end{figure*} + +\section{Experimental Settings} +\label{app:exp} + +Following the hyperparameters in Table \ref{tab:hyperparameters}, we train our pre-trained language model ViSoBERT for Vietnamese social media texts. +\begin{table}[!ht] +\centering +\begin{tabular}{llr} +\hline +\multirow{7}{*}{Optimizer} & Algorithm & Adam \\ + & Learning rate & 5e-5 \\ + & Epsilon & 1e-8 \\ + & LR scheduler & linear decay and warmup \\ + & Warmup steps & 1000 \\ + & Betas & 0.9 and 0.99 \\ + & Weight decay & 0.01 \\ +\hline +\multirow{3}{*}{Batch} & Sequence length & 128 \\ + & Batch size & 128 \\ + & Vocab size & 15002 \\ +\hline +\multirow{2}{*}{Misc} & Dropout & 0.1 \\ + & Attention dropout & 0.1 \\ +\hline +\end{tabular} +\caption{All hyperparameters established for training ViSoBERT.} +\label{tab:hyperparameters} +\end{table} +\newpage + +\section{PLMs with Pre-processing Techniques} +\label{appendix:pre-process} + +For an in-depth understanding of the impact of social media texts on PLMs, we conducted an analysis of the test results on various processing aspects. Table~\ref{tab:teencode} presents performances of the pre-trained language models on downstream Vietnamese social media tasks by applying word standardizing pre-processing techniques, while Table~\ref{tab:diacritics} presents performances of the pre-trained language models on downstream Vietnamese social media tasks by removing diacritics in all datasets. + +\begin{table*}[!ht] +\centering +\resizebox{\textwidth}{!}{ +\begin{tabular}{c|www|www|www|www|S[table-column-width=1.1cm,table-format=2.2,table-space-text-pre=\dwnarr,color=black]S[table-column-width=1.8cm,table-format=2.2,table-space-text-pre=\dwnarr,color=black]S[table-column-width=1.1cm,table-format=2.2,table-space-text-pre=\dwnarr,color=black]} +\hline +\multirow{2}{*}{\textbf{Model}} & \multicolumn{3}{c|}{\textbf{Emotion Recognition}} & \multicolumn{3}{c|}{\textbf{Hate Speech Detection}} & \multicolumn{3}{c|}{\textbf{Sentiment Analysis}} & \multicolumn{3}{c|}{\textbf{Spam Reviews Detection}} & \multicolumn{3}{c}{\textbf{Hate Speech Spans Detection}} \\ +\cline{2-16} + & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} \\ +\hline +\multicolumn{1}{l|}{PhoBERT$_\text{Large}$} & 64.94 & 64.85 & 62.71 & 87.68 & 87.25 & 65.41 & 76.80 & 76.61 & 76.61 & 89.47 & 89.41 & 76.12 & 91.73 & 91.62 & 86.59 \\ +$\Delta$ & \multicolumn{1}{y}{\uparr 0.23} & \multicolumn{1}{y}{\uparr 0.19} & \multicolumn{1}{y|}{\uparr 0.16} & \multicolumn{1}{y}{\uparr 0.36} & \multicolumn{1}{y}{\uparr 0.27} & \multicolumn{1}{y|}{\uparr 0.27} & \multicolumn{1}{y}{\uparr 0.28} & \multicolumn{1}{y}{\uparr 0.25} & \multicolumn{1}{y|}{\uparr 0.25} & \multicolumn{1}{z}{\dwnarr 0.65} & \multicolumn{1}{z}{\dwnarr 0.62} & \multicolumn{1}{z|}{\dwnarr 0.76} & \multicolumn{1}{y}{\uparr 0.29} & \multicolumn{1}{y}{\uparr 0.16} & \multicolumn{1}{y}{\uparr 0.03} \\ +\multicolumn{1}{l|}{TwHIN-BERT$_\text{Large}$} & 64.42 & 64.46 & 61.28 & 87.82 & 87.28 & 65.68 & 77.17 & 76.94 & 76.94 & 89.49 & 89.43 & 76.35 & 91.74 & 91.64 & 86.67 \\ +$\Delta$ & \multicolumn{1}{y}{\uparr 0.21} & \multicolumn{1}{y}{\uparr 0.17} & \multicolumn{1}{y|}{\uparr 0.16} & \multicolumn{1}{y}{\uparr 0.59} & \multicolumn{1}{y}{\uparr 0.50} & \multicolumn{1}{y|}{\uparr 0.45} & \multicolumn{1}{y}{\uparr 0.25} & \multicolumn{1}{y}{\uparr 0.11} & \multicolumn{1}{y|}{\uparr 0.11} & \multicolumn{1}{z}{\dwnarr 0.98} & \multicolumn{1}{z}{\dwnarr 0.99} & \multicolumn{1}{z|}{\dwnarr 0.93} & \multicolumn{1}{y}{\uparr 0.29} & \multicolumn{1}{y}{\uparr 0.17} & \multicolumn{1}{y}{\uparr 0.02} \\ +\hdashline +\multicolumn{1}{l|}{ViSoBERT [$\clubsuit$]} & \bfseries 68.25 & \bfseries 68.52 & \bfseries 65.94 & \bfseries 88.53 & \bfseries 88.33 & \bfseries 68.82 & \bfseries 78.01 & \bfseries 77.88 & \bfseries 77.88 & 90.83 & 90.75 & 78.77 & \bfseries 91.89 & \bfseries 91.82 & \bfseries 86.93 \\ +$\Delta$ & \multicolumn{1}{y}{\uparr 0.15} & \multicolumn{1}{y}{\uparr 0.15} & \multicolumn{1}{y|}{\uparr 0.06} & \multicolumn{1}{y}{\uparr 0.02} & \multicolumn{1}{y}{\uparr 0.02} & \multicolumn{1}{y|}{\uparr 0.08} & \multicolumn{1}{y}{\uparr 0.18} & \multicolumn{1}{y}{\uparr 0.13} & \multicolumn{1}{y|}{\uparr 0.13} & \multicolumn{1}{z}{\dwnarr 0.16} & \multicolumn{1}{z}{\dwnarr 0.17} & \multicolumn{1}{z|}{\dwnarr 0.29} & \multicolumn{1}{y}{\uparr 0.27} & \multicolumn{1}{y}{\uparr 0.25} & \multicolumn{1}{y}{\uparr 0.13} \\ +\hline +\multicolumn{1}{l|}{ViSoBERT [$\vardiamondsuit$]} & 68.10 & 68.37 & 65.88 & 88.51 & 88.31 & 68.74 & 77.83 & 77.75 & 77.75 & \bfseries 90.99 & \bfseries 90.92 & \bfseries 79.06 & 91.62 & 91.57 & 86.80 \\ +\hline +\end{tabular}} +\caption{Performances of the pre-trained language models on downstream Vietnamese social media tasks by applying word standardizing pre-processing techniques. $[\clubsuit] \text{ and } [\vardiamondsuit]$ denoted with and without standardizing word technique, respectively. $\Delta$ denoted the increase ($\uparrow$) and the decrease ($\downarrow$) in performances of the pre-trained language models compared to its competitors without normalizing teencodes.} +\label{tab:teencode} +\end{table*} + +To emphasize the essentials of diacritics, we conducted an analysis on several data samples by removing 100\%, 75\%, 50\%, and 25\% diacritics of total words that included diacritics in each comment of five downstream tasks. Table~\ref{tab:diacritics} presents performances of the pre-trained language models on downstream Vietnamese social media tasks by removing diacritics in all datasets. + +\begin{table*}[!ht] +\centering +\resizebox{\textwidth}{!}{ +\begin{tabular}{c|zzz|zzz|zzz|zzz|S[table-column-width=1.1cm,table-format=2.2,table-space-text-pre=\dwnarr,color=red]S[table-column-width=1.8cm,table-format=2.2,table-space-text-pre=\dwnarr,color=red]S[table-column-width=1.1cm,table-format=2.2,table-space-text-pre=\dwnarr,color=red]} +\hline +\multirow{2}{*}{\textbf{Model}} & \multicolumn{3}{c|}{\textbf{Emotion Regconition}} & \multicolumn{3}{c|}{\textbf{Hate Speech Detection}} & \multicolumn{3}{c|}{\textbf{Sentiment Analysis}} & \multicolumn{3}{c|}{\textbf{Spam Reviews Detection}} & \multicolumn{3}{c}{\textbf{Hate Speech Spans Detection}} \\ +\cline{2-16} + & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} \\ +\hline +\multicolumn{16}{c}{\textbf{\textit{Removing 100\% diacritics in each comment}}} \\ +\hline +\multicolumn{1}{l|}{PhoBERT$_\text{Large}$} & \multicolumn{1}{w}{49.35} & \multicolumn{1}{w}{49.18} & \multicolumn{1}{w|}{43.95} & \multicolumn{1}{w}{81.25} & \multicolumn{1}{w}{81.42} & \multicolumn{1}{w|}{55.43} & \multicolumn{1}{w}{62.38} & \multicolumn{1}{w}{62.36} & \multicolumn{1}{w|}{62.36} & \multicolumn{1}{w}{87.68} & \multicolumn{1}{w}{87.56} & \multicolumn{1}{w|}{71.89} & \multicolumn{1}{w}{91.32} & \multicolumn{1}{w}{91.37} & \multicolumn{1}{w}{86.43} \\ +$\Delta$ & \dwnarr 15.36 & \dwnarr 15.48 & \dwnarr 18.60 & \dwnarr \color{red} 6.07 & \dwnarr 5.56 & \dwnarr 9.71 & \dwnarr 14.14 & \dwnarr 14.00 & \dwnarr 13.86 & \dwnarr 2.44 & \dwnarr 2.47 & \dwnarr 4.99 & \dwnarr 0.12 & \dwnarr 0.09 & \dwnarr 0.13 \\ +\multicolumn{1}{l|}{TwHIN-BERT$_\text{Large}$} & \multicolumn{1}{w}{49.32} & \multicolumn{1}{w}{49.15} & \multicolumn{1}{w|}{43.52} & \multicolumn{1}{w}{84.25} & \multicolumn{1}{w}{78.48} & \multicolumn{1}{w|}{51.32} & \multicolumn{1}{w}{66.66} & \multicolumn{1}{w}{66.68} & \multicolumn{1}{w|}{66.68} & \multicolumn{1}{w}{89.45} & \multicolumn{1}{w}{89.26} & \multicolumn{1}{w|}{74.59} & \multicolumn{1}{w}{91.12} & \multicolumn{1}{w}{91.22} & \multicolumn{1}{w}{86.33} \\ +$\Delta$ & \dwnarr 14.89 & \dwnarr 15.14 & \dwnarr 17.60 & \dwnarr 2.98 & \dwnarr 8.30 & \dwnarr 12.99 & \dwnarr 10.26 & \dwnarr 10.15 & \dwnarr 10.15 & \dwnarr 1.02 & \dwnarr 1.16 & \dwnarr 2.69 & \dwnarr 0.33 & \dwnarr 0.25 & \dwnarr 0.32 \\ \hdashline +\multicolumn{1}{l|}{ViSoBERT $[\clubsuit]$} & \multicolumn{1}{w}{61.96} & \multicolumn{1}{w}{62.05} & \multicolumn{1}{w|}{58.48} & \multicolumn{1}{w}{87.29} & \multicolumn{1}{w}{86.76} & \multicolumn{1}{w|}{64.87} & \multicolumn{1}{w}{72.95} & \multicolumn{1}{w}{72.91} & \multicolumn{1}{w|}{72.91} & \multicolumn{1}{w}{89.75} & \multicolumn{1}{w}{89.72} & \multicolumn{1}{w|}{76.12} & \multicolumn{1}{w}{91.48} & \multicolumn{1}{w}{91.42} & \multicolumn{1}{w}{86.69} \\ +$\Delta$ & \dwnarr 6.14 & \dwnarr 6.32 & \dwnarr 7.40 & \dwnarr 1.22 & \dwnarr 1.55 & \dwnarr 3.90 & \dwnarr 4.88 & \dwnarr 4.84 & \dwnarr 4.84 & \dwnarr 1.24 & \dwnarr 1.20 & \dwnarr 2.94 & \dwnarr 0.14 & \dwnarr 0.15 & \dwnarr 0.11 \\ +\hline +\multicolumn{16}{c}{\textbf{\textit{Removing 75\% diacritics in each comment}}} \\ +\hline +\multicolumn{1}{l|}{PhoBERT$_\text{Large}$} & \multicolumn{1}{w}{51.94} & \multicolumn{1}{w}{51.79} & \multicolumn{1}{w|}{47.79} & \multicolumn{1}{w}{84.74} & \multicolumn{1}{w}{84.03} & \multicolumn{1}{w|}{58.37} & \multicolumn{1}{w}{66.00} & \multicolumn{1}{w}{65.98} & \multicolumn{1}{w|}{65.98} & \multicolumn{1}{w}{88.23} & \multicolumn{1}{w}{88.12} & \multicolumn{1}{w|}{72.42} & \multicolumn{1}{w}{90.38} & \multicolumn{1}{w}{90.23} & \multicolumn{1}{w}{85.42} \\ +$\Delta$ & \dwnarr 12.77 & \dwnarr 12.87 & \dwnarr 14.76 & \dwnarr 2.58 & \dwnarr 2.95 & \dwnarr 6.77 & \dwnarr 10.52 & \dwnarr 10.38 & \dwnarr 10.24 & \dwnarr 1.89 & \dwnarr 1.91 & \dwnarr 4.46 & \dwnarr 1.06 & \dwnarr 1.23 & \dwnarr 1.14 \\ +\multicolumn{1}{l|}{TwHIN-BERT$_\text{Large}$} & \multicolumn{1}{w}{51.32} & \multicolumn{1}{w}{51.17} & \multicolumn{1}{w|}{44.63} & \multicolumn{1}{w}{83.22} & \multicolumn{1}{w}{81.42} & \multicolumn{1}{w|}{52.24} & \multicolumn{1}{w}{67.23} & \multicolumn{1}{w}{67.32} & \multicolumn{1}{w|}{67.32} & \multicolumn{1}{w}{89.12} & \multicolumn{1}{w}{88.95} & \multicolumn{1}{w|}{75.20} & \multicolumn{1}{w}{90.62} & \multicolumn{1}{w}{89.93} & \multicolumn{1}{w}{85.81} \\ +$\Delta$ & \dwnarr 12.89 & \dwnarr 13.12 & \dwnarr 16.49 & \dwnarr 4.01 & \dwnarr 5.36 & \dwnarr 12.99 & \dwnarr 9.69 & \dwnarr 9.51 & \dwnarr 9.51 & \dwnarr 1.35 & \dwnarr 1.47 & \dwnarr 2.08 & \dwnarr 0.83 & \dwnarr 1.54 & \dwnarr 0.84 \\ +\hdashline +\multicolumn{1}{l|}{ViSoBERT $[\spadesuit]$} & \multicolumn{1}{w}{62.34} & \multicolumn{1}{w}{62.26} & \multicolumn{1}{w|}{58.13} & \multicolumn{1}{w}{87.35} & \multicolumn{1}{w}{86.88} & \multicolumn{1}{w|}{65.12} & \multicolumn{1}{w}{73.90} & \multicolumn{1}{w}{73.97} & \multicolumn{1}{w|}{73.97} & \multicolumn{1}{w}{90.41} & \multicolumn{1}{w}{90.31} & \multicolumn{1}{w|}{76.17} & \multicolumn{1}{w}{91.02} & \multicolumn{1}{w}{91.17} & \multicolumn{1}{w}{86.02} \\ +$\Delta$ & \dwnarr 5.76 & \dwnarr 6.11 & \dwnarr 7.75 & \dwnarr 1.16 & \dwnarr 1.43 & \dwnarr 3.65 & \dwnarr 3.93 & \dwnarr 3.78 & \dwnarr 3.78 & \dwnarr 0.58 & \dwnarr 0.61 & \dwnarr 2.89 & \dwnarr 0.60 & \dwnarr 0.40 & \dwnarr 0.78 \\ +\hline +\multicolumn{16}{c}{\textbf{\textit{Removing 50\% diacritics in each comment}}} \\ +\hline +\multicolumn{1}{l|}{PhoBERT$_\text{Large}$} & \multicolumn{1}{w}{57.28} & \multicolumn{1}{w}{57.36} & \multicolumn{1}{w|}{54.02} & \multicolumn{1}{w}{85.29} & \multicolumn{1}{w}{84.71} & \multicolumn{1}{w|}{59.40} & \multicolumn{1}{w}{66.57} & \multicolumn{1}{w}{66.46} & \multicolumn{1}{w|}{66.46} & \multicolumn{1}{w}{89.02} & \multicolumn{1}{w}{88.81} & \multicolumn{1}{w|}{73.10} & \multicolumn{1}{w}{90.42} & \multicolumn{1}{w}{90.47} & \multicolumn{1}{w}{85.62} \\ +$\Delta$ & \dwnarr 7.43 & \dwnarr 7.30 & \dwnarr 8.53 & \dwnarr 2.03 & \dwnarr 2.27 & \dwnarr 5.74 & \dwnarr 9.95 & \dwnarr 9.90 & \dwnarr 9.76 & \dwnarr 1.10 & \dwnarr 1.22 & \dwnarr 3.78 & \dwnarr 1.02 & \dwnarr 0.99 & \dwnarr 0.94 \\ +\multicolumn{1}{l|}{TwHIN-BERT$_\text{Large}$} & \multicolumn{1}{w}{53.70} & \multicolumn{1}{w}{53.39} & \multicolumn{1}{w|}{49.55} & \multicolumn{1}{w}{83.41} & \multicolumn{1}{w}{83.31} & \multicolumn{1}{w|}{55.22} & \multicolumn{1}{w}{70.42} & \multicolumn{1}{w}{70.53} & \multicolumn{1}{w|}{70.53} & \multicolumn{1}{w}{89.33} & \multicolumn{1}{w}{89.05} & \multicolumn{1}{w|}{75.32} & \multicolumn{1}{w}{90.73} & \multicolumn{1}{w}{90.12} & \multicolumn{1}{w}{85.92} \\ +$\Delta$ & \dwnarr 10.51 & \dwnarr 10.90 & \dwnarr 11.57 & \dwnarr 3.82 & \dwnarr 3.47 & \dwnarr 10.01 & \dwnarr 6.50 & \dwnarr 6.30 & \dwnarr 6.30 & \dwnarr 1.14 & \dwnarr 1.37 & \dwnarr 1.96 & \dwnarr 0.72 & \dwnarr 1.35 & \dwnarr 0.73 \\ +\hdashline +\multicolumn{1}{l|}{ViSoBERT $[\varheartsuit]$} & \multicolumn{1}{w}{62.96} & \multicolumn{1}{w}{62.87} & \multicolumn{1}{w|}{60.55} & \multicolumn{1}{w}{87.44} & \multicolumn{1}{w}{87.10} & \multicolumn{1}{w|}{65.25} & \multicolumn{1}{w}{74.76} & \multicolumn{1}{w}{74.72} & \multicolumn{1}{w|}{74.72} & \multicolumn{1}{w}{90.41} & \multicolumn{1}{w}{90.35} & \multicolumn{1}{w|}{77.31} & \multicolumn{1}{w}{91.12} & \multicolumn{1}{w}{91.24} & \multicolumn{1}{w}{86.22} \\ +$\Delta$ & \dwnarr 5.14 & \dwnarr 5.50 & \dwnarr 5.33 & \dwnarr 1.07 & \dwnarr 1.21 & \dwnarr 3.52 & \dwnarr 3.07 & \dwnarr 3.03 & \dwnarr 3.03 & \dwnarr 0.58 & \dwnarr 0.57 & \dwnarr 1.75 & \dwnarr 0.50 & \dwnarr 0.33 & \dwnarr 0.58 \\ +\hline +\multicolumn{16}{c}{\textbf{\textit{Removing 25\% diacritics in each comment}}} \\ +\hline +\multicolumn{1}{l|}{PhoBERT$_\text{Large}$} & \multicolumn{1}{w}{61.03} & \multicolumn{1}{w}{60.80} & \multicolumn{1}{w|}{57.87} & \multicolumn{1}{w}{85.97} & \multicolumn{1}{w}{85.51} & \multicolumn{1}{w|}{61.96} & \multicolumn{1}{w}{73.42} & \multicolumn{1}{w}{73.28} & \multicolumn{1}{w|}{73.28} & \multicolumn{1}{w}{89.80} & \multicolumn{1}{w}{89.59} & \multicolumn{1}{w|}{75.53} & \multicolumn{1}{w}{90.63} & \multicolumn{1}{w}{90.69} & \multicolumn{1}{w}{85.76} \\ +$\Delta$ & \dwnarr 3.68 & \dwnarr 3.86 & \dwnarr 4.68 & \dwnarr 1.35 & \dwnarr 1.47 & \dwnarr 3.18 & \dwnarr 3.10 & \dwnarr 3.08 & \dwnarr 2.94 & \dwnarr 0.32 & \dwnarr 0.44 & \dwnarr 1.35 & \dwnarr 0.81 & \dwnarr 0.77 & \dwnarr 0.80 \\ +\multicolumn{1}{l|}{TwHIN-BERT$_\text{Large}$} & \multicolumn{1}{w}{61.18} & \multicolumn{1}{w}{60.98} & \multicolumn{1}{w|}{57.42} & \multicolumn{1}{w}{86.85} & \multicolumn{1}{w}{86.13} & \multicolumn{1}{w|}{63.14} & \multicolumn{1}{w}{73.21} & \multicolumn{1}{w}{73.11} & \multicolumn{1}{w|}{73.11} & \multicolumn{1}{w}{89.91} & \multicolumn{1}{w}{89.43} & \multicolumn{1}{w|}{76.32} & \multicolumn{1}{w}{91.09} & \multicolumn{1}{w}{90.72} & \multicolumn{1}{w}{86.02} \\ +$\Delta$ & \dwnarr 3.03 & \dwnarr 3.31 & \dwnarr 3.70 & \dwnarr 0.38 & \dwnarr 0.65 & \dwnarr 2.09 & \dwnarr 3.71 & \dwnarr 3.72 & \dwnarr 3.72 & \dwnarr 0.56 & \dwnarr 0.99 & \dwnarr 0.96 & \dwnarr 0.36 & \dwnarr 0.75 & \dwnarr 0.63 \\ +\hdashline +\multicolumn{1}{l|}{ViSoBERT $[\maltese]$} & \multicolumn{1}{w}{64.64} & \multicolumn{1}{w}{64.53} & \multicolumn{1}{w|}{61.29} & \multicolumn{1}{w}{87.85} & \multicolumn{1}{w}{87.56} & \multicolumn{1}{w|}{66.54} & \multicolumn{1}{w}{75.42} & \multicolumn{1}{w}{75.44} & \multicolumn{1}{w|}{75.44} & \multicolumn{1}{w}{90.76} & \multicolumn{1}{w}{90.64} & \multicolumn{1}{w|}{78.15} & \multicolumn{1}{w}{91.22} & \multicolumn{1}{w}{91.24} & \multicolumn{1}{w}{86.47} \\ +$\Delta$ & \dwnarr 3.43 & \dwnarr 3.84 & \dwnarr 4.59 & \dwnarr 0.66 & \dwnarr 0.75 & \dwnarr 2.23 & \dwnarr 2.41 & \dwnarr 2.31 & \dwnarr 2.31 & \dwnarr 0.23 & \dwnarr 0.28 & \dwnarr 0.91 & \dwnarr 0.40 & \dwnarr 0.33 & \dwnarr 0.33 \\ +\hline +\multicolumn{1}{l|}{ViSoBERT $[\vardiamondsuit]$} & \multicolumn{1}{w}{\bfseries 68.10} & \multicolumn{1}{w}{\bfseries 68.37} & \multicolumn{1}{w|}{\bfseries 65.88} & \multicolumn{1}{w}{\bfseries 88.51} & \multicolumn{1}{w}{\bfseries 88.31} & \multicolumn{1}{w|}{\bfseries 68.77} & \multicolumn{1}{w}{\bfseries 77.83} & \multicolumn{1}{w}{\bfseries 77.75} & \multicolumn{1}{w|}{\bfseries 77.75} & \multicolumn{1}{w}{\bfseries 90.99} & \multicolumn{1}{w}{\bfseries 90.92} & \multicolumn{1}{w|}{\bfseries 79.06} & \multicolumn{1}{w}{\bfseries 91.62} & \multicolumn{1}{w}{\bfseries 91.57} & \multicolumn{1}{w}{\bfseries 86.80} \\ +\hline +\end{tabular}} +\caption{Performances of the pre-trained language models on downstream Vietnamese social media tasks by removing diacritics in all datasets. $[\clubsuit]$, $[\spadesuit]$, $[\varheartsuit]$, $[\maltese]$ and $[\vardiamondsuit]$ denoted the performances of our pre-trained on removing 100\%, 75\%, 50\%, 25\% in each comment, respectively, and not removing diacritics marks dataset, respectively. $\Delta$ denoted the increase ($\uparrow$) and the decrease ($\downarrow$) in performances of the pre-trained language models compared to its competitors without removing diacritics marks.} +\label{tab:diacritics} +\end{table*} +% cai bang to qua thay :v +% nhìn vào người ta accept liền :D + +\newpage +\section{PLM-based Features for BiLSTM and BiGRU} +\label{plm-based} + +We conduct experiments with various models, including BiLSTM and BiGRU, to understand better the word embedding feature extracted from the pre-trained language models. Table \ref{tab:feature-based} shows performances of the pre-trained language model as input features to BiLSTM and BiGRU on downstream Vietnamese social media tasks. + +\begin{table*}[!ht] +\centering +\resizebox{\textwidth}{!}{ +\begin{tabular}{l|ccc|ccc|ccc|>{\centering\arraybackslash}m{1.1cm}>{\centering\arraybackslash}m{1.1cm}>{\centering\arraybackslash}m{1.1cm}|>{\centering\arraybackslash}m{1.1cm}>{\centering\arraybackslash}m{1.8cm}>{\centering\arraybackslash}m{1.1cm}} +\hline +\multicolumn{1}{c|}{\multirow{2}{*}{\textbf{Model}}} & \multicolumn{3}{c|}{\textbf{Emotion Regconition}} & \multicolumn{3}{c|}{\textbf{Hate Speech Detection}} & \multicolumn{3}{c|}{\textbf{Sentiment Analysis}} & \multicolumn{3}{c|}{\textbf{Spam Reviews Detection}} & \multicolumn{3}{c}{\textbf{Hate Speech Spans Detection}} \\ +\cline{2-16} +\multicolumn{1}{c|}{} & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} \\ +\hline +\multicolumn{16}{c}{\textbf{\textit{BiLSTM}}} \\ +\hline +PhoBERT$_\text{Large}$ & 57.58 & 56.65 & 50.55 & 86.11 & 84.04 & 56.03 & 69.71 & 69.70 & 69.70 & 87.80 & 87.10 & 68.95 & 84.01 & 80.70 & 74.35 \\ +TwHIN-BERT$_\text{Large}$ & 61.47 & 61.31 & 56.73 & 83.14 & 82.72 & 55.84 & 64.76 & 64.82 & 64.82 & 88.73 & 88.23 & 72.18 & 85.92 & 84.43 & 78.28 \\ +\hdashline +ViSoBERT & \textbf{63.06} & \textbf{62.36} & \textbf{59.16} & \textbf{87.62} & \textbf{86.81} & \textbf{64.82} & \textbf{73.52} & \textbf{73.50} & \textbf{73.50} & \textbf{90.11} & \textbf{89.79} & \textbf{75.71} & \textbf{88.37} & \textbf{87.87} & \textbf{82.18} \\ +\hline +\multicolumn{16}{c}{\textbf{\textit{BiGRU}}} \\ +\hline +PhoBERT$_\text{Large}$ & 55.12 & 54.53 & 49.59 & 85.21 & 83.23 & 54.59 & 70.01 & 70.01 & 70.01 & 86.06 & 84.89 & 62.54 & 84.23 & 81.01 & 74.57 \\ +TwHIN-BERT$_\text{Large}$ & 60.46 & 60.30 & 55.23 & 85.73 & 83.45 & 54.74 & 63.11 & 61.39 & 61.39 & 87.67 & 86.38 & 66.83 & 86.10 & 84.52 & 78.49 \\ +\hdashline +ViSoBERT & \textbf{63.20} & \textbf{63.25} & \textbf{60.73} & \textbf{87.02} & \textbf{86.25} & \textbf{63.36} & \textbf{70.48} & \textbf{70.53} & \textbf{70.53} & \textbf{89.33} & \textbf{88.98} & \textbf{76.57} & \textbf{88.88} & \textbf{88.19} & \textbf{82.63} \\ +\hline +\end{tabular}} +\caption{Performances of the pre-trained language models as input features to BiLSTM and BiGRU on downstream Vietnamese social media tasks.} +\label{tab:feature-based} +\end{table*} + +We implemented various PLMs when used as input features in combination with BiLSTM and BiGRU models to verify the ability to extract Vietnamese social media texts. The evaluation is conducted on the dev set, and the performance is measured per epoch for downstream tasks. Table~\ref{tab:feature-based} shows performances of the PLMs as input features to BiLSTM and BiGRU on the dev set per epoch. +\begin{figure}[!ht] + \centering + \begin{subfigure}[a]{0.323\textwidth} + \centering + \includegraphics[width=\textwidth]{vsmec.pdf} + \caption{Emotion Regconition} + \label{fig:ER} + \end{subfigure} + \hfill + \begin{subfigure}[a]{0.323\textwidth} + \centering + \includegraphics[width=\textwidth]{vihsd.pdf} + \caption{Hate Speech Detection} + \label{fig:HSD} + \end{subfigure} + \hfill + \begin{subfigure}[a]{0.323\textwidth} + \centering + \includegraphics[width=\textwidth]{vlsp.pdf} + \caption{Sentiment Analysis} + \label{fig:SA} + \end{subfigure} + \hfill + \begin{subfigure}[a]{0.323\textwidth} + \centering + \includegraphics[width=\textwidth]{vispam.pdf} + \caption{Spam Reviews Detection} + \label{fig:SRD} + \end{subfigure} + % \hfill + \begin{subfigure}[a]{0.323\textwidth} + \centering + \includegraphics[width=\textwidth]{vihos.pdf} + \caption{Hate Speech Spans Detection} + \label{fig:HSSD} + \end{subfigure} + \begin{subfigure}[c]{0.323\textwidth} + \vspace*{-25pt} + \hspace*{40pt}\includegraphics[scale=0.430]{legend.pdf} + \end{subfigure} + +\caption{Performances of the PLMs as input features to BiLSTM and BiGRU on the dev set per epoch on Vietnamese social media downstream tasks. \textit{Large} versions of PhoBERT and TwHIN-BERT are implemented for these experiments.} +\label{fig:perepoch} +\end{figure} + +\newpage +% ~\newpage +\section{Updating New Spans of Hate Speech Span Detection Samples with Pre-processing Techniques} +\label{app:updatingViHOS} + +Due to performing pre-processing techniques, the span positions on the data samples can be changed. Therefore, we present Algorithm \ref{agrt:updatenewspans}, which shows how to update new span positions of samples applied with pre-processing techniques in the Hate Speech Spans Detection task (UIT-ViHOS dataset). This algorithm takes as input a comment and its spans and returns the pre-processed comment and its span along with pre-processing techniques. + +\begin{algorithm}[H] +\begin{algorithmic}[1] + +\Procedure{Algorithm}{$\text{comment}$, $\text{label}$, $\text{delete}$}\caption{Updating new spans of samples applied with pre-processing techniques in Hate Speech Spans Detection task (UIT-ViHOS dataset).}\label{agrt:updatenewspans} + \State \textbf{assert} $\text{len(comment)}$ == $\text{len(label)}$ + \State $\text{new\_comment} \gets []$, $\text{new\_label} \gets []$ + % \State $\text{new\_label} \gets []$ + \For{$i \gets 0$ \textbf{to} $\text{len(comment)}$} + \State $\text{check} \gets 0$ + \If{$\text{comment}[i]$ \textbf{in} $\text{emoji\_to\_word.keys()}$} + \If{$\text{delete}$} + % \Comment{\textit{delete} variable stands for is removing emoji} + \State \textbf{continue} + \EndIf + \For{$j \gets 0$ \textbf{to} $\text{len(emoji\_to\_word[comment[i]].split(` '))}$} + \If{$\text{label}[i]$ == `B-T'} + \If{$\text{check}$ == $0$} + \State $\text{check} \gets \text{check} + 1$, $\text{new\_label.append(label[i])}$ + % \State $\text{new\_label.append(label[i])}$ + \Else + \State $\text{new\_label.append(`I-T')}$ + \EndIf + \Else + \State $\text{new\_label.append(label[i])}$ + \EndIf + \State $\text{new\_comment.append(emoji\_to\_word[comment[i]].split(` ')[j])}$ + \EndFor + \Else + \State $\text{new\_sentence.append(comment[i])}$ + \State $\text{new\_label.append(label[i])}$ + \EndIf + \State \textbf{assert} $\text{len(new\_comment)}$ == $\text{len(new\_label)}$ + \EndFor + \State \textbf{return} \text{new\_comment}, \text{new\_label} +\EndProcedure + +\end{algorithmic} +\end{algorithm} + +% \newpage +\section{Tokenizations of the PLMs on Removing Diacritics Social Comments}\label{removingdiacritics} +We analyze several data samples to see the tokenized ability of Vietnamese social media textual data while removing diacritics on comments. Table~\ref{tab:diacriticscomments} shows several non-diacritics Vietnamese social comments and their tokenizations with the tokenizers of the three best pre-trained language models, ViSoBERT (ours), PhoBERT, and TwHIN-BERT. + +\begin{table}[!ht] +\resizebox{\textwidth}{!}{% +\begin{tabular}{lll} +\hline +\multicolumn{1}{l|}{\textbf{Model}} & + \multicolumn{1}{c|}{\textbf{Example 1}} & + \multicolumn{1}{c}{\textbf{Example 2}} \\ \hline +\multicolumn{1}{l|}{Raw comment} & \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}cái con đồ chơi đó mua ở đâu nhỉ . cười đéo nhặt được \\mồm \includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png} +\\\textit{English}: where did you buy that toy . LMAO \includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\end{tabular}} & \begin{tabular}[c]{@{}l@{}}Ôi bố cái lũ thanh niên hãm lol. Đẹp mặt quá \includegraphics[width=12pt]{unamused-face_1f612.png}\includegraphics[width=12pt]{unamused-face_1f612.png}\\\textit{English}:~Oh my god damn teenagers, lol. So deserved \includegraphics[width=12pt]{unamused-face_1f612.png}\includegraphics[width=12pt]{unamused-face_1f612.png}\end{tabular} \\ +\hline +\multicolumn{3}{c}{\textit{\textbf{Removing 100\% diacritics in each comment}}} \\ \hline +\multicolumn{1}{l|}{Comment} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}cai con do choi do mua o dau nhi . cuoi deo nhat duoc \\mom . \includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\end{tabular}} & + Oi bo cai lu thanh nien ham lol. Dep mat qua \includegraphics[width=12pt]{unamused-face_1f612.png}\includegraphics[width=12pt]{unamused-face_1f612.png} \\ \hline +\multicolumn{1}{l|}{PhoBERT} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "c a i", "c o n", "d o", "c h o @ @", "i", "d o", "m u a", \\ "o", "d @ @", "a u", "n h i", ".", "c u @ @", "o i", \\"d @ @", "e o", "n h @ @", "a t", "d u @ @", "o c", \\"m o m", ".", \textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \textless{}/s\textgreater{}\end{tabular}} & + \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "O @ @", "i", "b o", "c a i", "l u", "t h a n h", "n i @ @",\\"e n", "h a m", "l o @ @", "l", ".", "D e @ @", "p", "m a t",\\"q u a", \textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \textless{}/s\textgreater{}\end{tabular} \\ \hline +\multicolumn{1}{l|}{TwHIN-BERT} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "cai", "con", "do", "cho", "i", "do", "mua", "o", "dau", \\"nhi", "", ".", "cu", "oi", "de", "o", "nha", "t", "du", "oc", \\"mom", "", ".", "", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", \textless{}/s\textgreater{}\end{tabular}} & + \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "Oi", "bo", "cai", "lu", "thanh", "nie", "n", "ham", "lol", \\".", "De", "p", "mat", "qua", "", "\includegraphics[width=12pt]{unamused-face_1f612.png}", "\includegraphics[width=12pt]{unamused-face_1f612.png}", \textless{}/s\textgreater{}\end{tabular} \\ \hline +\multicolumn{1}{l|}{ViSoBERT} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "cai", "con", "do", "choi", "do", "mua", "o", "dau", "nhi", \\".","cu", "oi", "d", "eo", "nhat", "duoc", "m", "om", ".", \\"\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", \textless{}/s\textgreater{}\end{tabular}} & + \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "O", "i", "bo", "cai", "lu", "thanh", "ni", "en", "h", "am", \\"lol", ".", "D", "ep", "mat", "qua", "\includegraphics[width=12pt]{unamused-face_1f612.png}\includegraphics[width=12pt]{unamused-face_1f612.png}", \textless{}/s\textgreater{}\end{tabular} \\ \hline +\multicolumn{3}{c}{\textit{\textbf{Removing 75\% diacritics in each comment}}} \\ \hline +\multicolumn{1}{l|}{Comment} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}cai con do chơi do mua o đâu nhi . cười deo nhat duoc \\mom . \includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\end{tabular}} & + Ôi bo cai lu thanh niên hãm lol. Dep mat qua \includegraphics[width=12pt]{unamused-face_1f612.png}\includegraphics[width=12pt]{unamused-face_1f612.png} \\ \hline +\multicolumn{1}{l|}{PhoBERT} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "c a i", "c o n", "d o", "c h ơ i", "d o", "m u a", "o", \\"đ â u", "n h i", ".", "c ư ờ i", "d @ @", "e o", "n h @ @", \\"a t", "d u @ @", "o c", "m o m", ".",\textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \\\textless{}unk\textgreater{}, \textless{}/s\textgreater{}\end{tabular}} & + \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "Ô i", "b o", "c a i", "l u", "t h a n h \_ n i ê n", "h ã m", \\"l o @ @", "l", ".", "D e @ @", "p", "m a t", "q u a", \textless{}unk\textgreater{}, \\\textless{}unk\textgreater{}, \textless{}/s\textgreater{}\end{tabular} \\ \hline +\multicolumn{1}{l|}{TwHIN-BERT} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "cai", "con", "do", "chơi", "do", "mua", "o", "đâu", \\"nhi", "", ".", "cười", "de", "o", "nha", "t", "du", "oc", \\"mom", "", ".", "", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", \textless{}/s\textgreater{}\end{tabular}} & + \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "Ô", "i", "bo", "cai", "lu", "thanh", "niên", "", "hã", "m", \\"lol", ".", "De", "p", "mat", "qua", "", "\includegraphics[width=12pt]{unamused-face_1f612.png}", "\includegraphics[width=12pt]{unamused-face_1f612.png}", \textless{}/s\textgreater{}\end{tabular} \\ \hline +\multicolumn{1}{l|}{ViSoBERT} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "cai", "con", "do", "chơi", "do", "mua", "o", "đâu", \\"nhi", ".", "cười", "d", "eo", "nhat", "duoc", "m", "om", \\".", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", \textless{}/s\textgreater{}\end{tabular}} & + \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "Ôi", "bo", "cai", "lu", "thanh", "n", "iên", "hã", "m", \\"lol", ".", "D", "ep", "mat", "qua", "\includegraphics[width=12pt]{unamused-face_1f612.png}\includegraphics[width=12pt]{unamused-face_1f612.png}", \textless{}/s\textgreater{}\end{tabular} \\ \hline +\multicolumn{3}{c}{\textit{\textbf{Removing 50\% diacritics in each comment}}} \\ \hline +\multicolumn{1}{l|}{Comment} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}cai con do chơi do mua o đâu nhỉ . cười đéo nhặt duoc \\mom . \includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\end{tabular}} & + Ôi bo cai lu thanh niên hãm lol. Dep mặt quá \includegraphics[width=12pt]{unamused-face_1f612.png}\includegraphics[width=12pt]{unamused-face_1f612.png} \\ \hline +\multicolumn{1}{l|}{PhoBERT} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "c a i", "c o n", "d o", "c h ơ i", "d o", "m u a", "o", \\"đ â u", "n h ỉ", ".", "c ư ờ i", "đ @ @", "é o", "n h ặ t", \\"d u @ @", "o c", "m o m", ".", \textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \textless{}/s\textgreater{}\end{tabular}} & + \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "Ô i", "b o", "c a i", "l u", "t h a n h \_ n i ê n", "h ã m", \\"l o @ @", "l", ".", "D e @ @", "p", "m ặ t", "q u á", \textless{}unk\textgreater{}, \\\textless{}unk\textgreater{}, \textless{}/s\textgreater{}\end{tabular} \\ \hline +\multicolumn{1}{l|}{TwHIN-BERT} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "cai", "con", "do", "chơi", "do", "mua", "o", "đâu", "nhỉ", \\"", ".", "cười", "đ", "é", "o", "nh", "ặt", "du", "oc", "mom", \\"", ".", "", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", \textless{}/s\textgreater{}\end{tabular}} & + \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "Ô", "i", "bo", "cai", "lu", "thanh", "niên", "", "hã", "m", \\"lol", ".", "De", "p", "mặt", "quá", "", "\includegraphics[width=12pt]{unamused-face_1f612.png}", "\includegraphics[width=12pt]{unamused-face_1f612.png}", \textless{}/s\textgreater{}\end{tabular} \\ \hline +\multicolumn{1}{l|}{ViSoBERT} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "cai", "con", "do", "chơi", "do", "mua", "o", "đâu", \\"nhỉ", ".", "cười", "đéo", "nh", "ặt", "duoc", "m", "om", \\".", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", \textless{}/s\textgreater{}\end{tabular}} & + \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "Ôi", "bo", "cai", "lu", "thanh", "n", "iên", "hã", "m", \\"lol", ".", "D", "ep", "mặt", "quá", "\includegraphics[width=12pt]{unamused-face_1f612.png}\includegraphics[width=12pt]{unamused-face_1f612.png}", \textless{}/s\textgreater{}\end{tabular} \\ \hline +\multicolumn{3}{c}{\textit{\textbf{Removing 25\% diacritics in each comment}}} \\ \hline +\multicolumn{1}{l|}{Comment} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}cai con do chơi đó mua ở đâu nhỉ . cười đéo nhặt duoc \\mồm . \includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\end{tabular}} & + Ôi bo cai lu thanh niên hãm lol. Đep mặt quá \includegraphics[width=12pt]{unamused-face_1f612.png}\includegraphics[width=12pt]{unamused-face_1f612.png} \\ \hline +\multicolumn{1}{l|}{PhoBERT} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "c a i", "c o n", "d o", "c h ơ i", "đ ó", "m u a", "ở", \\"đ â u", "n h ỉ", ".", "c ư ờ i", "đ @ @", "é o", "n h ặ t", \\"d u @ @", "o c", "m ồ m", ".", \textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \textless{}/s\textgreater{}\end{tabular}} & + \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "Ô i", "b o", "c a i", "l u", "t h a n h \_ n i ê n", "h ã m", \\"l o @ @", "l", ".", "Đ e p \_ @ @", "m ặ t", "q u á", \textless{}unk\textgreater{}, \\\textless{}unk\textgreater{}, \textless{}/s\textgreater{}\end{tabular} \\ \hline +\multicolumn{1}{l|}{TwHIN-BERT} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "cai", "con", "do", "chơi", "do", "mua", "o", "đâu", \\"nhỉ", "", ".", "cười", "đ", "é", "o", "nh", "ặt", "du", "oc", \\"mom", "", ".", "", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", \textless{}/s\textgreater{}\end{tabular}} & + \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "Ô", "i", "bo", "cai", "lu", "thanh", "niên", "", "hã", "m", \\"lol", ".", "Đep", "mặt", "quá", "", "\includegraphics[width=12pt]{unamused-face_1f612.png}", "\includegraphics[width=12pt]{unamused-face_1f612.png}", \textless{}/s\textgreater{}\end{tabular} \\ \hline +\multicolumn{1}{l|}{ViSoBERT} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "cai", "con", "do", "chơi", "đó", "mua", "ở", "đâu", \\"nhỉ", ".", "cười", "đéo", "nh", "ặt", "duoc", "mồm", ".", \\"\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", \textless{}/s\textgreater{}\end{tabular}} & + \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "Ôi", "bo", "cai", "lu", "thanh", "n", "iên", "hã", "m", \\"lol", ".", "Đep", "mặt", "quá", "\includegraphics[width=12pt]{unamused-face_1f612.png}\includegraphics[width=12pt]{unamused-face_1f612.png}", \textless{}/s\textgreater{}\end{tabular} \\ \hline +\end{tabular}% +} +\caption{Actual social comments and their tokenizations with the tokenizers of the three pre-trained language models, including PhoBERT, TwHIN-BERT, and ViSoBERT, on removing diacritics of social comments.} +\label{tab:diacriticscomments} +\end{table} + +% \section{aaaa} +% \begin{figure}[ht] +% \centering +% \includegraphics[width=\textwidth]{emnlp2023-latex/Figures/f1_macro_masked_rate (2).png} +% \caption{Impact of masking rate on our pre-trained ViSoBERT in terms of MF1.} +% \label{maskingrate} +% \end{figure} + +% \section{Detailed Discussion Results}\label{appendix:detailed} +% \subsection{Impact of masking rate}\label{appendix:masking} +% \subsection{Vocabulary efficiency}\label{appendix:vocab} +% \subsection{Impact of Emoji}\label{appendix:emoji} +% \subsection{Impact of Teencode}\label{appendix:teencode} + + + +\end{document} diff --git a/references/2023.arxiv.nguyen/source/acl_natbib.bst b/references/2023.arxiv.nguyen/source/acl_natbib.bst new file mode 100644 index 0000000000000000000000000000000000000000..ca569042cb5e93ee718b781f1935c2080bfc1a5e --- /dev/null +++ b/references/2023.arxiv.nguyen/source/acl_natbib.bst @@ -0,0 +1,1979 @@ +%%% Modification of BibTeX style file acl_natbib_nourl.bst +%%% ... by urlbst, version 0.7 (marked with "% urlbst") +%%% See +%%% Added webpage entry type, and url and lastchecked fields. +%%% Added eprint support. +%%% Added DOI support. +%%% Added PUBMED support. +%%% Added hyperref support. +%%% Original headers follow... + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% BibTeX style file acl_natbib_nourl.bst +% +% intended as input to urlbst script +% $ ./urlbst --hyperref --inlinelinks acl_natbib_nourl.bst > acl_natbib.bst +% +% adapted from compling.bst +% in order to mimic the style files for ACL conferences prior to 2017 +% by making the following three changes: +% - for @incollection, page numbers now follow volume title. +% - for @inproceedings, address now follows conference name. +% (address is intended as location of conference, +% not address of publisher.) +% - for papers with three authors, use et al. in citation +% Dan Gildea 2017/06/08 +% +% - fixed a bug with format.chapter - error given if chapter is empty +% with inbook. +% Shay Cohen 2018/02/16 +% +% - sort "van Noord" under "v" not "N" +% this is what previous ACL style files did and is pretty standard +% Dan Gildea 2019/04/12 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% BibTeX style file compling.bst +% +% Intended for the journal Computational Linguistics (ACL/MIT Press) +% Created by Ron Artstein on 2005/08/22 +% For use with for author-year citations. +% +% I created this file in order to allow submissions to the journal +% Computational Linguistics using the package for author-year +% citations, which offers a lot more flexibility than , CL's +% official citation package. This file adheres strictly to the official +% style guide available from the MIT Press: +% +% http://mitpress.mit.edu/journals/coli/compling_style.pdf +% +% This includes all the various quirks of the style guide, for example: +% - a chapter from a monograph (@inbook) has no page numbers. +% - an article from an edited volume (@incollection) has page numbers +% after the publisher and address. +% - an article from a proceedings volume (@inproceedings) has page +% numbers before the publisher and address. +% +% Where the style guide was inconsistent or not specific enough I +% looked at actual published articles and exercised my own judgment. +% I noticed two inconsistencies in the style guide: +% +% - The style guide gives one example of an article from an edited +% volume with the editor's name spelled out in full, and another +% with the editors' names abbreviated. I chose to accept the first +% one as correct, since the style guide generally shuns abbreviations, +% and editors' names are also spelled out in some recently published +% articles. +% +% - The style guide gives one example of a reference where the word +% "and" between two authors is preceded by a comma. This is most +% likely a typo, since in all other cases with just two authors or +% editors there is no comma before the word "and". +% +% One case where the style guide is not being specific is the placement +% of the edition number, for which no example is given. I chose to put +% it immediately after the title, which I (subjectively) find natural, +% and is also the place of the edition in a few recently published +% articles. +% +% This file correctly reproduces all of the examples in the official +% style guide, except for the two inconsistencies noted above. I even +% managed to get it to correctly format the proceedings example which +% has an organization, a publisher, and two addresses (the conference +% location and the publisher's address), though I cheated a bit by +% putting the conference location and month as part of the title field; +% I feel that in this case the conference location and month can be +% considered as part of the title, and that adding a location field +% is not justified. Note also that a location field is not standard, +% so entries made with this field would not port nicely to other styles. +% However, if authors feel that there's a need for a location field +% then tell me and I'll see what I can do. +% +% The file also produces to my satisfaction all the bibliographical +% entries in my recent (joint) submission to CL (this was the original +% motivation for creating the file). I also tested it by running it +% on a larger set of entries and eyeballing the results. There may of +% course still be errors, especially with combinations of fields that +% are not that common, or with cross-references (which I seldom use). +% If you find such errors please write to me. +% +% I hope people find this file useful. Please email me with comments +% and suggestions. +% +% Ron Artstein +% artstein [at] essex.ac.uk +% August 22, 2005. +% +% Some technical notes. +% +% This file is based on a file generated with the package +% by Patrick W. Daly (see selected options below), which was then +% manually customized to conform with certain CL requirements which +% cannot be met by . Departures from the generated file +% include: +% +% Function inbook: moved publisher and address to the end; moved +% edition after title; replaced function format.chapter.pages by +% new function format.chapter to output chapter without pages. +% +% Function inproceedings: moved publisher and address to the end; +% replaced function format.in.ed.booktitle by new function +% format.in.booktitle to output the proceedings title without +% the editor. +% +% Functions book, incollection, manual: moved edition after title. +% +% Function mastersthesis: formatted title as for articles (unlike +% phdthesis which is formatted as book) and added month. +% +% Function proceedings: added new.sentence between organization and +% publisher when both are present. +% +% Function format.lab.names: modified so that it gives all the +% authors' surnames for in-text citations for one, two and three +% authors and only uses "et. al" for works with four authors or more +% (thanks to Ken Shan for convincing me to go through the trouble of +% modifying this function rather than using unreliable hacks). +% +% Changes: +% +% 2006-10-27: Changed function reverse.pass so that the extra label is +% enclosed in parentheses when the year field ends in an uppercase or +% lowercase letter (change modeled after Uli Sauerland's modification +% of nals.bst). RA. +% +% +% The preamble of the generated file begins below: +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%% +%% This is file `compling.bst', +%% generated with the docstrip utility. +%% +%% The original source files were: +%% +%% merlin.mbs (with options: `ay,nat,vonx,nm-revv1,jnrlst,keyxyr,blkyear,dt-beg,yr-per,note-yr,num-xser,pre-pub,xedn,nfss') +%% ---------------------------------------- +%% *** Intended for the journal Computational Linguistics *** +%% +%% Copyright 1994-2002 Patrick W Daly + % =============================================================== + % IMPORTANT NOTICE: + % This bibliographic style (bst) file has been generated from one or + % more master bibliographic style (mbs) files, listed above. + % + % This generated file can be redistributed and/or modified under the terms + % of the LaTeX Project Public License Distributed from CTAN + % archives in directory macros/latex/base/lppl.txt; either + % version 1 of the License, or any later version. + % =============================================================== + % Name and version information of the main mbs file: + % \ProvidesFile{merlin.mbs}[2002/10/21 4.05 (PWD, AO, DPC)] + % For use with BibTeX version 0.99a or later + %------------------------------------------------------------------- + % This bibliography style file is intended for texts in ENGLISH + % This is an author-year citation style bibliography. As such, it is + % non-standard LaTeX, and requires a special package file to function properly. + % Such a package is natbib.sty by Patrick W. Daly + % The form of the \bibitem entries is + % \bibitem[Jones et al.(1990)]{key}... + % \bibitem[Jones et al.(1990)Jones, Baker, and Smith]{key}... + % The essential feature is that the label (the part in brackets) consists + % of the author names, as they should appear in the citation, with the year + % in parentheses following. There must be no space before the opening + % parenthesis! + % With natbib v5.3, a full list of authors may also follow the year. + % In natbib.sty, it is possible to define the type of enclosures that is + % really wanted (brackets or parentheses), but in either case, there must + % be parentheses in the label. + % The \cite command functions as follows: + % \citet{key} ==>> Jones et al. (1990) + % \citet*{key} ==>> Jones, Baker, and Smith (1990) + % \citep{key} ==>> (Jones et al., 1990) + % \citep*{key} ==>> (Jones, Baker, and Smith, 1990) + % \citep[chap. 2]{key} ==>> (Jones et al., 1990, chap. 2) + % \citep[e.g.][]{key} ==>> (e.g. Jones et al., 1990) + % \citep[e.g.][p. 32]{key} ==>> (e.g. Jones et al., p. 32) + % \citeauthor{key} ==>> Jones et al. + % \citeauthor*{key} ==>> Jones, Baker, and Smith + % \citeyear{key} ==>> 1990 + %--------------------------------------------------------------------- + +ENTRY + { address + author + booktitle + chapter + edition + editor + howpublished + institution + journal + key + month + note + number + organization + pages + publisher + school + series + title + type + volume + year + eprint % urlbst + doi % urlbst + pubmed % urlbst + url % urlbst + lastchecked % urlbst + } + {} + { label extra.label sort.label short.list } +INTEGERS { output.state before.all mid.sentence after.sentence after.block } +% urlbst... +% urlbst constants and state variables +STRINGS { urlintro + eprinturl eprintprefix doiprefix doiurl pubmedprefix pubmedurl + citedstring onlinestring linktextstring + openinlinelink closeinlinelink } +INTEGERS { hrefform inlinelinks makeinlinelink + addeprints adddoiresolver addpubmedresolver } +FUNCTION {init.urlbst.variables} +{ + % The following constants may be adjusted by hand, if desired + + % The first set allow you to enable or disable certain functionality. + #1 'addeprints := % 0=no eprints; 1=include eprints + #1 'adddoiresolver := % 0=no DOI resolver; 1=include it + #1 'addpubmedresolver := % 0=no PUBMED resolver; 1=include it + #2 'hrefform := % 0=no crossrefs; 1=hypertex xrefs; 2=hyperref refs + #1 'inlinelinks := % 0=URLs explicit; 1=URLs attached to titles + + % String constants, which you _might_ want to tweak. + "URL: " 'urlintro := % prefix before URL; typically "Available from:" or "URL": + "online" 'onlinestring := % indication that resource is online; typically "online" + "cited " 'citedstring := % indicator of citation date; typically "cited " + "[link]" 'linktextstring := % dummy link text; typically "[link]" + "http://arxiv.org/abs/" 'eprinturl := % prefix to make URL from eprint ref + "arXiv:" 'eprintprefix := % text prefix printed before eprint ref; typically "arXiv:" + "https://doi.org/" 'doiurl := % prefix to make URL from DOI + "doi:" 'doiprefix := % text prefix printed before DOI ref; typically "doi:" + "http://www.ncbi.nlm.nih.gov/pubmed/" 'pubmedurl := % prefix to make URL from PUBMED + "PMID:" 'pubmedprefix := % text prefix printed before PUBMED ref; typically "PMID:" + + % The following are internal state variables, not configuration constants, + % so they shouldn't be fiddled with. + #0 'makeinlinelink := % state variable managed by possibly.setup.inlinelink + "" 'openinlinelink := % ditto + "" 'closeinlinelink := % ditto +} +INTEGERS { + bracket.state + outside.brackets + open.brackets + within.brackets + close.brackets +} +% ...urlbst to here +FUNCTION {init.state.consts} +{ #0 'outside.brackets := % urlbst... + #1 'open.brackets := + #2 'within.brackets := + #3 'close.brackets := % ...urlbst to here + + #0 'before.all := + #1 'mid.sentence := + #2 'after.sentence := + #3 'after.block := +} +STRINGS { s t} +% urlbst +FUNCTION {output.nonnull.original} +{ 's := + output.state mid.sentence = + { ", " * write$ } + { output.state after.block = + { add.period$ write$ + newline$ + "\newblock " write$ + } + { output.state before.all = + 'write$ + { add.period$ " " * write$ } + if$ + } + if$ + mid.sentence 'output.state := + } + if$ + s +} + +% urlbst... +% The following three functions are for handling inlinelink. They wrap +% a block of text which is potentially output with write$ by multiple +% other functions, so we don't know the content a priori. +% They communicate between each other using the variables makeinlinelink +% (which is true if a link should be made), and closeinlinelink (which holds +% the string which should close any current link. They can be called +% at any time, but start.inlinelink will be a no-op unless something has +% previously set makeinlinelink true, and the two ...end.inlinelink functions +% will only do their stuff if start.inlinelink has previously set +% closeinlinelink to be non-empty. +% (thanks to 'ijvm' for suggested code here) +FUNCTION {uand} +{ 'skip$ { pop$ #0 } if$ } % 'and' (which isn't defined at this point in the file) +FUNCTION {possibly.setup.inlinelink} +{ makeinlinelink hrefform #0 > uand + { doi empty$ adddoiresolver uand + { pubmed empty$ addpubmedresolver uand + { eprint empty$ addeprints uand + { url empty$ + { "" } + { url } + if$ } + { eprinturl eprint * } + if$ } + { pubmedurl pubmed * } + if$ } + { doiurl doi * } + if$ + % an appropriately-formatted URL is now on the stack + hrefform #1 = % hypertex + { "\special {html: }{" * 'openinlinelink := + "\special {html:}" 'closeinlinelink := } + { "\href {" swap$ * "} {" * 'openinlinelink := % hrefform=#2 -- hyperref + % the space between "} {" matters: a URL of just the right length can cause "\% newline em" + "}" 'closeinlinelink := } + if$ + #0 'makeinlinelink := + } + 'skip$ + if$ % makeinlinelink +} +FUNCTION {add.inlinelink} +{ openinlinelink empty$ + 'skip$ + { openinlinelink swap$ * closeinlinelink * + "" 'openinlinelink := + } + if$ +} +FUNCTION {output.nonnull} +{ % Save the thing we've been asked to output + 's := + % If the bracket-state is close.brackets, then add a close-bracket to + % what is currently at the top of the stack, and set bracket.state + % to outside.brackets + bracket.state close.brackets = + { "]" * + outside.brackets 'bracket.state := + } + 'skip$ + if$ + bracket.state outside.brackets = + { % We're outside all brackets -- this is the normal situation. + % Write out what's currently at the top of the stack, using the + % original output.nonnull function. + s + add.inlinelink + output.nonnull.original % invoke the original output.nonnull + } + { % Still in brackets. Add open-bracket or (continuation) comma, add the + % new text (in s) to the top of the stack, and move to the close-brackets + % state, ready for next time (unless inbrackets resets it). If we come + % into this branch, then output.state is carefully undisturbed. + bracket.state open.brackets = + { " [" * } + { ", " * } % bracket.state will be within.brackets + if$ + s * + close.brackets 'bracket.state := + } + if$ +} + +% Call this function just before adding something which should be presented in +% brackets. bracket.state is handled specially within output.nonnull. +FUNCTION {inbrackets} +{ bracket.state close.brackets = + { within.brackets 'bracket.state := } % reset the state: not open nor closed + { open.brackets 'bracket.state := } + if$ +} + +FUNCTION {format.lastchecked} +{ lastchecked empty$ + { "" } + { inbrackets citedstring lastchecked * } + if$ +} +% ...urlbst to here +FUNCTION {output} +{ duplicate$ empty$ + 'pop$ + 'output.nonnull + if$ +} +FUNCTION {output.check} +{ 't := + duplicate$ empty$ + { pop$ "empty " t * " in " * cite$ * warning$ } + 'output.nonnull + if$ +} +FUNCTION {fin.entry.original} % urlbst (renamed from fin.entry, so it can be wrapped below) +{ add.period$ + write$ + newline$ +} + +FUNCTION {new.block} +{ output.state before.all = + 'skip$ + { after.block 'output.state := } + if$ +} +FUNCTION {new.sentence} +{ output.state after.block = + 'skip$ + { output.state before.all = + 'skip$ + { after.sentence 'output.state := } + if$ + } + if$ +} +FUNCTION {add.blank} +{ " " * before.all 'output.state := +} + +FUNCTION {date.block} +{ + new.block +} + +FUNCTION {not} +{ { #0 } + { #1 } + if$ +} +FUNCTION {and} +{ 'skip$ + { pop$ #0 } + if$ +} +FUNCTION {or} +{ { pop$ #1 } + 'skip$ + if$ +} +FUNCTION {new.block.checkb} +{ empty$ + swap$ empty$ + and + 'skip$ + 'new.block + if$ +} +FUNCTION {field.or.null} +{ duplicate$ empty$ + { pop$ "" } + 'skip$ + if$ +} +FUNCTION {emphasize} +{ duplicate$ empty$ + { pop$ "" } + { "\emph{" swap$ * "}" * } + if$ +} +FUNCTION {tie.or.space.prefix} +{ duplicate$ text.length$ #3 < + { "~" } + { " " } + if$ + swap$ +} + +FUNCTION {capitalize} +{ "u" change.case$ "t" change.case$ } + +FUNCTION {space.word} +{ " " swap$ * " " * } + % Here are the language-specific definitions for explicit words. + % Each function has a name bbl.xxx where xxx is the English word. + % The language selected here is ENGLISH +FUNCTION {bbl.and} +{ "and"} + +FUNCTION {bbl.etal} +{ "et~al." } + +FUNCTION {bbl.editors} +{ "editors" } + +FUNCTION {bbl.editor} +{ "editor" } + +FUNCTION {bbl.edby} +{ "edited by" } + +FUNCTION {bbl.edition} +{ "edition" } + +FUNCTION {bbl.volume} +{ "volume" } + +FUNCTION {bbl.of} +{ "of" } + +FUNCTION {bbl.number} +{ "number" } + +FUNCTION {bbl.nr} +{ "no." } + +FUNCTION {bbl.in} +{ "in" } + +FUNCTION {bbl.pages} +{ "pages" } + +FUNCTION {bbl.page} +{ "page" } + +FUNCTION {bbl.chapter} +{ "chapter" } + +FUNCTION {bbl.techrep} +{ "Technical Report" } + +FUNCTION {bbl.mthesis} +{ "Master's thesis" } + +FUNCTION {bbl.phdthesis} +{ "Ph.D. thesis" } + +MACRO {jan} {"January"} + +MACRO {feb} {"February"} + +MACRO {mar} {"March"} + +MACRO {apr} {"April"} + +MACRO {may} {"May"} + +MACRO {jun} {"June"} + +MACRO {jul} {"July"} + +MACRO {aug} {"August"} + +MACRO {sep} {"September"} + +MACRO {oct} {"October"} + +MACRO {nov} {"November"} + +MACRO {dec} {"December"} + +MACRO {acmcs} {"ACM Computing Surveys"} + +MACRO {acta} {"Acta Informatica"} + +MACRO {cacm} {"Communications of the ACM"} + +MACRO {ibmjrd} {"IBM Journal of Research and Development"} + +MACRO {ibmsj} {"IBM Systems Journal"} + +MACRO {ieeese} {"IEEE Transactions on Software Engineering"} + +MACRO {ieeetc} {"IEEE Transactions on Computers"} + +MACRO {ieeetcad} + {"IEEE Transactions on Computer-Aided Design of Integrated Circuits"} + +MACRO {ipl} {"Information Processing Letters"} + +MACRO {jacm} {"Journal of the ACM"} + +MACRO {jcss} {"Journal of Computer and System Sciences"} + +MACRO {scp} {"Science of Computer Programming"} + +MACRO {sicomp} {"SIAM Journal on Computing"} + +MACRO {tocs} {"ACM Transactions on Computer Systems"} + +MACRO {tods} {"ACM Transactions on Database Systems"} + +MACRO {tog} {"ACM Transactions on Graphics"} + +MACRO {toms} {"ACM Transactions on Mathematical Software"} + +MACRO {toois} {"ACM Transactions on Office Information Systems"} + +MACRO {toplas} {"ACM Transactions on Programming Languages and Systems"} + +MACRO {tcs} {"Theoretical Computer Science"} +FUNCTION {bibinfo.check} +{ swap$ + duplicate$ missing$ + { + pop$ pop$ + "" + } + { duplicate$ empty$ + { + swap$ pop$ + } + { swap$ + pop$ + } + if$ + } + if$ +} +FUNCTION {bibinfo.warn} +{ swap$ + duplicate$ missing$ + { + swap$ "missing " swap$ * " in " * cite$ * warning$ pop$ + "" + } + { duplicate$ empty$ + { + swap$ "empty " swap$ * " in " * cite$ * warning$ + } + { swap$ + pop$ + } + if$ + } + if$ +} +STRINGS { bibinfo} +INTEGERS { nameptr namesleft numnames } + +FUNCTION {format.names} +{ 'bibinfo := + duplicate$ empty$ 'skip$ { + 's := + "" 't := + #1 'nameptr := + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + duplicate$ #1 > + { "{ff~}{vv~}{ll}{, jj}" } + { "{ff~}{vv~}{ll}{, jj}" } % first name first for first author +% { "{vv~}{ll}{, ff}{, jj}" } % last name first for first author + if$ + format.name$ + bibinfo bibinfo.check + 't := + nameptr #1 > + { + namesleft #1 > + { ", " * t * } + { + numnames #2 > + { "," * } + 'skip$ + if$ + s nameptr "{ll}" format.name$ duplicate$ "others" = + { 't := } + { pop$ } + if$ + t "others" = + { + " " * bbl.etal * + } + { + bbl.and + space.word * t * + } + if$ + } + if$ + } + 't + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ + } if$ +} +FUNCTION {format.names.ed} +{ + 'bibinfo := + duplicate$ empty$ 'skip$ { + 's := + "" 't := + #1 'nameptr := + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + "{ff~}{vv~}{ll}{, jj}" + format.name$ + bibinfo bibinfo.check + 't := + nameptr #1 > + { + namesleft #1 > + { ", " * t * } + { + numnames #2 > + { "," * } + 'skip$ + if$ + s nameptr "{ll}" format.name$ duplicate$ "others" = + { 't := } + { pop$ } + if$ + t "others" = + { + + " " * bbl.etal * + } + { + bbl.and + space.word * t * + } + if$ + } + if$ + } + 't + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ + } if$ +} +FUNCTION {format.key} +{ empty$ + { key field.or.null } + { "" } + if$ +} + +FUNCTION {format.authors} +{ author "author" format.names +} +FUNCTION {get.bbl.editor} +{ editor num.names$ #1 > 'bbl.editors 'bbl.editor if$ } + +FUNCTION {format.editors} +{ editor "editor" format.names duplicate$ empty$ 'skip$ + { + "," * + " " * + get.bbl.editor + * + } + if$ +} +FUNCTION {format.note} +{ + note empty$ + { "" } + { note #1 #1 substring$ + duplicate$ "{" = + 'skip$ + { output.state mid.sentence = + { "l" } + { "u" } + if$ + change.case$ + } + if$ + note #2 global.max$ substring$ * "note" bibinfo.check + } + if$ +} + +FUNCTION {format.title} +{ title + duplicate$ empty$ 'skip$ + { "t" change.case$ } + if$ + "title" bibinfo.check +} +FUNCTION {format.full.names} +{'s := + "" 't := + #1 'nameptr := + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + "{vv~}{ll}" format.name$ + 't := + nameptr #1 > + { + namesleft #1 > + { ", " * t * } + { + s nameptr "{ll}" format.name$ duplicate$ "others" = + { 't := } + { pop$ } + if$ + t "others" = + { + " " * bbl.etal * + } + { + numnames #2 > + { "," * } + 'skip$ + if$ + bbl.and + space.word * t * + } + if$ + } + if$ + } + 't + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ +} + +FUNCTION {author.editor.key.full} +{ author empty$ + { editor empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { editor format.full.names } + if$ + } + { author format.full.names } + if$ +} + +FUNCTION {author.key.full} +{ author empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { author format.full.names } + if$ +} + +FUNCTION {editor.key.full} +{ editor empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { editor format.full.names } + if$ +} + +FUNCTION {make.full.names} +{ type$ "book" = + type$ "inbook" = + or + 'author.editor.key.full + { type$ "proceedings" = + 'editor.key.full + 'author.key.full + if$ + } + if$ +} + +FUNCTION {output.bibitem.original} % urlbst (renamed from output.bibitem, so it can be wrapped below) +{ newline$ + "\bibitem[{" write$ + label write$ + ")" make.full.names duplicate$ short.list = + { pop$ } + { * } + if$ + "}]{" * write$ + cite$ write$ + "}" write$ + newline$ + "" + before.all 'output.state := +} + +FUNCTION {n.dashify} +{ + 't := + "" + { t empty$ not } + { t #1 #1 substring$ "-" = + { t #1 #2 substring$ "--" = not + { "--" * + t #2 global.max$ substring$ 't := + } + { { t #1 #1 substring$ "-" = } + { "-" * + t #2 global.max$ substring$ 't := + } + while$ + } + if$ + } + { t #1 #1 substring$ * + t #2 global.max$ substring$ 't := + } + if$ + } + while$ +} + +FUNCTION {word.in} +{ bbl.in capitalize + " " * } + +FUNCTION {format.date} +{ year "year" bibinfo.check duplicate$ empty$ + { + } + 'skip$ + if$ + extra.label * + before.all 'output.state := + after.sentence 'output.state := +} +FUNCTION {format.btitle} +{ title "title" bibinfo.check + duplicate$ empty$ 'skip$ + { + emphasize + } + if$ +} +FUNCTION {either.or.check} +{ empty$ + 'pop$ + { "can't use both " swap$ * " fields in " * cite$ * warning$ } + if$ +} +FUNCTION {format.bvolume} +{ volume empty$ + { "" } + { bbl.volume volume tie.or.space.prefix + "volume" bibinfo.check * * + series "series" bibinfo.check + duplicate$ empty$ 'pop$ + { swap$ bbl.of space.word * swap$ + emphasize * } + if$ + "volume and number" number either.or.check + } + if$ +} +FUNCTION {format.number.series} +{ volume empty$ + { number empty$ + { series field.or.null } + { series empty$ + { number "number" bibinfo.check } + { output.state mid.sentence = + { bbl.number } + { bbl.number capitalize } + if$ + number tie.or.space.prefix "number" bibinfo.check * * + bbl.in space.word * + series "series" bibinfo.check * + } + if$ + } + if$ + } + { "" } + if$ +} + +FUNCTION {format.edition} +{ edition duplicate$ empty$ 'skip$ + { + output.state mid.sentence = + { "l" } + { "t" } + if$ change.case$ + "edition" bibinfo.check + " " * bbl.edition * + } + if$ +} +INTEGERS { multiresult } +FUNCTION {multi.page.check} +{ 't := + #0 'multiresult := + { multiresult not + t empty$ not + and + } + { t #1 #1 substring$ + duplicate$ "-" = + swap$ duplicate$ "," = + swap$ "+" = + or or + { #1 'multiresult := } + { t #2 global.max$ substring$ 't := } + if$ + } + while$ + multiresult +} +FUNCTION {format.pages} +{ pages duplicate$ empty$ 'skip$ + { duplicate$ multi.page.check + { + bbl.pages swap$ + n.dashify + } + { + bbl.page swap$ + } + if$ + tie.or.space.prefix + "pages" bibinfo.check + * * + } + if$ +} +FUNCTION {format.journal.pages} +{ pages duplicate$ empty$ 'pop$ + { swap$ duplicate$ empty$ + { pop$ pop$ format.pages } + { + ":" * + swap$ + n.dashify + "pages" bibinfo.check + * + } + if$ + } + if$ +} +FUNCTION {format.vol.num.pages} +{ volume field.or.null + duplicate$ empty$ 'skip$ + { + "volume" bibinfo.check + } + if$ + number "number" bibinfo.check duplicate$ empty$ 'skip$ + { + swap$ duplicate$ empty$ + { "there's a number but no volume in " cite$ * warning$ } + 'skip$ + if$ + swap$ + "(" swap$ * ")" * + } + if$ * + format.journal.pages +} + +FUNCTION {format.chapter} +{ chapter empty$ + 'format.pages + { type empty$ + { bbl.chapter } + { type "l" change.case$ + "type" bibinfo.check + } + if$ + chapter tie.or.space.prefix + "chapter" bibinfo.check + * * + } + if$ +} + +FUNCTION {format.chapter.pages} +{ chapter empty$ + 'format.pages + { type empty$ + { bbl.chapter } + { type "l" change.case$ + "type" bibinfo.check + } + if$ + chapter tie.or.space.prefix + "chapter" bibinfo.check + * * + pages empty$ + 'skip$ + { ", " * format.pages * } + if$ + } + if$ +} + +FUNCTION {format.booktitle} +{ + booktitle "booktitle" bibinfo.check + emphasize +} +FUNCTION {format.in.booktitle} +{ format.booktitle duplicate$ empty$ 'skip$ + { + word.in swap$ * + } + if$ +} +FUNCTION {format.in.ed.booktitle} +{ format.booktitle duplicate$ empty$ 'skip$ + { + editor "editor" format.names.ed duplicate$ empty$ 'pop$ + { + "," * + " " * + get.bbl.editor + ", " * + * swap$ + * } + if$ + word.in swap$ * + } + if$ +} +FUNCTION {format.thesis.type} +{ type duplicate$ empty$ + 'pop$ + { swap$ pop$ + "t" change.case$ "type" bibinfo.check + } + if$ +} +FUNCTION {format.tr.number} +{ number "number" bibinfo.check + type duplicate$ empty$ + { pop$ bbl.techrep } + 'skip$ + if$ + "type" bibinfo.check + swap$ duplicate$ empty$ + { pop$ "t" change.case$ } + { tie.or.space.prefix * * } + if$ +} +FUNCTION {format.article.crossref} +{ + word.in + " \cite{" * crossref * "}" * +} +FUNCTION {format.book.crossref} +{ volume duplicate$ empty$ + { "empty volume in " cite$ * "'s crossref of " * crossref * warning$ + pop$ word.in + } + { bbl.volume + capitalize + swap$ tie.or.space.prefix "volume" bibinfo.check * * bbl.of space.word * + } + if$ + " \cite{" * crossref * "}" * +} +FUNCTION {format.incoll.inproc.crossref} +{ + word.in + " \cite{" * crossref * "}" * +} +FUNCTION {format.org.or.pub} +{ 't := + "" + address empty$ t empty$ and + 'skip$ + { + t empty$ + { address "address" bibinfo.check * + } + { t * + address empty$ + 'skip$ + { ", " * address "address" bibinfo.check * } + if$ + } + if$ + } + if$ +} +FUNCTION {format.publisher.address} +{ publisher "publisher" bibinfo.warn format.org.or.pub +} + +FUNCTION {format.organization.address} +{ organization "organization" bibinfo.check format.org.or.pub +} + +% urlbst... +% Functions for making hypertext links. +% In all cases, the stack has (link-text href-url) +% +% make 'null' specials +FUNCTION {make.href.null} +{ + pop$ +} +% make hypertex specials +FUNCTION {make.href.hypertex} +{ + "\special {html: }" * swap$ * + "\special {html:}" * +} +% make hyperref specials +FUNCTION {make.href.hyperref} +{ + "\href {" swap$ * "} {\path{" * swap$ * "}}" * +} +FUNCTION {make.href} +{ hrefform #2 = + 'make.href.hyperref % hrefform = 2 + { hrefform #1 = + 'make.href.hypertex % hrefform = 1 + 'make.href.null % hrefform = 0 (or anything else) + if$ + } + if$ +} + +% If inlinelinks is true, then format.url should be a no-op, since it's +% (a) redundant, and (b) could end up as a link-within-a-link. +FUNCTION {format.url} +{ inlinelinks #1 = url empty$ or + { "" } + { hrefform #1 = + { % special case -- add HyperTeX specials + urlintro "\url{" url * "}" * url make.href.hypertex * } + { urlintro "\url{" * url * "}" * } + if$ + } + if$ +} + +FUNCTION {format.eprint} +{ eprint empty$ + { "" } + { eprintprefix eprint * eprinturl eprint * make.href } + if$ +} + +FUNCTION {format.doi} +{ doi empty$ + { "" } + { doiprefix doi * doiurl doi * make.href } + if$ +} + +FUNCTION {format.pubmed} +{ pubmed empty$ + { "" } + { pubmedprefix pubmed * pubmedurl pubmed * make.href } + if$ +} + +% Output a URL. We can't use the more normal idiom (something like +% `format.url output'), because the `inbrackets' within +% format.lastchecked applies to everything between calls to `output', +% so that `format.url format.lastchecked * output' ends up with both +% the URL and the lastchecked in brackets. +FUNCTION {output.url} +{ url empty$ + 'skip$ + { new.block + format.url output + format.lastchecked output + } + if$ +} + +FUNCTION {output.web.refs} +{ + new.block + inlinelinks + 'skip$ % links were inline -- don't repeat them + { + output.url + addeprints eprint empty$ not and + { format.eprint output.nonnull } + 'skip$ + if$ + adddoiresolver doi empty$ not and + { format.doi output.nonnull } + 'skip$ + if$ + addpubmedresolver pubmed empty$ not and + { format.pubmed output.nonnull } + 'skip$ + if$ + } + if$ +} + +% Wrapper for output.bibitem.original. +% If the URL field is not empty, set makeinlinelink to be true, +% so that an inline link will be started at the next opportunity +FUNCTION {output.bibitem} +{ outside.brackets 'bracket.state := + output.bibitem.original + inlinelinks url empty$ not doi empty$ not or pubmed empty$ not or eprint empty$ not or and + { #1 'makeinlinelink := } + { #0 'makeinlinelink := } + if$ +} + +% Wrapper for fin.entry.original +FUNCTION {fin.entry} +{ output.web.refs % urlbst + makeinlinelink % ooops, it appears we didn't have a title for inlinelink + { possibly.setup.inlinelink % add some artificial link text here, as a fallback + linktextstring output.nonnull } + 'skip$ + if$ + bracket.state close.brackets = % urlbst + { "]" * } + 'skip$ + if$ + fin.entry.original +} + +% Webpage entry type. +% Title and url fields required; +% author, note, year, month, and lastchecked fields optional +% See references +% ISO 690-2 http://www.nlc-bnc.ca/iso/tc46sc9/standard/690-2e.htm +% http://www.classroom.net/classroom/CitingNetResources.html +% http://neal.ctstateu.edu/history/cite.html +% http://www.cas.usf.edu/english/walker/mla.html +% for citation formats for web pages. +FUNCTION {webpage} +{ output.bibitem + author empty$ + { editor empty$ + 'skip$ % author and editor both optional + { format.editors output.nonnull } + if$ + } + { editor empty$ + { format.authors output.nonnull } + { "can't use both author and editor fields in " cite$ * warning$ } + if$ + } + if$ + new.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ + format.title "title" output.check + inbrackets onlinestring output + new.block + year empty$ + 'skip$ + { format.date "year" output.check } + if$ + % We don't need to output the URL details ('lastchecked' and 'url'), + % because fin.entry does that for us, using output.web.refs. The only + % reason we would want to put them here is if we were to decide that + % they should go in front of the rather miscellaneous information in 'note'. + new.block + note output + fin.entry +} +% ...urlbst to here + + +FUNCTION {article} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title "title" output.check + new.block + crossref missing$ + { + journal + "journal" bibinfo.check + emphasize + "journal" output.check + possibly.setup.inlinelink format.vol.num.pages output% urlbst + } + { format.article.crossref output.nonnull + format.pages output + } + if$ + new.block + format.note output + fin.entry +} +FUNCTION {book} +{ output.bibitem + author empty$ + { format.editors "author and editor" output.check + editor format.key output + } + { format.authors output.nonnull + crossref missing$ + { "author and editor" editor either.or.check } + 'skip$ + if$ + } + if$ + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.btitle "title" output.check + format.edition output + crossref missing$ + { format.bvolume output + new.block + format.number.series output + new.sentence + format.publisher.address output + } + { + new.block + format.book.crossref output.nonnull + } + if$ + new.block + format.note output + fin.entry +} +FUNCTION {booklet} +{ output.bibitem + format.authors output + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title "title" output.check + new.block + howpublished "howpublished" bibinfo.check output + address "address" bibinfo.check output + new.block + format.note output + fin.entry +} + +FUNCTION {inbook} +{ output.bibitem + author empty$ + { format.editors "author and editor" output.check + editor format.key output + } + { format.authors output.nonnull + crossref missing$ + { "author and editor" editor either.or.check } + 'skip$ + if$ + } + if$ + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.btitle "title" output.check + format.edition output + crossref missing$ + { + format.bvolume output + format.number.series output + format.chapter "chapter" output.check + new.sentence + format.publisher.address output + new.block + } + { + format.chapter "chapter" output.check + new.block + format.book.crossref output.nonnull + } + if$ + new.block + format.note output + fin.entry +} + +FUNCTION {incollection} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title "title" output.check + new.block + crossref missing$ + { format.in.ed.booktitle "booktitle" output.check + format.edition output + format.bvolume output + format.number.series output + format.chapter.pages output + new.sentence + format.publisher.address output + } + { format.incoll.inproc.crossref output.nonnull + format.chapter.pages output + } + if$ + new.block + format.note output + fin.entry +} +FUNCTION {inproceedings} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title "title" output.check + new.block + crossref missing$ + { format.in.booktitle "booktitle" output.check + format.bvolume output + format.number.series output + format.pages output + address "address" bibinfo.check output + new.sentence + organization "organization" bibinfo.check output + publisher "publisher" bibinfo.check output + } + { format.incoll.inproc.crossref output.nonnull + format.pages output + } + if$ + new.block + format.note output + fin.entry +} +FUNCTION {conference} { inproceedings } +FUNCTION {manual} +{ output.bibitem + format.authors output + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.btitle "title" output.check + format.edition output + organization address new.block.checkb + organization "organization" bibinfo.check output + address "address" bibinfo.check output + new.block + format.note output + fin.entry +} + +FUNCTION {mastersthesis} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title + "title" output.check + new.block + bbl.mthesis format.thesis.type output.nonnull + school "school" bibinfo.warn output + address "address" bibinfo.check output + month "month" bibinfo.check output + new.block + format.note output + fin.entry +} + +FUNCTION {misc} +{ output.bibitem + format.authors output + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title output + new.block + howpublished "howpublished" bibinfo.check output + new.block + format.note output + fin.entry +} +FUNCTION {phdthesis} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.btitle + "title" output.check + new.block + bbl.phdthesis format.thesis.type output.nonnull + school "school" bibinfo.warn output + address "address" bibinfo.check output + new.block + format.note output + fin.entry +} + +FUNCTION {proceedings} +{ output.bibitem + format.editors output + editor format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.btitle "title" output.check + format.bvolume output + format.number.series output + new.sentence + publisher empty$ + { format.organization.address output } + { organization "organization" bibinfo.check output + new.sentence + format.publisher.address output + } + if$ + new.block + format.note output + fin.entry +} + +FUNCTION {techreport} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title + "title" output.check + new.block + format.tr.number output.nonnull + institution "institution" bibinfo.warn output + address "address" bibinfo.check output + new.block + format.note output + fin.entry +} + +FUNCTION {unpublished} +{ output.bibitem + format.authors "author" output.check + author format.key output + format.date "year" output.check + date.block + title empty$ 'skip$ 'possibly.setup.inlinelink if$ % urlbst + format.title "title" output.check + new.block + format.note "note" output.check + fin.entry +} + +FUNCTION {default.type} { misc } +READ +FUNCTION {sortify} +{ purify$ + "l" change.case$ +} +INTEGERS { len } +FUNCTION {chop.word} +{ 's := + 'len := + s #1 len substring$ = + { s len #1 + global.max$ substring$ } + 's + if$ +} +FUNCTION {format.lab.names} +{ 's := + "" 't := + s #1 "{vv~}{ll}" format.name$ + s num.names$ duplicate$ + #2 > + { pop$ + " " * bbl.etal * + } + { #2 < + 'skip$ + { s #2 "{ff }{vv }{ll}{ jj}" format.name$ "others" = + { + " " * bbl.etal * + } + { bbl.and space.word * s #2 "{vv~}{ll}" format.name$ + * } + if$ + } + if$ + } + if$ +} + +FUNCTION {author.key.label} +{ author empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { author format.lab.names } + if$ +} + +FUNCTION {author.editor.key.label} +{ author empty$ + { editor empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { editor format.lab.names } + if$ + } + { author format.lab.names } + if$ +} + +FUNCTION {editor.key.label} +{ editor empty$ + { key empty$ + { cite$ #1 #3 substring$ } + 'key + if$ + } + { editor format.lab.names } + if$ +} + +FUNCTION {calc.short.authors} +{ type$ "book" = + type$ "inbook" = + or + 'author.editor.key.label + { type$ "proceedings" = + 'editor.key.label + 'author.key.label + if$ + } + if$ + 'short.list := +} + +FUNCTION {calc.label} +{ calc.short.authors + short.list + "(" + * + year duplicate$ empty$ + short.list key field.or.null = or + { pop$ "" } + 'skip$ + if$ + * + 'label := +} + +FUNCTION {sort.format.names} +{ 's := + #1 'nameptr := + "" + s num.names$ 'numnames := + numnames 'namesleft := + { namesleft #0 > } + { s nameptr + "{vv{ } }{ll{ }}{ ff{ }}{ jj{ }}" + format.name$ 't := + nameptr #1 > + { + " " * + namesleft #1 = t "others" = and + { "zzzzz" * } + { t sortify * } + if$ + } + { t sortify * } + if$ + nameptr #1 + 'nameptr := + namesleft #1 - 'namesleft := + } + while$ +} + +FUNCTION {sort.format.title} +{ 't := + "A " #2 + "An " #3 + "The " #4 t chop.word + chop.word + chop.word + sortify + #1 global.max$ substring$ +} +FUNCTION {author.sort} +{ author empty$ + { key empty$ + { "to sort, need author or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { author sort.format.names } + if$ +} +FUNCTION {author.editor.sort} +{ author empty$ + { editor empty$ + { key empty$ + { "to sort, need author, editor, or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { editor sort.format.names } + if$ + } + { author sort.format.names } + if$ +} +FUNCTION {editor.sort} +{ editor empty$ + { key empty$ + { "to sort, need editor or key in " cite$ * warning$ + "" + } + { key sortify } + if$ + } + { editor sort.format.names } + if$ +} +FUNCTION {presort} +{ calc.label + label sortify + " " + * + type$ "book" = + type$ "inbook" = + or + 'author.editor.sort + { type$ "proceedings" = + 'editor.sort + 'author.sort + if$ + } + if$ + #1 entry.max$ substring$ + 'sort.label := + sort.label + * + " " + * + title field.or.null + sort.format.title + * + #1 entry.max$ substring$ + 'sort.key$ := +} + +ITERATE {presort} +SORT +STRINGS { last.label next.extra } +INTEGERS { last.extra.num number.label } +FUNCTION {initialize.extra.label.stuff} +{ #0 int.to.chr$ 'last.label := + "" 'next.extra := + #0 'last.extra.num := + #0 'number.label := +} +FUNCTION {forward.pass} +{ last.label label = + { last.extra.num #1 + 'last.extra.num := + last.extra.num int.to.chr$ 'extra.label := + } + { "a" chr.to.int$ 'last.extra.num := + "" 'extra.label := + label 'last.label := + } + if$ + number.label #1 + 'number.label := +} +FUNCTION {reverse.pass} +{ next.extra "b" = + { "a" 'extra.label := } + 'skip$ + if$ + extra.label 'next.extra := + extra.label + duplicate$ empty$ + 'skip$ + { year field.or.null #-1 #1 substring$ chr.to.int$ #65 < + { "{\natexlab{" swap$ * "}}" * } + { "{(\natexlab{" swap$ * "})}" * } + if$ } + if$ + 'extra.label := + label extra.label * 'label := +} +EXECUTE {initialize.extra.label.stuff} +ITERATE {forward.pass} +REVERSE {reverse.pass} +FUNCTION {bib.sort.order} +{ sort.label + " " + * + year field.or.null sortify + * + " " + * + title field.or.null + sort.format.title + * + #1 entry.max$ substring$ + 'sort.key$ := +} +ITERATE {bib.sort.order} +SORT +FUNCTION {begin.bib} +{ preamble$ empty$ + 'skip$ + { preamble$ write$ newline$ } + if$ + "\begin{thebibliography}{" number.label int.to.str$ * "}" * + write$ newline$ + "\expandafter\ifx\csname natexlab\endcsname\relax\def\natexlab#1{#1}\fi" + write$ newline$ +} +EXECUTE {begin.bib} +EXECUTE {init.urlbst.variables} % urlbst +EXECUTE {init.state.consts} +ITERATE {call.type$} +FUNCTION {end.bib} +{ newline$ + "\end{thebibliography}" write$ newline$ +} +EXECUTE {end.bib} +%% End of customized bst file +%% +%% End of file `compling.bst'. \ No newline at end of file diff --git a/references/2023.arxiv.nguyen/source/anthology.bib b/references/2023.arxiv.nguyen/source/anthology.bib new file mode 100644 index 0000000000000000000000000000000000000000..a130d8240d11acf1f46a05f24e071391c40b2395 --- /dev/null +++ b/references/2023.arxiv.nguyen/source/anthology.bib @@ -0,0 +1,570 @@ +% Unsupervised Cross-lingual Representation Learning at Scale +@inproceedings{xlm-r, + title = "{Unsupervised Cross-lingual Representation Learning at Scale}", + author = "Conneau, Alexis and + Khandelwal, Kartikay and + Goyal, Naman and + Chaudhary, Vishrav and + Wenzek, Guillaume and + Guzm{\'a}n, Francisco and + Grave, Edouard and + Ott, Myle and + Zettlemoyer, Luke and + Stoyanov, Veselin", + booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", + month = jul, + year = "2020", + address = "Online", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2020.acl-main.747", + doi = "10.18653/v1/2020.acl-main.747", + pages = "8440--8451", +} +% {P}ho{BERT}: Pre-trained language models for {V}ietnamese +@inproceedings{phobert, + title = "{P}ho{BERT}: Pre-trained language models for {V}ietnamese", + author = "Nguyen, Dat Quoc and + Tuan Nguyen, Anh", + booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020", + month = nov, + year = "2020", + address = "Online", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2020.findings-emnlp.92", + doi = "10.18653/v1/2020.findings-emnlp.92", + pages = "1037--1042", +} +% RoBERTa: A Robustly Optimized BERT Pretraining Approach +@misc{roberta, + title="{RoBERTa: A Robustly Optimized BERT Pretraining Approach}", + author={Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and Luke Zettlemoyer and Veselin Stoyanov}, + year={2019}, + eprint={1907.11692}, + archivePrefix={arXiv}, + primaryClass={cs.CL} +} +% Improving Sequence Tagging for Vietnamese Text Using Transformer-based Neural Models +@inproceedings{viBERTandvELECTRA, + title="{Improving sequence tagging for Vietnamese text using transformer-based neural models}", + author={Tran, Thi Oanh and Le Hong, Phuong and others}, + booktitle={Proceedings of the 34th Pacific Asia conference on language, information and computation}, + pages={13--20}, + year={2020}, + url={https://aclanthology.org/2020.paclic-1.2/} +} +% ViDeBERTa: A powerful pre-trained language model for Vietnamese +@inproceedings{viDeBerTa, + title="{ViDeBERTa: A powerful pre-trained language model for Vietnamese}", + author={Tran, Cong Dao and Pham, Nhut Huy and Nguyen, Anh-Tuan and Hy, Truong Son and Vu, Tu}, + booktitle={Findings of the Association for Computational Linguistics: EACL 2023}, + pages={1041--1048}, + url={https://aclanthology.org/2023.findings-eacl.79/}, + year={2023} +} + +% BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese +@inproceedings{BARTpho, + title = {{BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese}}, + author = {Nguyen Luong Tran and Duong Minh Le and Dat Quoc Nguyen}, + booktitle = {Proceedings of the 23rd Annual Conference of the International Speech Communication Association}, + year = {2022}, + url={https://arxiv.org/abs/2109.09701} +} +% ViT5: Pretrained Text-to-Text Transformer for Vietnamese Language Generation +@inproceedings{ViT5, + title = "{V}i{T}5: Pretrained Text-to-Text Transformer for {V}ietnamese Language Generation", + author = "Phan, Long and Tran, Hieu and Nguyen, Hieu and Trinh, Trieu H.", + booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop", + year = "2022", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2022.naacl-srw.18", + pages = "136--142", +} +@inproceedings{vihealthbert, + title = "{V}i{H}ealth{BERT}: Pre-trained Language Models for {V}ietnamese in Health Text Mining", + author = "Minh, Nguyen and + Tran, Vu Hoang and + Hoang, Vu and + Ta, Huy Duc and + Bui, Trung Huu and + Truong, Steven Quoc Hung", + booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference", + month = jun, + year = "2022", + address = "Marseille, France", + publisher = "European Language Resources Association", + url = "https://aclanthology.org/2022.lrec-1.35", + pages = "328--337", +} +% Attention Is All You Need +@article{attentionisallyouneed, + title="{Attention is all you need}", + author={Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, {\L}ukasz and Polosukhin, Illia}, + journal={Advances in neural information processing systems}, + volume={30}, + year={2017}, + url={https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf} +} + + +% {BERT}weet: A pre-trained language model for {E}nglish Tweets +@inproceedings{bertweet, + title = "{BERT}weet: A pre-trained language model for {E}nglish Tweets", + author = "Nguyen, Dat Quoc and + Vu, Thanh and + Tuan Nguyen, Anh", + booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", + month = oct, + year = "2020", + address = "Online", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2020.emnlp-demos.2", + doi = "10.18653/v1/2020.emnlp-demos.2", + pages = "9--14", +} +% {R}o{BERT}uito: a pre-trained language model for social media text in {S}panish +@inproceedings{robertuito, + title = "{R}o{BERT}uito: a pre-trained language model for social media text in {S}panish", + author = "P{\'e}rez, Juan Manuel and + Furman, Dami{\'a}n Ariel and + Alonso Alemany, Laura and + Luque, Franco M.", + booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference", + month = jun, + year = "2022", + address = "Marseille, France", + publisher = "European Language Resources Association", + url = "https://aclanthology.org/2022.lrec-1.785", + pages = "7235--7243", +} +% AlBERTo: Italian {BERT} Language Understanding Model for {NLP} Challenging Tasks Based on Tweets +@inproceedings{alberto, + author = {Marco Polignano and + Pierpaolo Basile and + Marco de Gemmis and + Giovanni Semeraro and + Valerio Basile}, + editor = {Raffaella Bernardi and + Roberto Navigli and + Giovanni Semeraro}, + title = {AlBERTo: Italian {BERT} Language Understanding Model for {NLP} Challenging + Tasks Based on Tweets}, + booktitle = {Proceedings of the Sixth Italian Conference on Computational Linguistics, + Bari, Italy, November 13-15, 2019}, + series = {{CEUR} Workshop Proceedings}, + volume = {2481}, + publisher = {CEUR-WS.org}, + year = {2019}, + url = {https://ceur-ws.org/Vol-2481/paper57.pdf}, + timestamp = {Fri, 10 Mar 2023 16:22:17 +0100}, + biburl = {https://dblp.org/rec/conf/clic-it/PolignanoBGSB19.bib}, + bibsource = {dblp computer science bibliography, https://dblp.org} +} +% TweetBERT: A Pretrained Language Representation Model for Twitter Text Analysis +@misc{tweetbert, + title="{TweetBERT: A Pretrained Language Representation Model for Twitter Text Analysis}", + author={Mohiuddin Md Abdul Qudar and Vijay Mago}, + year={2020}, + eprint={2010.11091}, + archivePrefix={arXiv}, + primaryClass={cs.CL} +} +% TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations +@article{twhinbert, + title="{TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations}", + author={Zhang, Xinyang and Malkov, Yury and Florez, Omar and Park, Serim and McWilliams, Brian and Han, Jiawei and El-Kishky, Ahmed}, + journal={arXiv preprint arXiv:2209.07562}, + year={2022}, + url={https://arxiv.org/abs/2209.07562} +} +% Bernice: A Multilingual Pre-trained Encoder for {T}witter +@inproceedings{bernice, + title = "{Bernice: A Multilingual Pre-trained Encoder for Twitter}", + author = "DeLucia, Alexandra and + Wu, Shijie and + Mueller, Aaron and + Aguirre, Carlos and + Resnik, Philip and + Dredze, Mark", + booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", + month = dec, + year = "2022", + address = "Abu Dhabi, United Arab Emirates", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2022.emnlp-main.415", + pages = "6191--6205", +} +% {I}ndo{BERT}weet: A Pretrained Language Model for {I}ndonesian {T}witter with Effective Domain-Specific Vocabulary Initialization +@inproceedings{koto-etal-2021-indobertweet, + title = "{I}ndo{BERT}weet: A Pretrained Language Model for {I}ndonesian {T}witter with Effective Domain-Specific Vocabulary Initialization", + author = "Koto, Fajri and + Lau, Jey Han and + Baldwin, Timothy", + booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", + month = nov, + year = "2021", + address = "Online and Punta Cana, Dominican Republic", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2021.emnlp-main.833", + doi = "10.18653/v1/2021.emnlp-main.833", + pages = "10660--10668", +} +% Transformer based contextualization of pre-trained word embeddings for irony detection in Twitter +@article{GONZALEZ2020102262, + title = "{Transformer based contextualization of pre-trained word embeddings for irony detection in Twitter}", + journal = {Information Processing \& Management}, + volume = {57}, + number = {4}, + pages = {102262}, + year = {2020}, + issn = {0306-4573}, + doi = {https://doi.org/10.1016/j.ipm.2020.102262}, + url = {https://www.sciencedirect.com/science/article/pii/S0306457320300200}, + author = {José Ángel González and Lluís-F. Hurtado and Ferran Pla}, +} +% TWilBert: Pre-trained deep bidirectional transformers for Spanish Twitter +@article{twilbert, + title = "{TWilBert: Pre-trained deep bidirectional transformers for Spanish Twitter}", + journal = {Neurocomputing}, + volume = {426}, + pages = {58-69}, + year = {2021}, + issn = {0925-2312}, + doi = {https://doi.org/10.1016/j.neucom.2020.09.078}, + url = {https://www.sciencedirect.com/science/article/pii/S0925231220316180}, + author = {José Ángel González and Lluís-F. Hurtado and Ferran Pla}, + keywords = {Contextualized Embeddings, Spanish, Twitter, TWilBERT}, +} +% Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer +@article{raffel2020exploring, + title="{Exploring the limits of transfer learning with a unified text-to-text transformer}", + author={Raffel, Colin and Shazeer, Noam and Roberts, Adam and Lee, Katherine and Narang, Sharan and Matena, Michael and Zhou, Yanqi and Li, Wei and Liu, Peter J}, + journal={The Journal of Machine Learning Research}, + volume={21}, + number={1}, + pages={5485--5551}, + year={2020}, + publisher={JMLRORG}, + url={https://dl.acm.org/doi/abs/10.5555/3455716.3455856} +} + +% Exploiting Vietnamese Social Media Characteristics for Textual Emotion Recognition in Vietnamese + +@inproceedings{ho2020emotion, + title="{Emotion Recognition for Vietnamese Social Media Text}", + author={Ho, Vong Anh and Nguyen, Duong Huynh-Cong and Nguyen, Danh Hoang and Pham, Linh Thi-Van and Nguyen, Duc-Vu and Nguyen, Kiet Van and Nguyen, Ngan Luu-Thuy}, + booktitle={Computational Linguistics: 16th International Conference of the Pacific Association for Computational Linguistics, PACLING 2019, Hanoi, Vietnam, October 11--13, 2019, Revised Selected Papers 16}, + pages={319--333}, + year={2020}, + url={https://link.springer.com/chapter/10.1007/978-981-15-6168-9_27}, + organization={Springer} +} + +% Constructive and Toxic Speech Detection for Open-Domain Social Media Comments in Vietnamese +@incollection{victsd, + doi = {10.1007/978-3-030-79457-6_49}, + year = 2021, + publisher = {Springer International Publishing}, + pages = {572--583}, + author = {Luan Thanh Nguyen and Kiet Van Nguyen and Ngan Luu-Thuy Nguyen}, + title = "{Constructive and Toxic Speech Detection for Open-Domain Social Media Comments in Vietnamese}", + booktitle = {Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices} +} +% A Large-Scale Dataset for Hate Speech Detection on Vietnamese Social Media Texts +@incollection{vihsd, + doi = {10.1007/978-3-030-79457-6_35}, + year = 2021, + publisher = {Springer International Publishing}, + pages = {415--426}, + author = {Son T. Luu and Kiet Van Nguyen and Ngan Luu-Thuy Nguyen}, + title = "{A Large-Scale Dataset for Hate Speech Detection on Vietnamese Social Media Texts}", + booktitle = {Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices} +} +% Vietnamese Complaint Detection on E-Commerce Websites +@incollection{viocd, + title="{Vietnamese Complaint Detection on E-Commerce Websites}", + author={Nguyen, Nhung Thi-Hong and Ha, Phuong Phan-Dieu and Nguyen, Luan Thanh and Nguyen, Kiet Van and Nguyen, Ngan Luu-Thuy}, + booktitle={New Trends in Intelligent Software Methodologies, Tools and Techniques}, + pages={618--629}, + year={2021}, + publisher={IOS Press} +} + +% ViHOS: Hate Speech Spans Detection for Vietnamese +@inproceedings{vihos, + title="{ViHOS: Hate Speech Spans Detection for Vietnamese}", + author={Hoang, Phu Gia and Luu, Canh and Tran, Khanh and Nguyen, Kiet and Nguyen, Ngan}, + booktitle={Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics}, + pages={652--669}, + url={https://aclanthology.org/2023.eacl-main.47/}, + year={2023} +} + +% ViSpamReviews: Detecting Spam Reviews on~Vietnamese E-Commerce Websites +@incollection{vispamreviews, + doi = {10.1007/978-3-031-21743-2_48}, + url = {https://doi.org/10.1007\%2F978-3-031-21743-2_48}, + year = 2022, + publisher = {Springer International Publishing}, + pages = {595--607}, + author = {Co Van Dinh and Son T. Luu and Anh Gia-Tuan Nguyen}, + title = "{Detecting Spam Reviews on Vietnamese E-Commerce Websites}", + booktitle = {Intelligent Information and Database Systems} +} +@inproceedings{postagging, + title="{Vietnamese POS Tagging for Social Media Text}", + author={Ngo Xuan Bach and Nguyen Dieu Linh and Tu Minh Phuong}, + booktitle={International Conference on Neural Information Processing}, + year={2016} +} +@article{phobertcnn, + title="{Vietnamese hate and offensive detection using PhoBERT-CNN and social media streaming data}", + author={Quoc Tran, Khanh and Trong Nguyen, An and Hoang, Phu Gia and Luu, Canh Duc and Do, Trong-Hop and Van Nguyen, Kiet}, + journal={Neural Computing and Applications}, + volume={35}, + number={1}, + pages={573--594}, + year={2023}, + publisher={Springer}, + url={https://link.springer.com/article/10.1007/s00521-022-07745-w} + +} + +@inproceedings{smtce, + title = "{SMTCE}: A Social Media Text Classification Evaluation Benchmark and {BERT}ology Models for {V}ietnamese", + author = "Nguyen, Luan and + Nguyen, Kiet and + Nguyen, Ngan", + booktitle = "Proceedings of the 36th Pacific Asia Conference on Language, Information and Computation", + month = oct, + year = "2022", + address = "Manila, Philippines", + publisher = "De La Salle University", + url = "https://aclanthology.org/2022.paclic-1.31", + pages = "282--291", +} + +@article{adam, + title="{Adam: A method for stochastic optimization}", + author={Kingma, Diederik P and Ba, Jimmy}, + journal={arXiv preprint arXiv:1412.6980}, + year={2014}, + url={arXiv preprint arXiv:1412.6980} +} + +@inproceedings{shouldyoumask, + title = "{Should You Mask 15{\%} in Masked Language Modeling?}", + author = "Wettig, Alexander and + Gao, Tianyu and + Zhong, Zexuan and + Chen, Danqi", + booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics", + month = may, + year = "2023", + address = "Dubrovnik, Croatia", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2023.eacl-main.217", + pages = "2985--3000", +} +@article{xlm, + title="{Cross-lingual Language Model Pretraining}", + author={Lample, Guillaume and Conneau, Alexis}, + journal={Advances in Neural Information Processing Systems (NeurIPS)}, + year={2019}, + url={https://proceedings.neurips.cc/paper/2019/file/c04c19c2c2474dbf5f7ac4372c5b9af1-Paper.pdf} +} +@inproceedings{nguyen2020exploiting, + title="{Exploiting Vietnamese social media characteristics for textual emotion recognition in Vietnamese}", + author={Nguyen, Khang Phuoc-Quy and Van Nguyen, Kiet}, + booktitle={2020 International Conference on Asian Language Processing (IALP)}, + pages={276--281}, + year={2020}, + url={https://ieeexplore.ieee.org/document/9310495}, + organization={IEEE} +} +@inproceedings{sentencepiece, + title = "{{S}entence{P}iece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing}", + author = "Kudo, Taku and + Richardson, John", + booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", + month = nov, + year = "2018", + address = "Brussels, Belgium", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/D18-2012", + doi = "10.18653/v1/D18-2012", + pages = "66--71", +} + +@article{nguyen2018vlsp, + title="{VLSP Shared Task: Sentiment Analysis}", + author={Nguyen, Huyen TM and Nguyen, Hung V and Ngo, Quyen T and Vu, Luong X and Tran, Vu Mai and Ngo, Bach X and Le, Cuong A}, + journal={Journal of Computer Science and Cybernetics}, + volume={34}, + number={4}, + pages={295--310}, + year={2018}, + url={https://vjs.ac.vn/index.php/jcc/article/view/13160} +} +@article{diacritics, + title = "{Diacritics generation and application in hate speech detection on Vietnamese social networks}", + journal = {Knowledge-Based Systems}, + volume = {233}, + pages = {107504}, + year = {2021}, + issn = {0950-7051}, + doi = {https://doi.org/10.1016/j.knosys.2021.107504}, + url = {https://www.sciencedirect.com/science/article/pii/S0950705121007668}, + author = {Phuong Le-Hong}, + keywords = {Diacritics generation, Hate speech detection, Recurrent neural networks, Transformers, Sentiment analysis, Text, Vietnamese}, +} + +@article{rasmy2021med, + title="{Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction}", + author={Rasmy, Laila and Xiang, Yang and Xie, Ziqian and Tao, Cui and Zhi, Degui}, + journal={NPJ digital medicine}, + volume={4}, + number={1}, + pages={86}, + year={2021}, + url={https://www.nature.com/articles/s41746-021-00455-y}, + publisher={Nature Publishing Group UK London} +} + + +@article{biobert, + author = {Lee, Jinhyuk and Yoon, Wonjin and Kim, Sungdong and Kim, Donghyeon and Kim, Sunkyu and So, Chan Ho and Kang, Jaewoo}, + title = "{BioBERT: a pre-trained biomedical language representation model for biomedical text mining}", + journal = {Bioinformatics}, + volume = {36}, + number = {4}, + pages = {1234-1240}, + year = {2019}, + month = {09}, + issn = {1367-4803}, + doi = {10.1093/bioinformatics/btz682}, + url = {https://doi.org/10.1093/bioinformatics/btz682}, + eprint = {https://academic.oup.com/bioinformatics/article-pdf/36/4/1234/48983216/bioinformatics\_36\_4\_1234.pdf}, +} + +@inproceedings{beltagy2019scibert, + title="{SciBERT: A Pretrained Language Model for Scientific Text}", + author={Beltagy, Iz and Lo, Kyle and Cohan, Arman}, + booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)}, + pages={3615--3620}, + year={2019}, + url={https://aclanthology.org/D19-1371/} +} +@inproceedings{phonlp, + title = "{PhoNLP: A joint multi-task learning model for Vietnamese part-of-speech tagging, named entity recognition and dependency parsing}", + author = {Linh The Nguyen and Dat Quoc Nguyen}, + booktitle = {Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations}, + pages = {1--7}, + year = {2021}, + url={https://aclanthology.org/2021.naacl-demos.1/} +} + +@inproceedings{bert, + title="{BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding}", + author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina}, + booktitle={Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)}, + pages={4171--4186}, + year={2019}, + url={https://aclanthology.org/N19-1423/} +} + +@inproceedings{legalbert, + title = "{LEGAL-BERT: The Muppets straight out of Law School}", + author = "Chalkidis, Ilias and + Fergadiotis, Manos and + Malakasiotis, Prodromos and + Aletras, Nikolaos and + Androutsopoulos, Ion", + booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020", + month = nov, + year = "2020", + address = "Online", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2020.findings-emnlp.261", + doi = "10.18653/v1/2020.findings-emnlp.261", + pages = "2898--2904", +} +@inproceedings{conflibert, + title = "{ConfliBERT: A Pre-trained Language Model for Political Conflict and Violence}", + author = "Hu, Yibo and + Hosseini, MohammadSaleh and + Skorupa Parolin, Erick and + Osorio, Javier and + Khan, Latifur and + Brandt, Patrick and + D{'}Orazio, Vito", + booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", + month = jul, + year = "2022", + address = "Seattle, United States", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2022.naacl-main.400", + doi = "10.18653/v1/2022.naacl-main.400", + pages = "5469--5482", +} +@inproceedings{huynh-etal-2022-vinli, + title = "{ViNLI: A Vietnamese Corpus for Studies on Open-Domain Natural Language Inference}", + author = "Huynh, Tin Van and + Nguyen, Kiet Van and + Nguyen, Ngan Luu-Thuy", + booktitle = "Proceedings of the 29th International Conference on Computational Linguistics", + month = oct, + year = "2022", + address = "Gyeongju, Republic of Korea", + publisher = "International Committee on Computational Linguistics", + url = "https://aclanthology.org/2022.coling-1.339", + pages = "3858--3872", +} + +@InProceedings{AdamW, + title = "{Decoupled Weight Decay Regularization}", + author = "Ilya Loshchilov and Frank Hutter", + year = {2019}, + booktitle = "{Proceedings of the International Conference on Learning Representations}", + url = "https://openreview.net/forum?id=Bkg6RiCqY7" +} +@inproceedings{barbieri-etal-2022-xlm, + title = "{XLM}-{T}: Multilingual Language Models in {T}witter for Sentiment Analysis and Beyond", + author = "Barbieri, Francesco and + Espinosa Anke, Luis and + Camacho-Collados, Jose", + booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference", + month = jun, + year = "2022", + address = "Marseille, France", + publisher = "European Language Resources Association", + url = "https://aclanthology.org/2022.lrec-1.27", + pages = "258--266", +} +@inproceedings{delucia-etal-2022-bernice, + title = "Bernice: A Multilingual Pre-trained Encoder for {T}witter", + author = "DeLucia, Alexandra and + Wu, Shijie and + Mueller, Aaron and + Aguirre, Carlos and + Resnik, Philip and + Dredze, Mark", + booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", + month = dec, + year = "2022", + address = "Abu Dhabi, United Arab Emirates", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2022.emnlp-main.415", + doi = "10.18653/v1/2022.emnlp-main.415", + pages = "6191--6205", +} +@book{ngo2020vietnamese, + title={Vietnamese: An Essential Grammar}, + author={Ngo, B.}, + isbn={9781138210714}, + lccn={2020007686}, + series={Essential grammar}, + url={https://books.google.com.vn/books?id=-N1TzQEACAAJ}, + year={2020}, + publisher={Routledge, Taylor \& Francis Group} +} diff --git a/references/2023.arxiv.nguyen/source/emnlp2023.bbl b/references/2023.arxiv.nguyen/source/emnlp2023.bbl new file mode 100644 index 0000000000000000000000000000000000000000..2951626c635b5d93e902b9ee91e45b798acb4af8 --- /dev/null +++ b/references/2023.arxiv.nguyen/source/emnlp2023.bbl @@ -0,0 +1,193 @@ +\begin{thebibliography}{38} +\expandafter\ifx\csname natexlab\endcsname\relax\def\natexlab#1{#1}\fi + +\bibitem[{Barbieri et~al.(2022)Barbieri, Espinosa~Anke, and Camacho-Collados}]{barbieri-etal-2022-xlm} +Francesco Barbieri, Luis Espinosa~Anke, and Jose Camacho-Collados. 2022. +\newblock \href {https://aclanthology.org/2022.lrec-1.27} {{XLM}-{T}: Multilingual language models in {T}witter for sentiment analysis and beyond}. +\newblock In \emph{Proceedings of the Thirteenth Language Resources and Evaluation Conference}, pages 258--266, Marseille, France. European Language Resources Association. + +\bibitem[{Beltagy et~al.(2019)Beltagy, Lo, and Cohan}]{beltagy2019scibert} +Iz~Beltagy, Kyle Lo, and Arman Cohan. 2019. +\newblock \href {https://aclanthology.org/D19-1371/} {{SciBERT: A Pretrained Language Model for Scientific Text}}. +\newblock In \emph{Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)}, pages 3615--3620. + +\bibitem[{Chalkidis et~al.(2020)Chalkidis, Fergadiotis, Malakasiotis, Aletras, and Androutsopoulos}]{legalbert} +Ilias Chalkidis, Manos Fergadiotis, Prodromos Malakasiotis, Nikolaos Aletras, and Ion Androutsopoulos. 2020. +\newblock \href {https://doi.org/10.18653/v1/2020.findings-emnlp.261} {{LEGAL-BERT: The Muppets straight out of Law School}}. +\newblock In \emph{Findings of the Association for Computational Linguistics: EMNLP 2020}, pages 2898--2904, Online. Association for Computational Linguistics. + +\bibitem[{Conneau et~al.(2020)Conneau, Khandelwal, Goyal, Chaudhary, Wenzek, Guzm{\'a}n, Grave, Ott, Zettlemoyer, and Stoyanov}]{xlm-r} +Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzm{\'a}n, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020. +\newblock \href {https://doi.org/10.18653/v1/2020.acl-main.747} {{Unsupervised Cross-lingual Representation Learning at Scale}}. +\newblock In \emph{Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics}, pages 8440--8451, Online. Association for Computational Linguistics. + +\bibitem[{DeLucia et~al.(2022)DeLucia, Wu, Mueller, Aguirre, Resnik, and Dredze}]{bernice} +Alexandra DeLucia, Shijie Wu, Aaron Mueller, Carlos Aguirre, Philip Resnik, and Mark Dredze. 2022. +\newblock \href {https://aclanthology.org/2022.emnlp-main.415} {{Bernice: A Multilingual Pre-trained Encoder for Twitter}}. +\newblock In \emph{Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing}, pages 6191--6205, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics. + +\bibitem[{Devlin et~al.(2019)Devlin, Chang, Lee, and Toutanova}]{bert} +Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. +\newblock \href {https://aclanthology.org/N19-1423/} {{BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding}}. +\newblock In \emph{Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)}, pages 4171--4186. + +\bibitem[{Dinh et~al.(2022)Dinh, Luu, and Nguyen}]{vispamreviews} +Co~Van Dinh, Son~T. Luu, and Anh Gia-Tuan Nguyen. 2022. +\newblock \href {https://doi.org/10.1007/978-3-031-21743-2_48} {{Detecting Spam Reviews on Vietnamese E-Commerce Websites}}. +\newblock In \emph{Intelligent Information and Database Systems}, pages 595--607. Springer International Publishing. + +\bibitem[{Ho et~al.(2020)Ho, Nguyen, Nguyen, Pham, Nguyen, Nguyen, and Nguyen}]{ho2020emotion} +Vong~Anh Ho, Duong Huynh-Cong Nguyen, Danh~Hoang Nguyen, Linh Thi-Van Pham, Duc-Vu Nguyen, Kiet~Van Nguyen, and Ngan Luu-Thuy Nguyen. 2020. +\newblock \href {https://link.springer.com/chapter/10.1007/978-981-15-6168-9_27} {{Emotion Recognition for Vietnamese Social Media Text}}. +\newblock In \emph{Computational Linguistics: 16th International Conference of the Pacific Association for Computational Linguistics, PACLING 2019, Hanoi, Vietnam, October 11--13, 2019, Revised Selected Papers 16}, pages 319--333. Springer. + +\bibitem[{Hoang et~al.(2023)Hoang, Luu, Tran, Nguyen, and Nguyen}]{vihos} +Phu~Gia Hoang, Canh Luu, Khanh Tran, Kiet Nguyen, and Ngan Nguyen. 2023. +\newblock \href {https://aclanthology.org/2023.eacl-main.47/} {{ViHOS: Hate Speech Spans Detection for Vietnamese}}. +\newblock In \emph{Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics}, pages 652--669. + +\bibitem[{Hu et~al.(2022)Hu, Hosseini, Skorupa~Parolin, Osorio, Khan, Brandt, and D{'}Orazio}]{conflibert} +Yibo Hu, MohammadSaleh Hosseini, Erick Skorupa~Parolin, Javier Osorio, Latifur Khan, Patrick Brandt, and Vito D{'}Orazio. 2022. +\newblock \href {https://doi.org/10.18653/v1/2022.naacl-main.400} {{ConfliBERT: A Pre-trained Language Model for Political Conflict and Violence}}. +\newblock In \emph{Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies}, pages 5469--5482, Seattle, United States. Association for Computational Linguistics. + +\bibitem[{Koto et~al.(2021)Koto, Lau, and Baldwin}]{koto-etal-2021-indobertweet} +Fajri Koto, Jey~Han Lau, and Timothy Baldwin. 2021. +\newblock \href {https://doi.org/10.18653/v1/2021.emnlp-main.833} {{I}ndo{BERT}weet: A pretrained language model for {I}ndonesian {T}witter with effective domain-specific vocabulary initialization}. +\newblock In \emph{Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing}, pages 10660--10668, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. + +\bibitem[{Kudo and Richardson(2018)}]{sentencepiece} +Taku Kudo and John Richardson. 2018. +\newblock \href {https://doi.org/10.18653/v1/D18-2012} {{{S}entence{P}iece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing}}. +\newblock In \emph{Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations}, pages 66--71, Brussels, Belgium. Association for Computational Linguistics. + +\bibitem[{Lample and Conneau(2019)}]{xlm} +Guillaume Lample and Alexis Conneau. 2019. +\newblock \href {https://proceedings.neurips.cc/paper/2019/file/c04c19c2c2474dbf5f7ac4372c5b9af1-Paper.pdf} {{Cross-lingual Language Model Pretraining}}. +\newblock \emph{Advances in Neural Information Processing Systems (NeurIPS)}. + +\bibitem[{Le-Hong(2021)}]{diacritics} +Phuong Le-Hong. 2021. +\newblock \href {https://doi.org/https://doi.org/10.1016/j.knosys.2021.107504} {{Diacritics generation and application in hate speech detection on Vietnamese social networks}}. +\newblock \emph{Knowledge-Based Systems}, 233:107504. + +\bibitem[{Lee et~al.(2019)Lee, Yoon, Kim, Kim, Kim, So, and Kang}]{biobert} +Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan~Ho So, and Jaewoo Kang. 2019. +\newblock \href {https://doi.org/10.1093/bioinformatics/btz682} {{BioBERT: a pre-trained biomedical language representation model for biomedical text mining}}. +\newblock \emph{Bioinformatics}, 36(4):1234--1240. + +\bibitem[{Liu et~al.(2019)Liu, Ott, Goyal, Du, Joshi, Chen, Levy, Lewis, Zettlemoyer, and Stoyanov}]{roberta} +Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. +\newblock \href {http://arxiv.org/abs/1907.11692} {{RoBERTa: A Robustly Optimized BERT Pretraining Approach}}. + +\bibitem[{Loshchilov and Hutter(2019)}]{AdamW} +Ilya Loshchilov and Frank Hutter. 2019. +\newblock \href {https://openreview.net/forum?id=Bkg6RiCqY7} {{Decoupled Weight Decay Regularization}}. +\newblock In \emph{{Proceedings of the International Conference on Learning Representations}}. + +\bibitem[{Luu et~al.(2021)Luu, Nguyen, and Nguyen}]{vihsd} +Son~T. 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European Language Resources Association. + +\bibitem[{Ngo(2020)}]{ngo2020vietnamese} +B.~Ngo. 2020. +\newblock \href {https://books.google.com.vn/books?id=-N1TzQEACAAJ} {\emph{Vietnamese: An Essential Grammar}}. +\newblock Essential grammar. Routledge, Taylor \& Francis Group. + +\bibitem[{Nguyen and Tuan~Nguyen(2020)}]{phobert} +Dat~Quoc Nguyen and Anh Tuan~Nguyen. 2020. +\newblock \href {https://doi.org/10.18653/v1/2020.findings-emnlp.92} {{P}ho{BERT}: Pre-trained language models for {V}ietnamese}. +\newblock In \emph{Findings of the Association for Computational Linguistics: EMNLP 2020}, pages 1037--1042, Online. Association for Computational Linguistics. + +\bibitem[{Nguyen et~al.(2020)Nguyen, Vu, and Tuan~Nguyen}]{bertweet} +Dat~Quoc Nguyen, Thanh Vu, and Anh Tuan~Nguyen. 2020. +\newblock \href {https://doi.org/10.18653/v1/2020.emnlp-demos.2} {{BERT}weet: A pre-trained language model for {E}nglish tweets}. +\newblock In \emph{Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations}, pages 9--14, Online. Association for Computational Linguistics. + +\bibitem[{Nguyen et~al.(2018)Nguyen, Nguyen, Ngo, Vu, Tran, Ngo, and Le}]{nguyen2018vlsp} +Huyen~TM Nguyen, Hung~V Nguyen, Quyen~T Ngo, Luong~X Vu, Vu~Mai Tran, Bach~X Ngo, and Cuong~A Le. 2018. +\newblock \href {https://vjs.ac.vn/index.php/jcc/article/view/13160} {{VLSP Shared Task: Sentiment Analysis}}. +\newblock \emph{Journal of Computer Science and Cybernetics}, 34(4):295--310. + +\bibitem[{Nguyen and Van~Nguyen(2020)}]{nguyen2020exploiting} +Khang Phuoc-Quy Nguyen and Kiet Van~Nguyen. 2020. +\newblock \href {https://ieeexplore.ieee.org/document/9310495} {{Exploiting Vietnamese social media characteristics for textual emotion recognition in Vietnamese}}. +\newblock In \emph{2020 International Conference on Asian Language Processing (IALP)}, pages 276--281. IEEE. + +\bibitem[{Nguyen and Nguyen(2021)}]{phonlp} +Linh~The Nguyen and Dat~Quoc Nguyen. 2021. +\newblock \href {https://aclanthology.org/2021.naacl-demos.1/} {{PhoNLP: A joint multi-task learning model for Vietnamese part-of-speech tagging, named entity recognition and dependency parsing}}. +\newblock In \emph{Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations}, pages 1--7. + +\bibitem[{Nguyen et~al.(2022)Nguyen, Nguyen, and Nguyen}]{smtce} +Luan Nguyen, Kiet Nguyen, and Ngan Nguyen. 2022. +\newblock \href {https://aclanthology.org/2022.paclic-1.31} {{SMTCE}: A social media text classification evaluation benchmark and {BERT}ology models for {V}ietnamese}. +\newblock In \emph{Proceedings of the 36th Pacific Asia Conference on Language, Information and Computation}, pages 282--291, Manila, Philippines. De La Salle University. + +\bibitem[{P{\'e}rez et~al.(2022)P{\'e}rez, Furman, Alonso~Alemany, and Luque}]{robertuito} +Juan~Manuel P{\'e}rez, Dami{\'a}n~Ariel Furman, Laura Alonso~Alemany, and Franco~M. Luque. 2022. +\newblock \href {https://aclanthology.org/2022.lrec-1.785} {{R}o{BERT}uito: a pre-trained language model for social media text in {S}panish}. +\newblock In \emph{Proceedings of the Thirteenth Language Resources and Evaluation Conference}, pages 7235--7243, Marseille, France. European Language Resources Association. + +\bibitem[{Phan et~al.(2022)Phan, Tran, Nguyen, and Trinh}]{ViT5} +Long Phan, Hieu Tran, Hieu Nguyen, and Trieu~H. Trinh. 2022. +\newblock \href {https://aclanthology.org/2022.naacl-srw.18} {{V}i{T}5: Pretrained text-to-text transformer for {V}ietnamese language generation}. +\newblock In \emph{Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop}, pages 136--142. Association for Computational Linguistics. + +\bibitem[{Quoc~Tran et~al.(2023)Quoc~Tran, Trong~Nguyen, Hoang, Luu, Do, and Van~Nguyen}]{phobertcnn} +Khanh Quoc~Tran, An~Trong~Nguyen, Phu~Gia Hoang, Canh~Duc Luu, Trong-Hop Do, and Kiet Van~Nguyen. 2023. +\newblock \href {https://link.springer.com/article/10.1007/s00521-022-07745-w} {{Vietnamese hate and offensive detection using PhoBERT-CNN and social media streaming data}}. +\newblock \emph{Neural Computing and Applications}, 35(1):573--594. + +\bibitem[{Raffel et~al.(2020)Raffel, Shazeer, Roberts, Lee, Narang, Matena, Zhou, Li, and Liu}]{raffel2020exploring} +Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter~J Liu. 2020. +\newblock \href {https://dl.acm.org/doi/abs/10.5555/3455716.3455856} {{Exploring the limits of transfer learning with a unified text-to-text transformer}}. +\newblock \emph{The Journal of Machine Learning Research}, 21(1):5485--5551. + +\bibitem[{Rasmy et~al.(2021)Rasmy, Xiang, Xie, Tao, and Zhi}]{rasmy2021med} +Laila Rasmy, Yang Xiang, Ziqian Xie, Cui Tao, and Degui Zhi. 2021. +\newblock \href {https://www.nature.com/articles/s41746-021-00455-y} {{Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction}}. +\newblock \emph{NPJ digital medicine}, 4(1):86. + +\bibitem[{Tran et~al.(2023)Tran, Pham, Nguyen, Hy, and Vu}]{viDeBerTa} +Cong~Dao Tran, Nhut~Huy Pham, Anh-Tuan Nguyen, Truong~Son Hy, and Tu~Vu. 2023. +\newblock \href {https://aclanthology.org/2023.findings-eacl.79/} {{ViDeBERTa: A powerful pre-trained language model for Vietnamese}}. +\newblock In \emph{Findings of the Association for Computational Linguistics: EACL 2023}, pages 1041--1048. + +\bibitem[{Tran et~al.(2022)Tran, Le, and Nguyen}]{BARTpho} +Nguyen~Luong Tran, Duong~Minh Le, and Dat~Quoc Nguyen. 2022. +\newblock \href {https://arxiv.org/abs/2109.09701} {{BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese}}. +\newblock In \emph{Proceedings of the 23rd Annual Conference of the International Speech Communication Association}. + +\bibitem[{Tran et~al.(2020)Tran, Le~Hong et~al.}]{viBERTandvELECTRA} +Thi~Oanh Tran, Phuong Le~Hong, et~al. 2020. +\newblock \href {https://aclanthology.org/2020.paclic-1.2/} {{Improving sequence tagging for Vietnamese text using transformer-based neural models}}. +\newblock In \emph{Proceedings of the 34th Pacific Asia conference on language, information and computation}, pages 13--20. + +\bibitem[{Vaswani et~al.(2017)Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser, and Polosukhin}]{attentionisallyouneed} +Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan~N Gomez, {\L}ukasz Kaiser, and Illia Polosukhin. 2017. +\newblock \href {https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf} {{Attention is all you need}}. +\newblock \emph{Advances in neural information processing systems}, 30. + +\bibitem[{Wettig et~al.(2023)Wettig, Gao, Zhong, and Chen}]{shouldyoumask} +Alexander Wettig, Tianyu Gao, Zexuan Zhong, and Danqi Chen. 2023. +\newblock \href {https://aclanthology.org/2023.eacl-main.217} {{Should You Mask 15{\%} in Masked Language Modeling?}} +\newblock In \emph{Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics}, pages 2985--3000, Dubrovnik, Croatia. Association for Computational Linguistics. + +\bibitem[{Zhang et~al.(2022)Zhang, Malkov, Florez, Park, McWilliams, Han, and El-Kishky}]{twhinbert} +Xinyang Zhang, Yury Malkov, Omar Florez, Serim Park, Brian McWilliams, Jiawei Han, and Ahmed El-Kishky. 2022. +\newblock \href {https://arxiv.org/abs/2209.07562} {{TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations}}. +\newblock \emph{arXiv preprint arXiv:2209.07562}. + +\bibitem[{Ángel González et~al.(2021)Ángel González, Hurtado, and Pla}]{twilbert} +José Ángel González, Lluís-F. Hurtado, and Ferran Pla. 2021. +\newblock \href {https://doi.org/https://doi.org/10.1016/j.neucom.2020.09.078} {{TWilBert: Pre-trained deep bidirectional transformers for Spanish Twitter}}. +\newblock \emph{Neurocomputing}, 426:58--69. + +\end{thebibliography} diff --git a/references/2023.arxiv.nguyen/source/emnlp2023.sty b/references/2023.arxiv.nguyen/source/emnlp2023.sty new file mode 100644 index 0000000000000000000000000000000000000000..7b0cc141e34275b660cbd44f97770a4ceb4554a6 --- /dev/null +++ b/references/2023.arxiv.nguyen/source/emnlp2023.sty @@ -0,0 +1,313 @@ +% This is the LaTex style file for *ACL. +% The official sources can be found at +% +% https://github.com/acl-org/ACLPUB/ +% +% This package is activated by adding +% +% \usepackage{acl} +% +% to your LaTeX file. When submitting your paper for review, add the "review" option: +% +% \usepackage[review]{acl} + +\newif\ifacl@finalcopy +\DeclareOption{final}{\acl@finalcopytrue} +\DeclareOption{review}{\acl@finalcopyfalse} +\ExecuteOptions{final} % final copy is the default + +% include hyperref, unless user specifies nohyperref option like this: +% \usepackage[nohyperref]{acl} +\newif\ifacl@hyperref +\DeclareOption{hyperref}{\acl@hyperreftrue} +\DeclareOption{nohyperref}{\acl@hyperreffalse} +\ExecuteOptions{hyperref} % default is to use hyperref +\ProcessOptions\relax + +\typeout{Conference Style for EMNLP 2023} + +\usepackage{xcolor} + +\ifacl@hyperref + \PassOptionsToPackage{breaklinks}{hyperref} + \RequirePackage{hyperref} + % make links dark blue + \definecolor{darkblue}{rgb}{0, 0, 0.5} + \hypersetup{colorlinks=true, citecolor=darkblue, linkcolor=darkblue, urlcolor=darkblue} +\else + % This definition is used if the hyperref package is not loaded. + % It provides a backup, no-op definiton of \href. + % This is necessary because \href command is used in the acl_natbib.bst file. + \def\href#1#2{{#2}} + \usepackage{url} +\fi + +\ifacl@finalcopy + % Hack to ignore these commands, which review mode puts into the .aux file. + \newcommand{\@LN@col}[1]{} + \newcommand{\@LN}[2]{} +\else + % Add draft line numbering via the lineno package + % https://texblog.org/2012/02/08/adding-line-numbers-to-documents/ + \usepackage[switch,mathlines]{lineno} + + % Line numbers in gray Helvetica 8pt + \font\aclhv = phvb at 8pt + \renewcommand\linenumberfont{\aclhv\color{lightgray}} + + % Zero-fill line numbers + % NUMBER with left flushed zeros \fillzeros[] + \newcount\cv@tmpc@ \newcount\cv@tmpc + \def\fillzeros[#1]#2{\cv@tmpc@=#2\relax\ifnum\cv@tmpc@<0\cv@tmpc@=-\cv@tmpc@\fi + \cv@tmpc=1 % + \loop\ifnum\cv@tmpc@<10 \else \divide\cv@tmpc@ by 10 \advance\cv@tmpc by 1 \fi + \ifnum\cv@tmpc@=10\relax\cv@tmpc@=11\relax\fi \ifnum\cv@tmpc@>10 \repeat + \ifnum#2<0\advance\cv@tmpc1\relax-\fi + \loop\ifnum\cv@tmpc<#1\relax0\advance\cv@tmpc1\relax\fi \ifnum\cv@tmpc<#1 \repeat + \cv@tmpc@=#2\relax\ifnum\cv@tmpc@<0\cv@tmpc@=-\cv@tmpc@\fi \relax\the\cv@tmpc@}% + \renewcommand\thelinenumber{\fillzeros[3]{\arabic{linenumber}}} + \linenumbers + + \setlength{\linenumbersep}{1.6cm} + + % Bug: An equation with $$ ... $$ isn't numbered, nor is the previous line. + + % Patch amsmath commands so that the previous line and the equation itself + % are numbered. Bug: multline has an extra line number. + % https://tex.stackexchange.com/questions/461186/how-to-use-lineno-with-amsmath-align + \usepackage{etoolbox} %% <- for \pretocmd, \apptocmd and \patchcmd + + \newcommand*\linenomathpatch[1]{% + \expandafter\pretocmd\csname #1\endcsname {\linenomath}{}{}% + \expandafter\pretocmd\csname #1*\endcsname {\linenomath}{}{}% + \expandafter\apptocmd\csname end#1\endcsname {\endlinenomath}{}{}% + \expandafter\apptocmd\csname end#1*\endcsname {\endlinenomath}{}{}% + } + \newcommand*\linenomathpatchAMS[1]{% + \expandafter\pretocmd\csname #1\endcsname {\linenomathAMS}{}{}% + \expandafter\pretocmd\csname #1*\endcsname {\linenomathAMS}{}{}% + \expandafter\apptocmd\csname end#1\endcsname {\endlinenomath}{}{}% + \expandafter\apptocmd\csname end#1*\endcsname {\endlinenomath}{}{}% + } + + %% Definition of \linenomathAMS depends on whether the mathlines option is provided + \expandafter\ifx\linenomath\linenomathWithnumbers + \let\linenomathAMS\linenomathWithnumbers + %% The following line gets rid of an extra line numbers at the bottom: + \patchcmd\linenomathAMS{\advance\postdisplaypenalty\linenopenalty}{}{}{} + \else + \let\linenomathAMS\linenomathNonumbers + \fi + + \AtBeginDocument{% + \linenomathpatch{equation}% + \linenomathpatchAMS{gather}% + \linenomathpatchAMS{multline}% + \linenomathpatchAMS{align}% + \linenomathpatchAMS{alignat}% + \linenomathpatchAMS{flalign}% + } +\fi + +\iffalse +\PassOptionsToPackage{ + a4paper, + top=2.21573cm,left=2.54cm, + textheight=24.7cm,textwidth=16.0cm, + headheight=0.17573cm,headsep=0cm +}{geometry} +\fi +\PassOptionsToPackage{a4paper,margin=2.5cm}{geometry} +\RequirePackage{geometry} + +\setlength\columnsep{0.6cm} +\newlength\titlebox +\setlength\titlebox{5cm} + +\flushbottom \twocolumn \sloppy + +% We're never going to need a table of contents, so just flush it to +% save space --- suggested by drstrip@sandia-2 +\def\addcontentsline#1#2#3{} + +\ifacl@finalcopy + \thispagestyle{empty} + \pagestyle{empty} +\else + \pagenumbering{arabic} +\fi + +%% Title and Authors %% + +\newcommand{\Thanks}[1]{\thanks{\ #1}} + +\newcommand\outauthor{ + \begin{tabular}[t]{c} + \ifacl@finalcopy + \bf\@author + \else + % Avoiding common accidental de-anonymization issue. --MM + \bf Anonymous EMNLP submission + \fi + \end{tabular}} + +% Mostly taken from deproc. +\def\maketitle{\par + \begingroup + \def\thefootnote{\fnsymbol{footnote}} + \def\@makefnmark{\hbox to 0pt{$^{\@thefnmark}$\hss}} + \twocolumn[\@maketitle] \@thanks + \endgroup + \setcounter{footnote}{0} + \let\maketitle\relax \let\@maketitle\relax + \gdef\@thanks{}\gdef\@author{}\gdef\@title{}\let\thanks\relax} +\def\@maketitle{\vbox to \titlebox{\hsize\textwidth + \linewidth\hsize \vskip 0.125in minus 0.125in \centering + {\Large\bf \@title \par} \vskip 0.2in plus 1fil minus 0.1in + {\def\and{\unskip\enspace{\rm and}\enspace}% + \def\And{\end{tabular}\hss \egroup \hskip 1in plus 2fil + \hbox to 0pt\bgroup\hss \begin{tabular}[t]{c}\bf}% + \def\AND{\end{tabular}\hss\egroup \hfil\hfil\egroup + \vskip 0.25in plus 1fil minus 0.125in + \hbox to \linewidth\bgroup\large \hfil\hfil + \hbox to 0pt\bgroup\hss \begin{tabular}[t]{c}\bf} + \hbox to \linewidth\bgroup\large \hfil\hfil + \hbox to 0pt\bgroup\hss + \outauthor + \hss\egroup + \hfil\hfil\egroup} + \vskip 0.3in plus 2fil minus 0.1in +}} + +% margins and font size for abstract +\renewenvironment{abstract}% + {\centerline{\large\bf Abstract}% + \begin{list}{}% + {\setlength{\rightmargin}{0.6cm}% + \setlength{\leftmargin}{0.6cm}}% + \item[]\ignorespaces% + \@setsize\normalsize{12pt}\xpt\@xpt + }% + {\unskip\end{list}} + +%\renewenvironment{abstract}{\centerline{\large\bf +% Abstract}\vspace{0.5ex}\begin{quote}}{\par\end{quote}\vskip 1ex} + +% Resizing figure and table captions - SL +% Support for interacting with the caption, subfigure, and subcaption packages - SL +\RequirePackage{caption} +\DeclareCaptionFont{10pt}{\fontsize{10pt}{12pt}\selectfont} +\captionsetup{font=10pt} + +\RequirePackage{natbib} +% for citation commands in the .tex, authors can use: +% \citep, \citet, and \citeyearpar for compatibility with natbib, or +% \cite, \newcite, and \shortcite for compatibility with older ACL .sty files +\renewcommand\cite{\citep} % to get "(Author Year)" with natbib +\newcommand\shortcite{\citeyearpar}% to get "(Year)" with natbib +\newcommand\newcite{\citet} % to get "Author (Year)" with natbib + +\newcommand{\citeposs}[1]{\citeauthor{#1}'s (\citeyear{#1})} + + +% Bibliography + +% Don't put a label in the bibliography at all. Just use the unlabeled format +% instead. +\def\thebibliography#1{\vskip\parskip% +\vskip\baselineskip% +\def\baselinestretch{1}% +\ifx\@currsize\normalsize\@normalsize\else\@currsize\fi% +\vskip-\parskip% +\vskip-\baselineskip% +\section*{References\@mkboth + {References}{References}}\list + {}{\setlength{\labelwidth}{0pt}\setlength{\leftmargin}{\parindent} + \setlength{\itemindent}{-\parindent}} + \def\newblock{\hskip .11em plus .33em minus -.07em} + \sloppy\clubpenalty4000\widowpenalty4000 + \sfcode`\.=1000\relax} +\let\endthebibliography=\endlist + + +% Allow for a bibliography of sources of attested examples +\def\thesourcebibliography#1{\vskip\parskip% +\vskip\baselineskip% +\def\baselinestretch{1}% +\ifx\@currsize\normalsize\@normalsize\else\@currsize\fi% +\vskip-\parskip% +\vskip-\baselineskip% +\section*{Sources of Attested Examples\@mkboth + {Sources of Attested Examples}{Sources of Attested Examples}}\list + {}{\setlength{\labelwidth}{0pt}\setlength{\leftmargin}{\parindent} + \setlength{\itemindent}{-\parindent}} + \def\newblock{\hskip .11em plus .33em minus -.07em} + \sloppy\clubpenalty4000\widowpenalty4000 + \sfcode`\.=1000\relax} +\let\endthesourcebibliography=\endlist + +% sections with less space +\def\section{\@startsection {section}{1}{\z@}{-2.0ex plus + -0.5ex minus -.2ex}{1.5ex plus 0.3ex minus .2ex}{\large\bf\raggedright}} +\def\subsection{\@startsection{subsection}{2}{\z@}{-1.8ex plus + -0.5ex minus -.2ex}{0.8ex plus .2ex}{\normalsize\bf\raggedright}} +%% changed by KO to - values to get the initial parindent right +\def\subsubsection{\@startsection{subsubsection}{3}{\z@}{-1.5ex plus + -0.5ex minus -.2ex}{0.5ex plus .2ex}{\normalsize\bf\raggedright}} +\def\paragraph{\@startsection{paragraph}{4}{\z@}{1.5ex plus + 0.5ex minus .2ex}{-1em}{\normalsize\bf}} +\def\subparagraph{\@startsection{subparagraph}{5}{\parindent}{1.5ex plus + 0.5ex minus .2ex}{-1em}{\normalsize\bf}} + +% Footnotes +\footnotesep 6.65pt % +\skip\footins 9pt plus 4pt minus 2pt 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+\def\Huge{\@setsize\Huge{28pt}\xxvpt\@xxvpt} diff --git a/references/2023.arxiv.nguyen/source/emnlp2023.tex b/references/2023.arxiv.nguyen/source/emnlp2023.tex new file mode 100644 index 0000000000000000000000000000000000000000..2a6ed5da6e6f4f5a9cc6c141826a4e3a2f4c6ea1 --- /dev/null +++ b/references/2023.arxiv.nguyen/source/emnlp2023.tex @@ -0,0 +1,984 @@ + % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended. +\pdfoutput=1 +% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines. + +\documentclass[11pt]{article} + +% Remove the "review" option to generate the final version. +\usepackage[final]{EMNLP2023} +% \usepackage[]{EMNLP2023} + +% Standard package includes +\usepackage{times} +\usepackage{latexsym} + +% For proper rendering and hyphenation of words containing Latin characters (including in bib files) +\usepackage[T1]{fontenc} +% For Vietnamese characters +\usepackage[T5]{fontenc} +% See https://www.latex-project.org/help/documentation/encguide.pdf for other character sets + +% This assumes your files are encoded as UTF8 +\usepackage[utf8]{inputenc} + +% This is not strictly necessary and may be commented out. +% However, it will improve the layout of the manuscript, +% and will typically save some space. +\usepackage{microtype} + +% This is also not strictly necessary and may be commented out. +% However, it will improve the aesthetics of text in +% the typewriter font. +\usepackage{array} +\usepackage{inconsolata} +\usepackage{amsmath} +\usepackage{multirow} +\usepackage{graphicx} +\usepackage{arydshln} +\usepackage{fdsymbol} +\usepackage{ifsym} +\usepackage{tablefootnote} +\usepackage{tikz} +\usepackage{wasysym} +\usepackage{booktabs} +\usepackage{algorithm} +\usepackage[noend]{algpseudocode} +\usepackage{longtable} +\usepackage{subcaption} +\usepackage{etoolbox} +% \usepackage{subfigure} +\usepackage{caption} +\usepackage{siunitx} +\sisetup{table-format=2.2,table-number-alignment=center,table-space-text-pre=\textuparrow,detect-weight=true,mode=text} +\usepackage{fdsymbol} +\newcommand{\dgr}{\raise0.65ex\hbox{\tiny\dagger}} +\newcommand{\ddgr}{\raise0.65ex\hbox{\tiny\ddagger}} +\newcommand{\dwnarr}{\hbox{\textcolor{red}{$\downarrow$}}} +\newcommand{\uparr}{\hbox{\textcolor{blue}{$\uparrow$}}} + + +\newcolumntype{j}{S[table-format=2.2,table-space-text-pre=~,table-space-text-post=\ddgr]} +\newcolumntype{z}{S[table-format=2.2, table-space-text-pre=\dwnarr,color=red]} +\newcolumntype{y}{S[table-format=2.2, table-space-text-pre=\uparr,color=blue]} +\newcolumntype{w}{S[table-format=2.2,table-space-text-pre=\dwnarr,color=black]} + + +%\usepackage{colortbl} +%\usepackage{xfrac} +%\usepackage{tcolorbox} +% \usepackage{hwemoji} +% \usepackage{fontawesome5} +% \setemojifont{Apple Color Emoji} +% \usepackage[perpage]{footmisc} +% \usepackage[symbol]{footmisc} + +\setcounter{footnote}{0} +% \captionsetup[table]{position=bottom} +% \renewcommand{\thefootnote}{\fnsymbol{footnote}} + + +% If the title and author information does not fit in the area allocated, uncomment the following +% +\setlength\titlebox{6.25cm} +% +% and set to something 5cm or larger. + +%\title{: Pre-trained Language Models for Vietnamese Social Media Texts} +\title{ViSoBERT: A Pre-Trained Language Model for Vietnamese Social Media Text Processing} + +% Author information can be set in various styles: +% For several authors from the same institution: +% \author{Author 1 \and ... \and Author n \\ +% Address line \\ ... \\ Address line} +% if the names do not fit well on one line use +% Author 1 \\ {\bf Author 2} \\ ... \\ {\bf Author n} \\ +% For authors from different institutions: +% \author{Author 1 \\ Address line \\ ... \\ Address line +% \And ... \And +% Author n \\ Address line \\ ... \\ Address line} +% To start a separate ``row'' of authors use \AND, as in +% \author{Author 1 \\ Address line \\ ... \\ Address line +% \AND +% Author 2 \\ Address line \\ ... \\ Address line \And +% Author 3 \\ Address line \\ ... \\ Address line} + + + + +\author{Quoc-Nam Nguyen\textsuperscript{1, 3, \protect\hyperlink{equally}{*}}, Thang Chau Phan\textsuperscript{1, 3, \protect\hyperlink{equally}{*}}, Duc-Vu Nguyen\textsuperscript{2, 3}, Kiet Van Nguyen\textsuperscript{1, 3} \\ +\textsuperscript{1}Faculty of Information Science and Engineering, University of Information Technology,\\Ho Chi Minh City, Vietnam\\ +\textsuperscript{2}Multimedia Communications Laboratory, University of Information Technology,\\Ho Chi Minh City, Vietnam\\ +\textsuperscript{3}Vietnam National University, Ho Chi Minh City, Vietnam \\ +\texttt{\{20520644, 20520929\}@gm.uit.edu.vn} \\ +\texttt{\{vund, kietnv\}@uit.edu.vn}} + + + +\begin{document} + +\maketitle +\def\thefootnote{*}\footnotetext{ +\raisebox{\baselineskip}[0pt][0pt]{\hypertarget{equally}{}}Equal contribution.}\def\thefootnote{\arabic{footnote}} +\setcounter{footnote}{3} + + +\begin{abstract} + +%Lát tui viết, giờ bận xem seminar +% da thay +%Bài này thế nào tụi nó cũng mời Mr. Dat Review :D nên để vào abstract cho mát dạ :D +% dạ thầy :v, bài này đấu trực teiesp với phobert luôn :v +%:D cũng nhẹ nhàng mà :D +% dạ bài này mình đánh low resource vs pre-trained là đúng cái contribution năm nay của emnlp luôn thầy :v. Chắc em dạo 1 vòng bài coi thêm được mấy cái ý liên quan đến 2 cái đóđó rồi thethêm zô thầy :v +%OK, mở rộng ra mấy cái pre-train khác nhau, xem cách nó lập luận +% dạ, bài này này còn phần feature nữa là xong bản draft á thầy +% em dang them may cai vi du vao` thay :v + + +% da thay oi, phan algorithm cua minh, minh co can phai elaborate cai algorithm ra khong thay + +English and Chinese, known as resource-rich languages, have witnessed the strong development of transformer-based language models for natural language processing tasks. Although Vietnam has approximately 100M people speaking Vietnamese, several pre-trained models, e.g., PhoBERT, ViBERT, and vELECTRA, performed well on general Vietnamese NLP tasks, including POS tagging and named entity recognition. These pre-trained language models are still limited to Vietnamese social media tasks. In this paper, we present the first monolingual pre-trained language model for Vietnamese social media texts, ViSoBERT, which is pre-trained on a large-scale corpus of high-quality and diverse Vietnamese social media texts using XLM-R architecture. Moreover, we explored our pre-trained model on five important natural language downstream tasks on Vietnamese social media texts: emotion recognition, hate speech detection, sentiment analysis, spam reviews detection, and hate speech spans detection. Our experiments demonstrate that ViSoBERT, with far fewer parameters, surpasses the previous state-of-the-art models on multiple Vietnamese social media tasks. Our ViSoBERT model is available\footnote{\url{https://huggingface.co/uitnlp/visobert}} only for research purposes. + +\textbf{Disclaimer}: This paper contains actual comments on social networks that might be construed as abusive, offensive, or obscene. + +\end{abstract} + + +% \input{emnlp2023-latex/Sections/1-Intro} +% \input{emnlp2023-latex/Sections/2-Related-Work} +% \input{emnlp2023-latex/Sections/3-ViSoBERT} +% \input{emnlp2023-latex/Sections/4-Experimental-results} +% \input{emnlp2023-latex/Sections/5-Discussion} +% \input{emnlp2023-latex/Sections/6-Conclusion} +\section{Introduction} \label{introduction} +Language models based on transformer architecture \cite{attentionisallyouneed} pre-trained on large-scale datasets have brought about a paradigm shift in natural language processing (NLP), reshaping how we analyze, understand, and generate text. In particular, BERT \cite{bert} and its variants \cite{roberta,xlm-r} have achieved state-of-the-art performance on a wide range of downstream NLP tasks, including but not limited to text classification, sentiment analysis, question answering, and machine translation. English is moving for the rapid development of language models across specific domains such as medical \cite{biobert,rasmy2021med}, scientific \cite{beltagy2019scibert}, legal \cite{legalbert}, political conflict and violence \cite{conflibert}, and especially social media \cite{bertweet,bernice,robertuito,twhinbert}. + +%By learning from vast amounts of data, pre-trained language models have acquired a deep understanding of the underlying linguistic structures and patterns, enabling them to generate coherent and meaningful text. + + +% These models are trained on massive amounts of text data and learn to represent the underlying linguistic structures and patterns of the language, enabling them to generate coherent and meaningful text. + +% dạ này để em viết cho thầy :D, em có viết mấy phần ở dưới á thầy, thầy check giúp tụi em với +%Phần introduction rất quan trọng đó, phải thể hiện rõ motivation nha +% Dạ thầy, từ giờ đến chiều chắc sẽ xong ạ + +%There are two primary reasons to experiment with Vietnamese social media texts. Nêu 1 số lý do mình thực hiện cho pre-trained social media texts for Vietnamese + +Vietnamese is the eighth largest language used over the internet, with around 85 million users across the world\footnote{\url{https://www.internetworldstats.com/stats3.htm}}. Despite a large amount of Vietnamese data available over the Internet, the advancement of NLP research in Vietnamese is still slow-moving. This can be attributed to several factors, to name a few: the scattered nature of available datasets, limited documentation, and minimal community engagement. Moreover, most existing pre-trained models for Vietnamese were primarily trained on large-scale corpora sourced from general texts \cite{viBERTandvELECTRA,phobert,viDeBerTa}. While these sources provide broad language coverage, they may not fully represent the sociolinguistic phenomena in Vietnamese social media texts. Social media texts often exhibit different linguistic patterns, informal language usage, non-standard vocabulary, lacking diacritics and emoticons that are not prevalent in formal written texts. The limitations of using language models pre-trained on general corpora become apparent when processing Vietnamese social media texts. The models can struggle to accurately understand and interpret the informal language, using emoji, teencode, and diacritics used in social media discussions. This can lead to suboptimal performance in Vietnamese social media tasks, including emotion recognition, hate speech detection, sentiment analysis, spam reviews detection, and hate speech spans detection. + +We present ViSoBERT, a pre-trained language model designed explicitly for Vietnamese social media texts to address these challenges. ViSoBERT is based on the transformer architecture and trained on a large-scale dataset of Vietnamese posts and comments extracted from well-known social media networks, including Facebook, Tiktok, and Youtube. Our model outperforms existing pre-trained models on various downstream tasks, including emotion recognition, hate speech detection, sentiment analysis, spam reviews detection, and hate speech spans detection, demonstrating its effectiveness in capturing the unique characteristics of Vietnamese social media texts. Our contributions are summarized as follows. +\begin{itemize} + \item We presented ViSoBERT, the first PLM based on the XLM-R architecture and pre-training procedure for Vietnamese social media text processing. ViSoBERT is available publicly for research purposes in Vietnamese social media mining. ViSoBERT can be a strong baseline for Vietnamese social media text processing tasks and their applications. + \item ViSoBERT produces SOTA performances on multiple Vietnamese downstream social media tasks, thus illustrating the effectiveness of our PLM on Vietnamese social media texts. + % : emotion recognition, hate speech detection, sentiment analysis, spam reviews detection, and hate speech span detection, thus illustrating the effectiveness of our model on Vietnamese social media texts. + \item To understand our pre-trained language model deeply, we analyze experimental results on the masking rate, examining social media characteristics, including emojis, teencode, and diacritics, and implementing feature-based extraction for task-specific models. +\end{itemize} + +\section{Fundamental of Pre-trained Language Models for Social Media Texts} \label{fundamental} +Pre-trained Language Models (PLMs) based on transformers \cite{attentionisallyouneed} have become a crucial element in cutting-edge NLP tasks, including text classification and natural language generation. Since then, language models based on transformers related to our study have been reviewed, including PLMs for Vietnamese social media texts. +\subsection{Pre-trained Language Models for Vietnamese} +Several PLMs have recently been developed for processing Vietnamese texts. These models have varied in their architectures, training data, and evaluation metrics. PhoBERT, developed by \citet{phobert}, is the first general pre-trained language model (PLM) created for the Vietnamese language. The model employs the same architecture as BERT \cite{bert} and the same pre-training technique as RoBERTa \cite{roberta} to ensure robust and reliable performance. PhoBERT was trained on a 20GB word-level Vietnamese Wikipedia corpus, which produces SOTA performances on a range of downstream tasks of POS tagging, dependency parsing, NER, and NLI. + +Following the success of PhoBERT, viBERT \cite{viBERTandvELECTRA} and vELECTRA \cite{viBERTandvELECTRA}, both monolingual pre-trained language models based on the BERT and ELECTRA architectures, were introduced. They were trained on substantial datasets, with ViBERT using a 10GB corpus and vELECTRA utilizing an even larger 60GB collection of uncompressed Vietnamese text. viBERT4news\footnote{\url{https://github.com/bino282/bert4news}} was published by NlpHUST, a Vietnamese version of BERT trained on more than 20 GB of news datasets. For Vietnamese text summarization, BARTpho \cite{BARTpho} is presented as the first large-scale monolingual seq2seq models pre-trained for Vietnamese, based on the seq2seq denoising autoencoder BART. Moreover, ViT5 \cite{ViT5} follows the encoder-decoder architecture proposed by \citet{attentionisallyouneed} and the T5 framework proposed by \citet{raffel2020exploring}. Many language models are designed for general use, while the availability of strong baseline models for domain-specific applications remains limited. Since then, \citet{vihealthbert} introduced ViHealthBERT, the first domain-specific PLM for Vietnamese healthcare. + +\subsection{Pre-trained Language Models for Social Media Texts} + +Multiple PLMs were introduced for social media for multilingual and monolinguals. BERTweet \cite{bertweet} was presented as the first public large-scale PLM for English Tweets. BERTweet has the same architecture as BERT$_\textit{Base}$ \cite{bert} and is trained using the RoBERTa pre-training procedure \cite{roberta}. \citet{koto-etal-2021-indobertweet} proposed IndoBERTweet, the first large-scale pre-trained model for Indonesian Twitter. IndoBERTweet is trained by extending a monolingually trained Indonesian BERT model with an additive domain-specific vocabulary. RoBERTuito, presented in \citet{robertuito}, is a robust transformer model trained on 500 million Spanish tweets. RoBERTuito excels in various language contexts, including multilingual and code-switching scenarios, such as Spanish and English. TWilBert \cite{twilbert} is proposed as a specialization of BERT architecture both for the Spanish language and the Twitter domain to address text classification tasks in Spanish Twitter. + +Bernice, introduced by \citet{bernice}, is the first multilingual pre-trained encoder designed exclusively for Twitter data. This model uses a customized tokenizer trained solely on Twitter data and incorporates a larger volume of Twitter data (2.5B tweets) than most BERT-style models. \citet{twhinbert} introduced TwHIN-BERT, a multilingual language model trained on 7 billion Twitter tweets in more than 100 different languages. It is designed to handle short, noisy, user-generated text effectively. Previously, \cite{barbieri-etal-2022-xlm} extended the training of the XLM-R \cite{xlm-r} checkpoint using a data set comprising 198 million multilingual tweets. As a result, XLM-T is adapted to the Twitter domain and was not exclusively trained on data from within that domain. + +% AlBERTo \cite{alberto} + +% TweetBERT \cite{tweetbert} + +% Transformer based contextualization of pre-trained word embeddings for irony detection in Twitter \cite{GONZALEZ2020102262} + +% \subsection{Vietnamese Social Media Texts tasks} +% There has been growing interest in developing NLP models specifically for processing Vietnamese social media texts in recent years. + +% % Constructive Speech Detection Task (UIT-ViCTSD) \cite{victsd} + +% Emotion Recognition Task (UIT-VSMEC) \cite{vsmec} + +% Hate Speech Detection Task (UIT-ViHSD) \cite{vihsd} + +% Complaint Comment Detection Task (UIT-ViOCD) \cite{viocd} + +% Spam Reviews Detection Task (ViSpamReviews) \cite{vispamreviews} + +% Hate Speech Spans Detection Task (UIT-ViHOS) \cite{vihos} + +\section{ViSoBERT} \label{ViSoBERT} +This section presents the architecture, pre-training data, and our custom tokenizer on Vietnamese social media texts for ViSoBERT. + +\subsection{Pre-training Data} +% This work uses a large corpus of 01 GB of uncompressed texts as a pre-training dataset. +% Updates to Google’s advertising resources indicate that YouTube had 63.00 million users in Vietnam in early 2023. Figures published in ByteDance’s advertising resources indicate that in Vietnam, TikTok had 49.86 million users aged 18 and above in early 2023. Data published in Meta’s advertising resources indicate that ads on Facebook Messenger reached 52.65 million users in Vietnam in early 2023. + +We crawled textual data from Vietnamese public social networks such as Facebook\footnote{\url{https://www.facebook.com/}}, Tiktok\footnote{\url{https://www.tiktok.com/}}, and YouTube\footnote{\url{https://www.youtube.com/}} which are the three most well known social networks in Vietnam, with 52.65, 49.86, and 63.00 million users\footnote{\url{https://datareportal.com/reports/digital-2023-vietnam}}, respectively, in early 2023. + +To effectively gather data from these platforms, we harnessed the capabilities of specialized tools provided by each platform. + +\begin{enumerate} + \item \textbf{Facebook}: We crawled comments from Vietnamese-verified pages by Facebook posts via the Facebook Graph API\footnote{\url{https://developers.facebook.com/}} between January 2016 and December 2022. + \item \textbf{TikTok}: We collected comments from Vietnamese-verified channels by TikTok through TikTok Research API\footnote{\url{https://developers.tiktok.com/products/research-api/}} between January 2020 and December 2022. + \item \textbf{YouTube}: We scrapped comments from Vietnam-verified channels' videos by YouTube via YouTube Data API\footnote{\url{https://developers.google.com/youtube/v3}} between January 2016 and December 2022. +\end{enumerate} + +% We crawled Vietnamese social media textual data using the official Facebook\footnote{\url{https://developers.facebook.com/}}, Tiktok\footnote{\url{https://developers.tiktok.com/products/research-api/}}, and YouTube\footnote{\url{https://developers.google.com/youtube/v3}}. + +%Therefore, we crawled Vietnamese social media textual data using the official Facebook\footnote{\url{https://developers.facebook.com/}}, Tiktok\footnote{\url{https://developers.tiktok.com/products/research-api/}}, and YouTube\footnote{\url{https://developers.google.com/youtube/v3}} API on these to capture all Vietnamese social media contexts as much as possible. + +\textbf{Pre-processing Data:} \label{preprocessing} +Pre-processing is vital for models consuming social media data, which is massively noisy, and has user handles (@username), hashtags, emojis, misspellings, hyperlinks, and other noncanonical texts. We perform the following steps to clean the dataset: removing noncanonical texts, removing comments including links, removing excessively repeated spam and meaningless comments, removing comments including only user handles (@username), and keeping emojis in training data. + +As a result, our pretraining data after crawling and preprocessing contains 1GB of uncompressed text. Our pretraining data is available only for research purposes. + + + +% xoá bỏ các ký tự lạ (noncanonical text) +% Spam: +% xoá bỏ các câu có link +% xoá bỏ các câu có chữ "vay vốn" +% xoá bỏ các câu có chữ "để lại sđt" +% "phật" +% "boom" +% "tri ân" + +% giữ các emoji +% xoá bỏ các câu có tag tên + +\subsection{Model Architecture} \label{architecture} + +Transformers \cite{attentionisallyouneed} have significantly advanced NLP research using trained models in recent years. Although language models \cite{phobert,phonlp} have also proven effective on a range of Vietnamese NLP tasks, their results on Vietnamese social media tasks \cite{smtce} need to be significantly improved. To address this issue, taking into account successful hyperparameters from XLM-R \cite{xlm-r}, we proposed ViSoBERT, a transformer-based model in the style of XLM-R architecture with 768 hidden units, 12 self-attention layers, and 12 attention heads, and used a masked language objective (the same as \citet{xlm-r}). + +\subsection{The Vietnamese Social Media Tokenizer} \label{tokenizer} +To the best of our knowledge, ViSoBERT is the first PLM with a custom tokenizer for Vietnamese social media texts. Bernice \cite{bernice} was the first multilingual model trained from scratch on Twitter\footnote{\url{https://twitter.com/}} data with a custom tokenizer; however, Bernice's tokenizer doesn't handle Vietnamese social media text effectively. Moreover, existing Vietnamese pre-trained models' tokenizers perform poorly on social media text because of different domain data training. Therefore, we developed the first custom tokenizer for Vietnamese social media texts. + +Owing to the ability to handle raw texts of SentencePiece \cite{sentencepiece} without any loss compared to Byte-Pair Encoding \cite{xlm-r}, we built a custom tokenizer on Vietnamese social media by SentencePiece on the whole training dataset. +A model has better coverage of data than another when \textit{fewer} subwords are needed to represent the text, and the subwords are \textit{longer} \cite{bernice}. Figure~\ref{fig:tokenlen} (in Appendix~\ref{app:socialtok}) displays the mean token length for each considered model and group of tasks. ViSoBERT achieves the shortest representations for all Vietnamese social media downstream tasks compared to other PLMs. + +Emojis and teencode are essential to the “language” on Vietnamese social media platforms. Our custom tokenizer's capability to decode emojis and teencode ensure that their semantic meaning and contextual significance are accurately captured and incorporated into the language representation, thus enhancing the overall quality and comprehensiveness of text analysis and understanding. + +% Table~\ref{tab:tokenizer} (in Appendix \ref{app:socialtok}) shows several examples of our tokenizer compared to the previous Vietnamese state-of-the-art PLM PhoBERT on Vietnamese social media text. + +To assess the tokenized ability of Vietnamese social media textual data, we conducted an analysis of several data samples. Table \ref{tab:tokenizer} shows several actual social comments and their tokenizations with the tokenizers of the two pre-trained language models, ViSoBERT and PhoBERT, the best strong baseline. The results show that our custom tokenizer performed better compared to others. + +\begin{table*}[!ht] +\centering +\resizebox{\textwidth}{!}{ +\begin{tabular}{l|l|l} +\hline +\textbf{Comments} & \multicolumn{1}{c|}{\textbf{ViSoBERT}} & \multicolumn{1}{c}{\textbf{PhoBERT}} \\ +\hline +\begin{tabular}[c]{@{}l@{}}concặc cáilồn gìđây\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\\\textit{English}: Wut is dis fuckingd1ck \includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\end{tabular} & \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{},~"conc", "ặc", "cái", "l", "ồn", "gì", "đây", \\"\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}", \textless{}/s\textgreater{}\end{tabular} & \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "c o n @ @", "c @ @", "ặ c", "c á @ @", \\"i l @ @", "ồ n", "g @ @","ì @ @", "đ â y", \\ \textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \textless{}/s\textgreater{}\end{tabular} \\ +\hline +\begin{tabular}[c]{@{}l@{}}e cảmơn anh\includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}\includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}\\\textit{English}: Thankyou \includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}\includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}\end{tabular} & \textless{}s\textgreater{}, "e", "cảm", "ơn", "anh", "\includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}", "\includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}", \textless{}/s\textgreater{} & \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{},~"e", "c ả @ @", "m @ @", "ơ n", "a n h", \\, , \textless{}/s\textgreater{} \end{tabular}\\ +\hline +\begin{tabular}[c]{@{}l@{}}d4y l4 vj du cko mot cau teencode\\\textit{English}: Th1s 1s 4 teencode s3nt3nc3\end{tabular} & \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "d", "4", "y", "l", "4", "vj", "du", "cko", "mot", \\"cau", "teen", "code", \textless{}/s\textgreater{}\end{tabular} & \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "d @ @", "4 @ @", "y", "l @ @", "4", \\"v @ @", "j", "d u", "c k @ @", "o", "m o @ @", \\"t", "c a u"; "t e @ @", "e n @ @", "c o d e", \textless{}/s\textgreater{}\end{tabular} \\ +\hline +\end{tabular}} +\caption{Actual social comments and their tokenizations with the tokenizers of the two pre-trained language models, ViSoBERT and PhoBERT.} +\label{tab:tokenizer} +\end{table*} + +% \begin{table*} +% \centering +% \caption{Actual social comments and their tokenizations with the tokenizers of the two pre-trained models, ViSoBERT and PhoBERT.} +% \label{tab:tokenizer} +% \resizebox{\textwidth}{!}{ +% \begin{tabular}{l|l|l} +% \hline +% \textbf{Comments} & \multicolumn{1}{c|}{\textbf{ViSoBERT}} & \multicolumn{1}{c}{\textbf{PhoBERT}} \\ +% \hline +% \begin{tabular}[c]{@{}l@{}}concặc cáilồn gìđây\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\\\textit{English}: Wut is dis fuckingd1ck \includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\end{tabular} & \begin{tabular}[c]{@{}l@{}}{[}\textless{}s\textgreater{},~"con", "c", "ặc", "cái", "l", "ồn", "gì", "đ", "ây", \\"\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}", "\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}", "\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}", \textless{}/s\textgreater{}]\end{tabular} & \begin{tabular}[c]{@{}l@{}}{[}\textless{}s\textgreater{}, "c o n @ @", "c @ @", "ặ c", "c á @ @", "i l @ @", "ồ n", \\"g @ @","ì @ @", "đ â y", \textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \textless{}/s\textgreater{}]\end{tabular} \\ +% \hline +% \begin{tabular}[c]{@{}l@{}}e cảmơn anh\includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}\includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}\\\textit{English}: Thankyou \includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}\includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}\end{tabular} & {[}\textless{}s\textgreater{}, "e", "cảm", "ơn", "anh", "\includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}", "\includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}", \textless{}/s\textgreater{}] & {[}\textless{}s\textgreater{},~"e", "c ả @ @", "m @ @", "ơ n", "a n h", , , \textless{}/s\textgreater{}] \\ +% \hline +% \begin{tabular}[c]{@{}l@{}}d4y l4 vj du cko mot cau teencode\\\textit{English}: Th1s 1s 4 teencode s3nt3nc3\end{tabular} & \begin{tabular}[c]{@{}l@{}}{[}\textless{}s\textgreater{}, "d4y", "l4", "v", "j", "du", "cko", "mot", "c", \\"au", "te", "en", "co", "de", \textless{}/s\textgreater{}]\end{tabular} & \begin{tabular}[c]{@{}l@{}}{[}\textless{}s\textgreater{}, "d @ @", "4 @ @", "y", "l @ @", "4", "v @ @", \\"j", "d u", "c k @ @", "o", "m o @ @", "t", "c a u"; "t e @ @", \\"e n @ @", "c o d e", \textless{}/s\textgreater{}]\end{tabular} \\ +% \hline +% \end{tabular}} +% \end{table*} + +\section{Experiments and Results} +\subsection{Experimental Settings} +\label{setup} +We accumulate gradients over one step to simulate a batch size of 128. When pretraining from scratch, we train the model for 1.2M steps in 12 epochs. We trained our model for about three days on 2$\times$RTX4090 GPUs (24GB). Each sentence is tokenized and masked dynamically with a probability equal to 30\% (which is extensively experimented on Section~\ref{impactofmasking} to explore the optimal value). Further details on hyperparameters and training can be found in Table~\ref{tab:hyperparameters} of Appendix \ref{app:exp}. + +%\begin{table}[!ht] +%\centering +%\caption{ All hyperparameters established for training ViSoBERT.} +%\label{tab:hyperparameters} +%\resizebox{\columnwidth}{!}{ +%\begin{tabular}{llr} +%\hline +%\multirow{7}{*}{Optimizer} & Algorithm & Adam \\ +% & Learning rate & 5e-5 \\ +% & Epsilon & 1e-8 \\ +% & LR scheduler & linear decay and warmup \\ +% & Warmup steps & 1000 \\ +% & Betas & 0.9 and 0.99 \\ +% & Weight decay & 0.01 \\ +%\hline +%\multirow{3}{*}{Batch} & Sequence length & 128 \\ +% & Batch size & 128 \\ +% & Vocab size & 15002 \\ +%\hline +%\multirow{2}{*}{Misc} & Dropout & 0.1 \\ +% & Attention dropout & 0.1 \\ +%\hline +%\end{tabular}} +%\end{table} + +\begin{table*}[!ht] +\centering +\resizebox{\textwidth}{!}{% +\begin{tabular}{l|ccc|l|c|c} +\hline +\textbf{Dataset} & \textbf{Train} & \textbf{Dev} & \textbf{Test} & \multicolumn{1}{c|}{\textbf{Task}} & \textbf{Evaluation Metrics} & \textbf{Classes} \\ +\hline +UIT-VSMEC & 5,548 & 686 & 693 & Emotion Recognition (ER) & \multirow{5}{*}{Acc, WF1, MF1 (\%)} & 7 \\ +UIT-HSD & 24,048 & 2,672 & 6,680 & Hate Speech Detection (HSD) & & 3 \\ +SA-VLSP2016 & 5,100 & - & 1,050 & Sentiment Analysis (SA) & & 3 \\ +ViSpamReviews & 14,306 & 1,590 & 3,974 & Spam Reviews Detection (SRD) & & 4 \\ +ViHOS & 8,844 & 1,106 & 1,106 & Hate Speech Spans Detection (HSSD) & & 3 \\ +\hline +\end{tabular}} +\caption{Statistics and descriptions of Vietnamese social media processing tasks. Acc, WF1, and MF1 denoted Accuracy, weighted F1-score, and macro F1-score metrics, respectively.} +\label{tab:datasets} +\end{table*} +% According to previous works \cite{smtce, phobertcnn}, these downstream tasks are still challenging because of the limitation of available models or data pre-processing techniques. +\textbf{Downstream tasks.} To evaluate ViSoBERT, we used five Vietnamese social media datasets available for research purposes, as summarized in Table \ref{tab:datasets}. The downstream tasks include emotion recognition (UIT-VSMEC) \cite{ho2020emotion}, hate speech detection (UIT-ViHSD) \cite{vihsd}, sentiment analysis (SA-VLSP2016) \cite{nguyen2018vlsp}, spam reviews detection (ViSpamReviews) \cite{vispamreviews}, and hate speech spans detection (UIT-ViHOS) \cite{vihos}. + +% We empirically fine-tuned all pre-trained language models using \textit{simpletransformers}\footnote{\url{https://simpletransformers.ai/} (ver 0.63.11)}. We followed fairly standard practices for fine-tuning, most of which are described in \cite{bert}. + +\textbf{Fine-tuning.} We conducted empirical fine-tuning for all pre-trained language models using the \textit{simpletransformers}\footnote{\url{https://simpletransformers.ai/} (ver 0.63.11)}. Our fine-tuning process followed standard procedures, most of which are outlined in \cite{bert}. For all tasks mentioned above, we use a batch size of 40, a maximum token length of 128, a learning rate of 2e-5, and AdamW optimizer \cite{AdamW} with an epsilon of 1e-8. We executed a 10-epoch training process and evaluated downstream tasks using the best-performing model from those epochs. Furthermore, none of the pre-processing techniques is applied in all datasets to evaluate our PLM's ability to handle raw texts. + +\textbf{Baseline models.} To establish the main baseline models, we utilized several well-known PLMs, including monolingual and multilingual, to support Vietnamese NLP social media tasks. The details of each model are shown in Table \ref{tab:pre-trained}. + +\begin{itemize} + \item \textbf{Monolingual language models}: viBERT \citep{viBERTandvELECTRA} and vELECTRA \citep{viBERTandvELECTRA} are PLMs for Vietnamese based on BERT and ELECTRA architecture, respectively. PhoBERT, which is based on BERT architecture and RoBERTa pre-training procedure, \citep{phobert} is the first large-scale monolingual language model pre-trained for Vietnamese; PhoBERT obtains state-of-the-art performances on a range of Vietnamese NLP tasks. + + \item \textbf{Multilingual language models}: Additionally, we incorporated two multilingual PLMs, mBERT \citep{bert} and XLM-R \citep{xlm-r}, which were previously shown to have competitive performances to monolingual Vietnamese models. XLM-R, a cross-lingual PLM introduced by \citet{xlm-r}, has been trained in 100 languages, among them Vietnamese, utilizing a vast 2.5TB Clean CommonCrawl dataset. XLM-R presents notable improvements in various downstream tasks, surpassing the performance of previously released multilingual models such as mBERT \cite{bert} and XLM \cite{xlm}. + + \item \textbf{Multilingual social media language models:} To ensure a fair comparison with our PLM, we integrated multiple multilingual social media PLMs, including XLM-T \cite{barbieri-etal-2022-xlm}, TwHIN-BERT \cite{twhinbert}, and Bernice \cite{bernice}. + +\end{itemize} + +% For PhoBERT, XLM-R, and TwHIN-BERT, we implemented two versions \textit{Base} and \textit{Large} for all tasks, although it is not a fair comparison due to their significantly larger model configurations. + +\begin{table*}[!ht] +\centering +\resizebox{\textwidth}{!}{ +\begin{tabular}{l|ccccccccc} +\hline +\textbf{Model} & \textbf{\#Layers} & \textbf{\#Heads} & \textbf{\#Steps} & \textbf{\#Batch} & \textbf{Domain Data} & \textbf{\#Params} & \textbf{\#Vocab} & \textbf{\#MSL} & \multicolumn{1}{l}{\textbf{CSMT}} \\ +\hline +viBERT \cite{viBERTandvELECTRA} & 12 & 12 & - & 16 & Vietnamese News & - & 30K & 256 & \textcolor{red}{No} \\ +vELECTRA \cite{viBERTandvELECTRA} & 12 & 3 & - & 16 & NewsCorpus + OscarCorpus & - & 30K & 256 & \textcolor{red}{No} \\ +PhoBERT$_\text{Base}$ \cite{phobert} & 12 & 12 & 540K & 1024 & ViWiki + ViNews & 135M & 64K & 256 & \textcolor{red}{No} \\ +PhoBERT$_\text{Large}$ \cite{phobert} & 24 & 16 & 1.08M & 512 & ViWiki + ViNews & 370M & 64K & 256 & \textcolor{red}{No} \\ +\hline +mBERT \cite{bert} & 12 & 12 & 1M & 256 & BookCorpus + EnWiki & 110M & 30K & 512 & \textcolor{red}{No} \\ +XLM-R$_\text{Base}$ \cite{xlm-r} & 12 & 12 & 1.5M & 8192 & CommonCrawl + Wiki & 270M & 250K & 512 & \textcolor{red}{No} \\ +XLM-R$_\text{Large}$ \cite{xlm-r} & 24 & 16 & 1.5M & 8192 & CommonCrawl + Wiki & 550M & 250K & 512 & \textcolor{red}{No} \\ +XLM-T \cite{barbieri-etal-2022-xlm} & 12 & 12 & - & 8192 & Multilingual Tweets & - & 250k & 512 & \textcolor{red}{No} \\ +TwHIN-BERT$_\text{Base}$ \cite{twhinbert} & 12 & 12 & 500K & 6K & Multilingual Tweets & 135M to 278M & 250K & 128 & \textcolor{red}{No} \\ +TwHIN-BERT$_\text{Large}$ \cite{twhinbert} & 24 & 16 & 500K & 8K & Multilingual Tweets & 550M & 250K & 128 & \textcolor{red}{No} \\ +Bernice \cite{bernice} & 12 & 12 & 405K+ & 8192 & Multilingual Tweets & 270M & 250K & 128 & \textcolor{green}{Yes} \\ +\hdashline +ViSoBERT (Ours) & 12 & 12 & 1.2M & 128 & Vietnamese social media & 97M & 15K & 512 & \textcolor{green}{Yes} \\ +\hline +\end{tabular}} +\caption{Detailed information about baselines and our PLM. \#Layers, \#Heads, \#Batch, \#Params, \#Vocab, \#MSL, and CSMT indicate the number of hidden layers, number of attention heads, batch size value, domain training data, number of total parameters, vocabulary size, max sequence length, and custom social media tokenizer, respectively.} +\label{tab:pre-trained} +\end{table*} + +\subsection{Main Results} +Table~\ref{mainresult} shows ViSoBERT's scores with the previous highest reported results on other PLMs using the same experimental setup. It is clear that our ViSoBERT produces new SOTA performance results for multiple Vietnamese downstream social media tasks without any pre-processing technique. + +\begin{table*}[!ht] +\centering +\resizebox{\textwidth}{!}{% +% \setlength{\tabcolsep}{6pt} % Default value: 6pt +% \renewcommand{\arraystretch}{1} +\begin{tabular}{l|c|ccj|ccj|ccj|>{\centering\arraybackslash}m{1.1cm}>{\centering\arraybackslash}m{1.1cm}S[table-format=2.2,table-space-text-pre=~,table-space-text-post=\ddgr,table-column-width=1.1cm]|>{\centering\arraybackslash}m{1.1cm}>{\centering\arraybackslash}m{1.8cm}S[table-format=2.2,table-space-text-pre=~,table-space-text-post=\ddgr,table-column-width=1.1cm]} +\hline +\multicolumn{1}{c|}{\multirow{2}{*}{\textbf{Model}}} & \multirow{2}{*}{\textbf{Avg}} & \multicolumn{3}{c|}{\textbf{Emotion Regconition}} & \multicolumn{3}{c|}{\textbf{Hate Speech Detection}} & \multicolumn{3}{c|}{\textbf{Sentiment Analysis}} & \multicolumn{3}{c|}{\textbf{Spam Reviews Detection}} & \multicolumn{3}{c}{\textbf{Hate Speech Spans Detection}} \\ +\cline{3-17} +\multicolumn{1}{c|}{} & & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} & \textbf{Acc} & \textbf{WF1} & \multicolumn{1}{c|}{\textbf{MF1}} & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} & \textbf{Acc} & \textbf{WF1} & \multicolumn{1}{c|}{\textbf{MF1}} & \textbf{Acc} & \textbf{WF1} & \multicolumn{1}{c}{\textbf{MF1}} \\ +\hline +viBERT & 71.57 & 61.91 & 61.98 & 59.70 & 85.34 & 85.01 & 62.07 & 74.85 & 74.73 & 74.73 & 89.93 & 89.79 & 76.80 & 90.42 & 90.45 & 84.55 \\ +vELECTRA & 72.43 & 64.79 & 64.71 & 61.95 & 86.96 & 86.37 & 63.95 & 74.95 & 74.88 & 74.88 & 89.83 & 89.68 & 76.23 & 90.59 & 90.58 & 85.12 \\ +PhoBERT$_\text{Base}$ & 72.81 & 63.49 & 63.36 & 61.41 & 87.12 & 86.81 & 65.01 & 75.72 & 75.52 & 75.52 & 89.83 & 89.75 & 76.18 & 91.32 & 91.38 & 85.92 \\ +PhoBERT$_\text{Large}$ & 73.47 & 64.71 & 64.66 & 62.55 & 87.32 & 86.98 & 65.14 & 76.52 & 76.36 & 76.22 & 90.12 & 90.03 & 76.88 & 91.44 & 91.46 & 86.56 \\ +\hline +mBERT (cased) & 68.07 & 56.27 & 56.17 & 53.48 & 83.55 & 83.99 & 60.62 & 67.14 & 67.16 & 67.16 & 89.05 & 88.89 & 74.52 & 89.88 & 89.87 & 84.57 \\ +mBERT (uncased) & 67.66 & 56.23 & 56.11 & 53.32 & 83.38 & 81.27 & 58.92 & 67.25 & 67.22 & 67.22 & 88.92 & 88.72 & 74.32 & 89.84 & 89.82 & 84.51 \\ +XLM-R$_\text{Base}$ & 72.08 & 60.92 & 61.02 & 58.67 & 86.36 & 86.08 & 63.39 & 76.38 & 76.38 & 76.38 & 90.16 & 89.96 & 76.55 & 90.74 & 90.72 & 85.42 \\ +XLM-R$_\text{Large}$ & 73.40 & 62.44 & 61.37 & 60.25 & 87.15 & 86.86 & 65.13 & \textbf{78.28} & \textbf{78.21} & \textbf{78.21} & 90.36 & 90.31 & 76.75 & 91.52 & 91.50 & 86.66 \\ +XLM-T & 72.23 & 64.64 & 64.37 & \multicolumn{1}{c|}{59.86} & 86.22 & 86.12 & 63.48 & 75.66 & 75.60 & \multicolumn{1}{c|}{75.60} & 90.07 & 90.11 & 76.66 & 90.88 & 90.88 & 85.53 \\ +TwHIN-BERT$_\text{Base}$ & 71.60 & 61.49 & 60.88 & 57.97 & 86.63 & 86.23 & 63.67 & 73.76 & 73.72 & 73.72 & 90.25 & 90.35 & 76.98 & 90.99 & 90.90 & 85.67 \\ +TwHIN-BERT$_\text{Large}$ & 73.42 & 64.21 & 64.29 & 61.12 & 87.23 & 86.78 & 65.23 & 76.92 & 76.83 & 76.83 & 90.47 & 90.42 & 77.28 & 91.45 & 91.47 & 86.65 \\ +Bernice & 72.49 & 64.21 & 64.27 & \multicolumn{1}{c|}{60.68} & 86.12 & 86.48 & 64.32 & 74.57 & 74.90 & \multicolumn{1}{c|}{74.90} & 90.22 & 90.21 & 76.89 & 90.48 & 90.06 & 85.67 \\ +\hdashline +ViSoBERT & \textbf{75.65} & \textbf{68.10} & \textbf{68.37} & \bfseries 65.88\ddgr & \textbf{88.51} & \textbf{88.31} & \bfseries 68.77\ddgr & 77.83 & 77.75 & 77.75 & \textbf{90.99} & \textbf{90.92} & \bfseries 79.06\ddgr & \textbf{91.62} & \textbf{91.57} & \textbf{86.80} \\ +\hline +\end{tabular}} +\caption{Performances on downstream Vietnamese social media tasks of previous state-of-the-art monolingual and multilingual PLMs without pre-processing techniques. Avg denoted the average MF1 score of each PLM. \ddgr~denotes that the highest result is statistically significant at $p < \text{0.01}$ compared to the second best, using a paired t-test.} +\label{mainresult} +\end{table*} + +\textbf{Emotion Recognition Task}: PhoBERT and TwHIN-BERT archive the previous SOTA performances on monolingual and multilingual models, respectively. ViSoBERT obtains 68.10\%, 68.37\%, and 65.88\% of Acc, WF1, and MF1, respectively, significantly higher than these PhoBERT and TwHIN-BERT models. + +\textbf{Hate Speech Detection Task}: ViSoBERT achieves significant improvements over previous state-of-the-art models, PhoBERT and TwHIN-BERT, with scores of 88.51\%, 88.31\%, and 68.77\% in Acc, WF1, and MF1, respectively. Notably, these achievements are made despite the presence of bias within the dataset\footnote{UIT-HSD is massively imbalanced, included 19,886; 1,606; and 2,556 of CLEAN, OFFENSIVE, and HATE class.}. + +\textbf{Sentiment Analysis Task}: XLM-R archived SOTA performance on three evaluation metrics. However, there is no significant increase in performance on this downstream task, for 0.45\%, 0.46\%, and 0.46\% higher on Acc, WF1, and MF1 compared to our pre-trained language model, PhoBERT$_\text{Large}$. +The SA-VLSP2016 dataset domain is technical article reviews, including TinhTe\footnote{\url{https://tinhte.vn/}} and VnExpress\footnote{\url{https://vnexpress.net/}}, which are often used as Vietnamese standard data. The reviews or comments in these newspapers are in proper form. While most of the dataset is sourced from articles, it also includes data from Facebook\footnote{\url{https://www.facebook.com/}}, a Vietnamese social media platform that accounts for only 12.21\% of the dataset. Therefore, the dataset does not fully capture Vietnamese social media platforms' diverse characteristics and informal language. However, ViSoBERT still surpassed other baselines by obtaining 1.31\%/0.91\% Acc, 1.39\%/0.92\% WF1, and 1.53\%/0.92\% MF1 compared to PhoBERT/TwHIN-BERT. + +% Therefore, the SA-VLSP2016 dataset does not contain many social media characteristics. +%The SA-VLSP2016 dataset also contains data from Facebook\footnote{\url{https://www.facebook.com/}}, but the percentage of Facebook data is minor (12.21\% of the dataset). + +% Unlike general texts, social media texts frequently update and change. The SA-VLSP2016 dataset, collected in 2016, does not align with the current Vietnamese social media texts used to train ViSoBERT. There is a contextual discrepancy that can result in information leakage in comments. Furthermore, the SA-VLSP2016 dataset primarily consists of data from TheGioiDiDong\footnote{\url{https://www.thegioididong.com/}}, a Vietnamese website focused on phones, making it more domain-specific to technical topics than social media content. However, ViSoBERT outperformed PhoBERT and TwHIN-BERT by obtaining 1.31\% Acc, 1.39\% WF1, and 1.53\% MF1 compared to PhoBERT, and 0.91\%, 0.92\%, and 0.92\% compared to TwHIN-BERT. + +\textbf{Spam Reviews Detection Task}: ViSoBERT performed better than the top two baseline models, PhoBERT and TwHIN-BERT. Specifically, it achieved 0.8\%, 0.9\%, and 2.18\% higher scores in accuracy (Acc), weighted F1 (WF1), and micro F1 (MF1) compared to PhoBERT. When compared to TwHIN-BERT, ViSoBERT outperformed it with 0.52\%, 0.50\%, and 1.78\% higher scores in Acc, WF1, and MF1, respectively. + +\textbf{Hate Speech Spans Detection Task}\footnote{For the Hate Speech Spans Detection task, we evaluate the total of spans on each comment rather than spans of each word in \citet{vihos} to retain the context of each comment.}: Our pre-trained ViSoBERT boosted the results up to 91.62\%, 91.57\%, and 86.80\% on Acc, WF1, and MF1, respectively. While the difference is insignificant, ViSoBERT indicates an outstanding ability to capture Vietnamese social media information compared to other PLMs (see Section~\ref{feature-based}). + +\textbf{Multilingual social media PLMs}: The results show that ViSoBERT consistently outperforms XLM-T and Bernice in five Vietnamese social media tasks. It's worth noting that XLM-T, TwHIN-BERT, and Bernice were all exclusively trained on data from the Twitter platform. However, this approach has limitations when applied to the Vietnamese context. The training data from this source may not capture the intricate linguistic and contextual nuances prevalent in Vietnamese social media because Twitter is not widely used in Vietnam. + +\section{Result Analysis and Discussion} +In this section, we consider the improvement of our PLM more compared to powerful others, including PhoBERT and TwHIN-BERT, in terms of different aspects. Firstly, we investigate the effects of masking rate on our pre-trained model performance (see Section \ref{impactofmasking}). Additionally, we examine the influence of social media characteristics on the model's ability to process and understand the language used in these social contexts (see Section \ref{characteristics}). Lastly, we employed feature-based extraction techniques on task-specific models to verify the potential of leveraging social media textual data to enhance word representations (see Section \ref{feature-based}). + +%We delve into the impact of various factors on our Vietnamese social media PLMs. + +% \subsubsection{A Cheaper and Faster Model} +% ViSoBERT was much cheaper and faster to train than its closest competitors. +% We argue this to the efficiency of in-domain pre-training data. Despite using orders of magnitude significantly fewer data, we have enough Vietnamese social media data to learn a better model than through adaptation. +\subsection{Impact of Masking Rate on Vietnamese Social Media PLM}\label{impactofmasking} +For the first time presenting the Masked Language Model, \citet{bert} consciously utilized a random masking rate of 15\%. The authors believed masking too many tokens could lead to losing crucial contextual information required to decode them accurately. Additionally, the authors felt that masking too few tokens would harm the training process and make it less effective. However, according to \citet{shouldyoumask}, 15\% is not universally optimal for model and training data. + +We experiment with masking rates ranging from 10\% to 50\% and evaluate the model's performance on five downstream Vietnamese social media tasks. Figure~\ref{maskingrate} illustrates the results obtained from our experiments with six different masking rates. Interestingly, our pre-trained ViSoBERT achieved the highest performance when using a masking rate of 30\%. This suggests a delicate balance between the amount of contextual information retained and the efficiency of the training process, and an optimal masking rate can be found within this range. + +However, the optimal masking rate also depends on the specific task. For instance, in the hate speech detection task, we found that a masking rate of 50\% yielded the best results, surpassing other masking rate values. This implies that the optimal masking rate may vary depending on the nature and requirements of different tasks. + +Considering the overall performance across multiple tasks, we determined that a masking rate of 30\% produced the optimal balance for our pre-trained ViSoBERT model. Consequently, we adopted this masking rate for ViSoBERT, ensuring efficient and effective utilization of contextual information during training. + +\begin{figure}[ht] + \centering + % da no bi out 8 trang thay :v, em dang fix + \includegraphics[width=\columnwidth]{f1_macro_masked_rate1111-crop.pdf} + \caption{Impact of masking rate on our pre-trained ViSoBERT in terms of MF1.} + \label{maskingrate} +\end{figure} + +% \subsubsection{Vocabulary and In-domain Pretraining Data Efficiency} +% % Vocabulary +% Figure~\ref{token} shows the average length of tokens per comment of baselines and our pre-trained model. ViSoBERT has more compact representations than the 3 best baselines, including PhoBERT for monolingual, XLM-R for multilingual, and TwHIN-BERT for social media domain. ViSoBERT has a slightly higher mean length compared to +% % In-domain +% PLM4 +% \begin{figure}[ht] +% \centering +% \includegraphics[width=\columnwidth]{emnlp2023-latex/Figures/token.png} +% \caption{Distribution of the average number of tokens per task of each pre-trained.} +% \label{token} +% \end{figure} + +\subsection{Impact of Vietnamese Social Media Characteristics} \label{characteristics} +Emojis, teencode, and diacritics are essential features of social media, especially Vietnamese social media. The ability of the tokenizer to decode emojis and the ability of the model to understand the context of teencode and diacritics are crucial. Hence, to evaluate the performance of ViSoBERT on social media characteristics, comprehensive experiments were conducted among several strong PLMs: PM4ViSMT, PhoBERT, and TwHIN-BERT. + +\textbf{Impact of Emoji on PLMs:} +\begin{table*}[!ht] +\centering +\resizebox{\textwidth}{!}{ +\begin{tabular}{c|www|www|www|www|S[table-column-width=1.1cm,table-format=2.2,table-space-text-pre=\dwnarr,color=black]S[table-column-width=1.8cm,table-format=2.2,table-space-text-pre=\dwnarr,color=black]S[table-column-width=1.1cm,table-format=2.2,table-space-text-pre=\dwnarr,color=black]} +\hline +\multirow{2}{*}{\textbf{Model}} & \multicolumn{3}{c|}{\textbf{Emotion Regconition}} & \multicolumn{3}{c|}{\textbf{Hate Speech Detection}} & \multicolumn{3}{c|}{\textbf{Sentiment Analysis}} & \multicolumn{3}{c|}{\textbf{Spam Reviews Detection}} & \multicolumn{3}{c}{\textbf{Hate Speech Spans Detection}} \\ +\cline{2-16} + & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} \\ +\hline +\multicolumn{16}{c}{\textit{\textbf{Converting emojis to text}}} \\ +\hline +\multicolumn{1}{l|}{PhoBERT$_\text{Large}$} & 66.08 & 66.15 & 63.35 & 87.43 & 87.22 & 65.32 & 76.73 & 76.48 & 76.48 & 90.35 & 90.11 & 77.02 & 92.16 & 91.98 & 86.72 \\ +$\Delta$ & \multicolumn{1}{y}{\uparr 1.37} & \multicolumn{1}{y}{\uparr 1.49} & \multicolumn{1}{y|}{\uparr 0.80} & \multicolumn{1}{y}{\uparr 0.11} & \multicolumn{1}{y}{\uparr 0.24} & \multicolumn{1}{y|}{\uparr 0.18} & \multicolumn{1}{z}{\dwnarr 0.21} & \multicolumn{1}{z}{\dwnarr 0.12} & \multicolumn{1}{z|}{\dwnarr 0.12} & \multicolumn{1}{y}{\uparr 0.23} & \multicolumn{1}{y}{\uparr 0.08} & \multicolumn{1}{y|}{\uparr 0.14} & \multicolumn{1}{y}{\uparr 0.72} & \multicolumn{1}{y}{\uparr 0.52} & \multicolumn{1}{y}{\uparr 0.16} \\ +\multicolumn{1}{l|}{TwHIN-BERT$_\text{Large}$} & 64.82 & 64.42 & 61.33 & 86.03 & 85.52 & 63.52 & 75.42 & 75.95 & 75.95 & 90.55 & 90.47 & 77.32 & 92.21 & 92.01 & 86.84 \\ +$\Delta$ & \multicolumn{1}{y}{\uparr 0.61} & \multicolumn{1}{y}{\uparr 0.13} & \multicolumn{1}{y|}{\uparr 0.21} & \multicolumn{1}{z}{\dwnarr 1.20} & \multicolumn{1}{z}{\dwnarr 1.26} & \multicolumn{1}{z|}{\dwnarr 1.71} & \multicolumn{1}{z}{\dwnarr 1.50} & \multicolumn{1}{z}{\dwnarr 0.88} & \multicolumn{1}{z|}{\dwnarr 0.88} & \multicolumn{1}{y}{\uparr 0.08} & \multicolumn{1}{y}{\uparr 0.05} & \multicolumn{1}{y|}{\uparr 0.04} & \multicolumn{1}{y}{\uparr 0.76} & \multicolumn{1}{y}{\uparr 0.54} & \multicolumn{1}{y}{\uparr 0.19} \\ +\hdashline +\multicolumn{1}{l|}{ViSoBERT $[\clubsuit]$} & 67.53 & 67.93 & 65.42 & 87.82 & 87.88 & 67.25 & 76.95 & 76.85 & 76.85 & 90.22 & 90.18 & 78.25 & 92.42 & 92.11 & 87.01 \\ +$\Delta$ & \multicolumn{1}{z}{\dwnarr 0.57} & \multicolumn{1}{z}{\dwnarr 0.44} & \multicolumn{1}{z|}{\dwnarr 0.46} & \multicolumn{1}{z}{\dwnarr 0.69} & \multicolumn{1}{z}{\dwnarr 0.41} & \multicolumn{1}{z|}{\dwnarr 1.49} & \multicolumn{1}{z}{\dwnarr 0.88} & \multicolumn{1}{z}{\dwnarr 0.90} & \multicolumn{1}{z|}{\dwnarr 0.90} & \multicolumn{1}{z}{\dwnarr 0.77} & \multicolumn{1}{z}{\dwnarr 0.74} & \multicolumn{1}{z|}{\dwnarr 0.81} & \multicolumn{1}{y}{\uparr 0.80} & \multicolumn{1}{y}{\uparr 0.54} & \multicolumn{1}{y}{\uparr 0.21} \\ +\hline +\multicolumn{16}{c}{\textit{\textbf{Removing emojis}}} \\ +\hline +\multicolumn{1}{l|}{PhoBERT$_\text{Large}$} & 65.21 & 65.14 & 62.81 & 87.25 & 86.72 & 64.85 & 76.72 & 76.48 & 76.48 & 90.21 & 90.09 & 77.02 & 91.53 & 91.51 & 86.62 \\ +$\Delta$ & \multicolumn{1}{y}{\uparr 0.50} & \multicolumn{1}{y}{\uparr 0.48} & \multicolumn{1}{y|}{\uparr 0.26} & \multicolumn{1}{z}{\dwnarr 0.07} & \multicolumn{1}{z}{\dwnarr 0.26} & \multicolumn{1}{z|}{\dwnarr 0.29} & \multicolumn{1}{y}{\uparr 0.20} & \multicolumn{1}{y}{\uparr 0.12} & \multicolumn{1}{y|}{\uparr 0.12} & \multicolumn{1}{y}{\uparr 0.09} & \multicolumn{1}{y}{\uparr 0.06} & \multicolumn{1}{y|}{\uparr 0.10} & \multicolumn{1}{y}{\uparr 0.09} & \multicolumn{1}{y}{\uparr 0.05} & \multicolumn{1}{y}{\uparr 0.09} \\ +\multicolumn{1}{l|}{TwHIN-BERT$_\text{Large}$} & 62.03 & 62.14 & 59.25 & 86.98 & 86.32 & 64.22 & 75.00 & 75.11 & 75.11 & 89.83 & 89.75 & 76.85 & 91.32 & 91.33 & 86.42 \\ +$\Delta$ & \multicolumn{1}{z}{\dwnarr 2.18} & \multicolumn{1}{z}{\dwnarr 1.15} & \multicolumn{1}{z|}{\dwnarr 1.87} & \multicolumn{1}{z}{\dwnarr 0.25} & \multicolumn{1}{z}{\dwnarr 0.46} & \multicolumn{1}{z|}{\dwnarr 1.01} & \multicolumn{1}{z}{\dwnarr 1.92} & \multicolumn{1}{z}{\dwnarr 1.72} & \multicolumn{1}{z|}{\dwnarr 1.72} & \multicolumn{1}{z}{\dwnarr 0.64} & \multicolumn{1}{z}{\dwnarr 0.67} & \multicolumn{1}{z|}{\dwnarr 0.43} & \multicolumn{1}{z}{\dwnarr 0.13} & \multicolumn{1}{z}{\dwnarr 0.14} & \multicolumn{1}{z}{\dwnarr 0.23} \\ +\hdashline +\multicolumn{1}{l|}{ViSoBERT $[\vardiamondsuit]$} & 66.52 & 67.02 & 64.55 & 87.32 & 87.12 & 66.98 & 76.25 & 75.98 & 75.98 & 89.72 & 89.69 & 77.95 & 91.58 & 91.53 & 86.72 \\ +$\Delta$ & \multicolumn{1}{z}{\dwnarr 1.58} & \multicolumn{1}{z}{\dwnarr 1.35} & \multicolumn{1}{z|}{\dwnarr 1.33} & \multicolumn{1}{z}{\dwnarr 1.19} & \multicolumn{1}{z}{\dwnarr 1.19} & \multicolumn{1}{z|}{\dwnarr 1.79} & \multicolumn{1}{z}{\dwnarr 1.58} & \multicolumn{1}{z}{\dwnarr 1.77} & \multicolumn{1}{z|}{\dwnarr 1.77} & \multicolumn{1}{z}{\dwnarr 1.27} & \multicolumn{1}{z}{\dwnarr 1.23} & \multicolumn{1}{z|}{\dwnarr 1.11} & \multicolumn{1}{z}{\dwnarr 0.04} & \multicolumn{1}{z}{\dwnarr 0.04} & \multicolumn{1}{z}{\dwnarr 0.08} \\ +\hline +\multicolumn{1}{l|}{ViSoBERT $[\spadesuit]$} & \bfseries 68.10 & \bfseries 68.37 & \bfseries 65.88 & \bfseries 88.51 & \bfseries 88.31 & \bfseries 68.77 & \bfseries 77.83 & \bfseries 77.75 & \bfseries 77.75 & \bfseries 90.99 & \bfseries 90.92 & \bfseries 79.06 & \bfseries 91.62 & \bfseries 91.57 & \bfseries 86.80 \\ +\hline +\end{tabular}} +\caption{Performances of pre-trained models on downstream Vietnamese social media tasks by applying two emojis pre-processing techniques. $[\clubsuit]$, $[\vardiamondsuit]$, and $[\spadesuit]$ denoted our pre-trained language model ViSoBERT converting emoji to text, removing emojis and without applying any pre-processing techniques, respectively. $\Delta$ denoted the increase ($\uparrow$) and the decrease ($\downarrow$) in performances of the PLMs compared to their competitors without applying any pre-processing techniques.} +\label{tab:emoji} +\end{table*} +% dạ phần emojis, hbua mình nhận xét đại ý là: Emojis quan trọng cho dữ liệu social media, tuy nhiên việc không decode được Emojis của PhoBERT làm giảm hiệu xuất của mô hình (do Emojis thì PhoBERT decode là token). Do đó khi convert về text hoặc xoá luôn emojis, PhoBERT produces better performances. Ngược lại, những mô hình decode được emojis thì reduce in performances. +% Giờ chạy lại em nhìn nhìn thì thấy có một số ý để nhận xét nữa thầy. Dạ là, khi so sánh performance khi convert về text vs xoá hẳn emojis thì đa phần là PhoBERT tốt hơn khi convert (này là do convert về text thì PhoBERT hiểu được ngữ cảnh của comment tốt hơn). +We conducted two experimental procedures to comprehensively investigate the importance of emojis, including converting emojis to general text and removing emojis. + +Table~\ref{tab:emoji} shows our detailed setting and experimental results on downstream tasks and pre-trained models. +The results indicate a moderate reduction in performance across all downstream tasks when emojis are removed or converted to text in our pre-trained ViSoBERT model. Our pre-trained decreases 0.62\% Acc, 0.55\% WF1, and 0.78\% MF1 on Average for downstream tasks while converting emojis to text. In addition, an average reduction of 1.33\% Acc, 1.32\% WF1, and 1.42\% MF1 can be seen in our pre-trained model while removing all emojis in each comment. This is because when emojis are converted to text, the context of the comment is preserved, while removing all emojis results in the loss of that context. + +This trend is also observed in the TwHIN-BERT model, specifically designed for social media processing. However, TwHIN-BERT slightly improves emotion recognition and spam reviews detection tasks compared to its competitors when operating on raw texts. Nevertheless, this improvement is marginal and insignificant, as indicated by the small increments of 0.61\%, 0.13\%, and 0.21\% in Acc, WF1, and MF1 on the emotion recognition task, respectively, and 0.08\% Acc, 0.05\% WF1, and 0.04\% MF1 on spam reviews detection task. One potential reason for this phenomenon is that TwHIN-BERT and ViSoBERT are PLMs trained on emojis datasets. Consequently, these models can comprehend the contextual meaning conveyed by emojis. This finding underscores the importance of emojis in social media texts. + +In contrast, there is a general trend of improved performance across a range of downstream tasks when removing or converting emojis to text on PhoBERT, the Vietnamese SOTA pre-trained language model. PhoBERT is a PLM on a general text (Vietnamese Wikipedia) dataset containing no emojis; therefore, when PhoBERT encounters an emoji, it treats it as an unknown token (see Table~\ref{tab:tokenizer} Appendix~\ref{app:exp}). Therefore, while applying emoji pre-processing techniques, including converting emoijs to text and removing emojis, PhoBERT produces better performances compared to raw text. + +% However, in the case of the sentiment analysis and hate speech spans detection tasks, PhoBERT exhibits a slight reduction in performance. Nonetheless, this reduction is minor, with decrements of 0.29\%/0.19\%, 0.28\%/0.54\%, and 0.14\%/0.21\% observed on Acc, WF1, and MF1, respectively. We argue this is because PhoBERT is a PLM on a general text (Vietnamese Wikipedia\footnote{\url{https://vi.wikipedia.org/wiki/}}) dataset containing no emojis. This leads to the fact that when PhoBERT encounters an emoji, it treats it as an unknown token (see Table~\ref{tab:tokenizer} Appendix~\ref{app:exp}). + +Our pre-trained model ViSoBERT on raw texts outperformed PhoBERT and TwHIN-BERT even when applying two pre-processing emojis techniques. This claims our pre-trained model's ability to handle Vietnamese social media raw texts. + +\textbf{Impact of Teencode on PLMs:} +% Để tiền xử lý được tốt, đòi hỏi người dùng phải thiết kế các rules phù hợp và tốn nhiều thời gian, để tăng một lượng % không đáng kể. +Due to informal and casual communication, social media texts often lead to common linguistic errors, such as misspellings and teencode. For example, the phrase ``ăng kơmmmmm'' should be ``ăn cơm'' (``Eat rice'' in English), and ``ko'' should be ``không'' (``No'' in English). To address this challenge, \citet{nguyen2020exploiting} presented several rules to standardize social media texts. Building upon the previous work, \citet{phobertcnn} proposed a strict and efficient pre-processing technique to clean comments on Vietnamese social media. + +Table~\ref{tab:teencode} (in Appendix~\ref{appendix:pre-process}) shows the results with and without standardizing teencode on social media texts. There is an uptrend across PhoBERT, TwHIN-BERT, and ViSoBERT while applying standardized pre-processing techniques. ViSoBERT, with standardized pre-processing techniques, outperforms almost downstream tasks but spam reviews detection. The possible reason is that the ViSpamReviews dataset contains samples in which users use the word with duplicated characters to improve the comment length while standardizing teencodes leads to misunderstanding. + +% The performances of PhoBERT and TwHIN-BERT increase while applying +%viet them xiu +Experimental results strongly suggest that the improvement achieved by applying complex pre-processing techniques to pre-trained models in the context of Vietnamese social media text is relatively insignificant. Despite the considerable time and effort invested in designing and implementing these techniques, the actual gains in PLMs performance are not substantial and unstable. + + +% See Appendix Table~\ref{tab:teencode} for more detailed experimental results. + +%The Vietnamese alphabet (Vietnamese: chữ Quốc ngữ, lit.'script of the National language') is the modern Latin writing script or writing system for Vietnamese. +% da tui em zia` nha thay :v +%OK + +\textbf{Impact of Vietnamese Diacritics on PLMs:} +Vietnamese words are created from 29 letters, including seven letters using four diacritics (ă, â-ê-ô, ơ-ư, and đ) and five diacritics used to designate tone (as in à, á, ả, ã, and ạ) \cite{ngo2020vietnamese}. These diacritics create meaningful words by combining syllables \cite{diacritics}. For instance, the syllable ``ngu'' can be combined with five different diacritic marks, resulting in five distinct syllables: ``ngú'', ``ngù'', ``ngụ'', ``ngủ'', and ``ngũ''. Each of these syllables functions as a standalone word. + +However, social media text does not always adhere to proper writing conventions. Due to various reasons, many users write text without diacritic marks when commenting on social media platforms. Consequently, effectively handling diacritics in Vietnamese social media becomes a critical challenge. To evaluate the PLMs' capability to address this challenge, we experimented by removing all diacritic marks from the datasets of five downstream tasks. This experiment aimed to assess the model's performance in processing text without diacritics and determine its ability to understand Vietnamese social media content in such cases. + +Table~\ref{tab:diacritics} (in Appendix~\ref{appendix:pre-process}) presents the results of the two best baselines compared to our pre-trained diacritics experiments. The experimental results reveal that the performance of all pre-trained models, including ours, exhibited a significant decrease when dealing with social media comments lacking diacritics. This decline in performance can be attributed to the loss of contextual information caused by the removal of diacritics. The lower the percentage of diacritic removal in each comment, the more significant the performance improvement in all PLMs. However, our ViSoBERT demonstrated a relatively minor reduction in performance across all downstream tasks. This suggests that our model possesses a certain level of robustness and adaptability in comprehending and analyzing Vietnamese social media content without diacritics. We attribute this to the efficiency of the in-domain pre-training data of ViSoBERT. + +In contrast, PhoBERT and TwHIN-BERT experienced a substantial drop in performance across the benchmark datasets. These PLMs struggled to cope with the absence of diacritics in Vietnamese social media comments. The main reason is that the tokenizer of PhoBERT can not encode non-diacritics comments due to not including those in pre-training data. Several tokenized examples of the three best PLMs are presented in Table~\ref{tab:diacriticscomments} (in Appendix~\ref{removingdiacritics}). Thus, the significant decrease in its performance highlights the challenge of handling diacritics on Vietnamese social media. While handling diacritics remains challenging, ViSoBERT demonstrates promising performance, suggesting the potential for specialized language models tailored for Vietnamese social media analysis. + +% 22h toi viet sau :v +%Viết đoạn bình thường thôi, ko cần tiêu đề +%\textbf{Cai nay em muon conclusion phan social media characteristics nhung ma khong biet dat ten gi:} +%In summary, despite the extensive efforts dedicated to pre-processing techniques for Vietnamese social media text, the improvements achieved by these techniques on existing Vietnamese PLMs are limited. Furthermore, the performance of these models, even with pre-processing, falls short of the performance exhibited by our pre-trained ViSoBERT model implemented directly to raw Vietnamese social media texts. This substantiates the superiority of our model in effectively handling and interpreting Vietnamese social media texts, positioning it as a reliable and powerful PLM for various NLP Vietnamese social media tasks. + +\subsection{Impact of Feature-based Extraction to Task-Specific Models} \label{feature-based} + +In task-specific models, the contextualized word embeddings from PLMs are typically employed as input features. We aim to assess the quality of contextualized word embeddings generated by PhoBERT, TwHIN-BERT, and ViSoBERT to verify whether social media data can enhance word representation. These contextualized word embeddings are applied as embedding features to BiLSTM, and BiGRU is randomly initialized before the classification layer. We append a linear prediction layer to the last transformer layer of each PLM regarding the first subword of each word token, which is similar to \citet{bert}. + +% The results are reported in Table~\ref{tab:feature-based} (in Appendix~\ref{appendix:pre-process}). +Our experiment results (see Table~\ref{tab:feature-based} in Appendix~\ref{appendix:pre-process}) demonstrate that the word embeddings generated by our pre-trained language model ViSoBERT outperform other pre-trained embeddings when utilized with BiLSTM and BiGRU for all downstream tasks. The experimental results indicate the significant impact of leveraging social media text data for enriching word embeddings. Furthermore, this finding underscores the effectiveness of our model in capturing the linguistic characteristics prevalent in Vietnamese social media texts. + +Figure~\ref{fig:perepoch} (in Appendix~\ref{plm-based}) presents the performances of the PLMs as input features to BiLSTM and BiGRU on the dev set per epoch in terms of MF1. The results demonstrate that ViSoBERT reaches its peak MF1 score in only 1 to 3 epochs, whereas other PLMs typically require an average of 8 to 10 epochs to achieve on-par performance. This suggests that ViSoBERT has a superior capability to extract Vietnamese social media information compared to other models. + +% For the Hate Speech Spans Detection task, we implemented BiLSTM-CRF and BiGRU-CRF. Following \cite{bert}, for Social Span Detection task, we append a Linear prediction layer to the last Transformer layer of ViSoBERT regarding the first subword of each word token. + +%Các công trình cho thấy kiến trúc CNN tốt hơn LSTM trên dữ liệu Vietnamese social media text +%https://arxiv.org/pdf/1911.09339.pdf +%https://link.springer.com/article/10.1007/s00521-022-07745-w +%https://aclanthology.org/2020.paclic-1.48.pdf + +\section{Conclusion and Future Work} +We presented ViSoBERT, a novel large-scale monolingual pre-trained language model on Vietnamese social media texts. We illustrated that ViSoBERT with fewer parameters outperforms recent strong pre-trained language models such as viBERT, vELECTRA, PhoBERT, XLM-R, XLM-T, TwHIN-BERT, and Bernice and achieves state-of-the-art performances for multiple downstream Vietnamese social media tasks, including emotion recognition, hate speech detection, spam reviews detection, and hate speech spans detection. We conducted extensive analyses to demonstrate the efficiency of ViSoBERT on various Vietnamese social media characteristics, including emojis, teencodes, and diacritics. Furthermore, our pre-trained language model ViSoBERT also shows the potential of leveraging Vietnamese social media text to enhance word representations compared to other PLMs. We hope the widespread use of our open-source ViSoBERT pre-trained language model will advance current NLP social media tasks and applications for Vietnamese. Other low-resource languages can adopt how to create PLMs for enhancing their current NLP social media tasks and relevant applications. + +\newpage +\section*{Limitations} +While we have demonstrated that ViSoBERT can perform state-of-the-art on a range of NLP social media tasks for Vietnamese, we think additional analyses and experiments are necessary to fully comprehend what aspects of ViSoBERT were responsible for its success and what understanding of Vietnamese social media texts ViSoBERT captures. We leave these additional investigations to future research. Moreover, future work aims to explore a broader range of Vietnamese social media downstream tasks that this paper may not cover. Also, we chose to train the base-size transformer model instead of the \textit{Large} variant because base models are more accessible due to their lower computational requirements. For PhoBERT, XLM-R, and TwHIN-BERT, we implemented two versions \textit{Base} and \textit{Large} for all Vietnamese social media downstream tasks. However, it is not a fair comparison due to their significantly larger model configurations. Moreover, regular updates and expansions of the pre-training data are essential to keep up with the rapid evolution of social media. This allows the pre-trained model to adapt effectively to the dynamic linguistic patterns and trends in Vietnamese social media. + +% limit on the range of social media tasks +% extend pre-training data + +\section*{Ethics Statement} +The authors introduce ViSoBERT, a pre-trained language model for investigating social language phenomena in social media in Vietnamese. ViSoBERT is based on pre-training an existing pre-trained language model (i.e., XLM-R), which lessens the influence of its construction on the environment. ViSoBERT makes use of a large-scale corpus of posts and comments from social communities that have been found to express harassment, bullying, incitement of violence, hate, offense, and abuse, as defined by the content policies of social media platforms, including Facebook, YouTube, and TikTok. + +%Sau khi accept mới open ra +\section*{Acknowledgement} +This research was supported by The VNUHCM-University of Information Technology’s Scientific Research Support Fund. We thank the anonymous EMNLP reviewers for their time and helpful suggestions that improved the quality of the paper. +%\section*{Acknowledgement} +%This research was supported by The VNUHCM-University of Information Technology's Scientific Research Support Fund. + + + + +\bibliography{anthology} +\bibliographystyle{acl_natbib} + +\newpage +% \input{emnlp2023-latex/Sections/7-Appendix} +\onecolumn +\appendix + +\section{Tokenizations of the PLMs on Social Comments} +\label{app:socialtok} + +% To assess the tokenized ability of Vietnamese social media textual data, we conducted an analysis on several data samples. Table \ref{tab:tokenizer} shows several actual social comments and their tokenizations with the tokenizers of the two pre-trained language models, ViSoBERT (ours) and PhoBERT (considered as the best strong baseline). + +% \begin{table*}[!ht] +% \centering +% \resizebox{\textwidth}{!}{ +% \begin{tabular}{l|l|l} +% \hline +% \textbf{Comments} & \multicolumn{1}{c|}{\textbf{ViSoBERT}} & \multicolumn{1}{c}{\textbf{PhoBERT}} \\ +% \hline +% \begin{tabular}[c]{@{}l@{}}concặc cáilồn gìđây\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\\\textit{English}: Wut is dis fuckingd1ck \includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\end{tabular} & \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{},~"conc", "ặc", "cái", "l", "ồn", "gì", "đây", \\"\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}\includegraphics[width=12pt]{slightly-smiling-face_1f642.png}", \textless{}/s\textgreater{}\end{tabular} & \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "c o n @ @", "c @ @", "ặ c", "c á @ @", \\"i l @ @", "ồ n", "g @ @","ì @ @", "đ â y", \\ \textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \textless{}/s\textgreater{}\end{tabular} \\ +% \hline +% \begin{tabular}[c]{@{}l@{}}e cảmơn anh\includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}\includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}\\\textit{English}: Thankyou \includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}\includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}\end{tabular} & \textless{}s\textgreater{}, "e", "cảm", "ơn", "anh", "\includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}", "\includegraphics[width=12pt]{smiling-face-with-sunglasses_1f60e.png}", \textless{}/s\textgreater{} & \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{},~"e", "c ả @ @", "m @ @", "ơ n", "a n h", \\, , \textless{}/s\textgreater{} \end{tabular}\\ +% \hline +% \begin{tabular}[c]{@{}l@{}}d4y l4 vj du cko mot cau teencode\\\textit{English}: Th1s 1s 4 teencode s3nt3nc3\end{tabular} & \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "d", "4", "y", "l", "4", "vj", "du", "cko", "mot", \\"cau", "teen", "code", \textless{}/s\textgreater{}\end{tabular} & \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "d @ @", "4 @ @", "y", "l @ @", "4", \\"v @ @", "j", "d u", "c k @ @", "o", "m o @ @", \\"t", "c a u"; "t e @ @", "e n @ @", "c o d e", \textless{}/s\textgreater{}\end{tabular} \\ +% \hline +% \end{tabular}} +% \caption{Actual social comments and their tokenizations with the tokenizers of the two pre-trained language models, ViSoBERT and PhoBERT. \textbf{Disclaimer}: This table contains actual comments on social networks that might be construed as abusive, offensive, or obscene.} +% \label{tab:tokenizer} +% \end{table*} + + + + +We conducted an analysis of average token length by tasks of Pre-trained Language Models to provide insights into how different PLMs perform regarding token length across various Vietnamese social media downstream tasks. Figure~\ref{fig:tokenlen} shows the average token length by Vietnamese social media downstream tasks of baseline PLMs and ours. + +\begin{figure*}[!h] + \centering + \includegraphics[width=\textwidth]{token_length11111-crop.pdf} + \caption{Average token length by tasks of PLMs.} + \label{fig:tokenlen} +\end{figure*} + +\section{Experimental Settings} +\label{app:exp} + +Following the hyperparameters in Table \ref{tab:hyperparameters}, we train our pre-trained language model ViSoBERT for Vietnamese social media texts. +\begin{table}[!ht] +\centering +\begin{tabular}{llr} +\hline +\multirow{7}{*}{Optimizer} & Algorithm & Adam \\ + & Learning rate & 5e-5 \\ + & Epsilon & 1e-8 \\ + & LR scheduler & linear decay and warmup \\ + & Warmup steps & 1000 \\ + & Betas & 0.9 and 0.99 \\ + & Weight decay & 0.01 \\ +\hline +\multirow{3}{*}{Batch} & Sequence length & 128 \\ + & Batch size & 128 \\ + & Vocab size & 15002 \\ +\hline +\multirow{2}{*}{Misc} & Dropout & 0.1 \\ + & Attention dropout & 0.1 \\ +\hline +\end{tabular} +\caption{All hyperparameters established for training ViSoBERT.} +\label{tab:hyperparameters} +\end{table} +\newpage + +\section{PLMs with Pre-processing Techniques} +\label{appendix:pre-process} + +For an in-depth understanding of the impact of social media texts on PLMs, we conducted an analysis of the test results on various processing aspects. Table~\ref{tab:teencode} presents performances of the pre-trained language models on downstream Vietnamese social media tasks by applying word standardizing pre-processing techniques, while Table~\ref{tab:diacritics} presents performances of the pre-trained language models on downstream Vietnamese social media tasks by removing diacritics in all datasets. + +\begin{table*}[!ht] +\centering +\resizebox{\textwidth}{!}{ +\begin{tabular}{c|www|www|www|www|S[table-column-width=1.1cm,table-format=2.2,table-space-text-pre=\dwnarr,color=black]S[table-column-width=1.8cm,table-format=2.2,table-space-text-pre=\dwnarr,color=black]S[table-column-width=1.1cm,table-format=2.2,table-space-text-pre=\dwnarr,color=black]} +\hline +\multirow{2}{*}{\textbf{Model}} & \multicolumn{3}{c|}{\textbf{Emotion Recognition}} & \multicolumn{3}{c|}{\textbf{Hate Speech Detection}} & \multicolumn{3}{c|}{\textbf{Sentiment Analysis}} & \multicolumn{3}{c|}{\textbf{Spam Reviews Detection}} & \multicolumn{3}{c}{\textbf{Hate Speech Spans Detection}} \\ +\cline{2-16} + & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} \\ +\hline +\multicolumn{1}{l|}{PhoBERT$_\text{Large}$} & 64.94 & 64.85 & 62.71 & 87.68 & 87.25 & 65.41 & 76.80 & 76.61 & 76.61 & 89.47 & 89.41 & 76.12 & 91.73 & 91.62 & 86.59 \\ +$\Delta$ & \multicolumn{1}{y}{\uparr 0.23} & \multicolumn{1}{y}{\uparr 0.19} & \multicolumn{1}{y|}{\uparr 0.16} & \multicolumn{1}{y}{\uparr 0.36} & \multicolumn{1}{y}{\uparr 0.27} & \multicolumn{1}{y|}{\uparr 0.27} & \multicolumn{1}{y}{\uparr 0.28} & \multicolumn{1}{y}{\uparr 0.25} & \multicolumn{1}{y|}{\uparr 0.25} & \multicolumn{1}{z}{\dwnarr 0.65} & \multicolumn{1}{z}{\dwnarr 0.62} & \multicolumn{1}{z|}{\dwnarr 0.76} & \multicolumn{1}{y}{\uparr 0.29} & \multicolumn{1}{y}{\uparr 0.16} & \multicolumn{1}{y}{\uparr 0.03} \\ +\multicolumn{1}{l|}{TwHIN-BERT$_\text{Large}$} & 64.42 & 64.46 & 61.28 & 87.82 & 87.28 & 65.68 & 77.17 & 76.94 & 76.94 & 89.49 & 89.43 & 76.35 & 91.74 & 91.64 & 86.67 \\ +$\Delta$ & \multicolumn{1}{y}{\uparr 0.21} & \multicolumn{1}{y}{\uparr 0.17} & \multicolumn{1}{y|}{\uparr 0.16} & \multicolumn{1}{y}{\uparr 0.59} & \multicolumn{1}{y}{\uparr 0.50} & \multicolumn{1}{y|}{\uparr 0.45} & \multicolumn{1}{y}{\uparr 0.25} & \multicolumn{1}{y}{\uparr 0.11} & \multicolumn{1}{y|}{\uparr 0.11} & \multicolumn{1}{z}{\dwnarr 0.98} & \multicolumn{1}{z}{\dwnarr 0.99} & \multicolumn{1}{z|}{\dwnarr 0.93} & \multicolumn{1}{y}{\uparr 0.29} & \multicolumn{1}{y}{\uparr 0.17} & \multicolumn{1}{y}{\uparr 0.02} \\ +\hdashline +\multicolumn{1}{l|}{ViSoBERT [$\clubsuit$]} & \bfseries 68.25 & \bfseries 68.52 & \bfseries 65.94 & \bfseries 88.53 & \bfseries 88.33 & \bfseries 68.82 & \bfseries 78.01 & \bfseries 77.88 & \bfseries 77.88 & 90.83 & 90.75 & 78.77 & \bfseries 91.89 & \bfseries 91.82 & \bfseries 86.93 \\ +$\Delta$ & \multicolumn{1}{y}{\uparr 0.15} & \multicolumn{1}{y}{\uparr 0.15} & \multicolumn{1}{y|}{\uparr 0.06} & \multicolumn{1}{y}{\uparr 0.02} & \multicolumn{1}{y}{\uparr 0.02} & \multicolumn{1}{y|}{\uparr 0.08} & \multicolumn{1}{y}{\uparr 0.18} & \multicolumn{1}{y}{\uparr 0.13} & \multicolumn{1}{y|}{\uparr 0.13} & \multicolumn{1}{z}{\dwnarr 0.16} & \multicolumn{1}{z}{\dwnarr 0.17} & \multicolumn{1}{z|}{\dwnarr 0.29} & \multicolumn{1}{y}{\uparr 0.27} & \multicolumn{1}{y}{\uparr 0.25} & \multicolumn{1}{y}{\uparr 0.13} \\ +\hline +\multicolumn{1}{l|}{ViSoBERT [$\vardiamondsuit$]} & 68.10 & 68.37 & 65.88 & 88.51 & 88.31 & 68.74 & 77.83 & 77.75 & 77.75 & \bfseries 90.99 & \bfseries 90.92 & \bfseries 79.06 & 91.62 & 91.57 & 86.80 \\ +\hline +\end{tabular}} +\caption{Performances of the pre-trained language models on downstream Vietnamese social media tasks by applying word standardizing pre-processing techniques. $[\clubsuit] \text{ and } [\vardiamondsuit]$ denoted with and without standardizing word technique, respectively. $\Delta$ denoted the increase ($\uparrow$) and the decrease ($\downarrow$) in performances of the pre-trained language models compared to its competitors without normalizing teencodes.} +\label{tab:teencode} +\end{table*} + +To emphasize the essentials of diacritics, we conducted an analysis on several data samples by removing 100\%, 75\%, 50\%, and 25\% diacritics of total words that included diacritics in each comment of five downstream tasks. Table~\ref{tab:diacritics} presents performances of the pre-trained language models on downstream Vietnamese social media tasks by removing diacritics in all datasets. + +\begin{table*}[!ht] +\centering +\resizebox{\textwidth}{!}{ +\begin{tabular}{c|zzz|zzz|zzz|zzz|S[table-column-width=1.1cm,table-format=2.2,table-space-text-pre=\dwnarr,color=red]S[table-column-width=1.8cm,table-format=2.2,table-space-text-pre=\dwnarr,color=red]S[table-column-width=1.1cm,table-format=2.2,table-space-text-pre=\dwnarr,color=red]} +\hline +\multirow{2}{*}{\textbf{Model}} & \multicolumn{3}{c|}{\textbf{Emotion Regconition}} & \multicolumn{3}{c|}{\textbf{Hate Speech Detection}} & \multicolumn{3}{c|}{\textbf{Sentiment Analysis}} & \multicolumn{3}{c|}{\textbf{Spam Reviews Detection}} & \multicolumn{3}{c}{\textbf{Hate Speech Spans Detection}} \\ +\cline{2-16} + & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} \\ +\hline +\multicolumn{16}{c}{\textbf{\textit{Removing 100\% diacritics in each comment}}} \\ +\hline +\multicolumn{1}{l|}{PhoBERT$_\text{Large}$} & \multicolumn{1}{w}{49.35} & \multicolumn{1}{w}{49.18} & \multicolumn{1}{w|}{43.95} & \multicolumn{1}{w}{81.25} & \multicolumn{1}{w}{81.42} & \multicolumn{1}{w|}{55.43} & \multicolumn{1}{w}{62.38} & \multicolumn{1}{w}{62.36} & \multicolumn{1}{w|}{62.36} & \multicolumn{1}{w}{87.68} & \multicolumn{1}{w}{87.56} & \multicolumn{1}{w|}{71.89} & \multicolumn{1}{w}{91.32} & \multicolumn{1}{w}{91.37} & \multicolumn{1}{w}{86.43} \\ +$\Delta$ & \dwnarr 15.36 & \dwnarr 15.48 & \dwnarr 18.60 & \dwnarr \color{red} 6.07 & \dwnarr 5.56 & \dwnarr 9.71 & \dwnarr 14.14 & \dwnarr 14.00 & \dwnarr 13.86 & \dwnarr 2.44 & \dwnarr 2.47 & \dwnarr 4.99 & \dwnarr 0.12 & \dwnarr 0.09 & \dwnarr 0.13 \\ +\multicolumn{1}{l|}{TwHIN-BERT$_\text{Large}$} & \multicolumn{1}{w}{49.32} & \multicolumn{1}{w}{49.15} & \multicolumn{1}{w|}{43.52} & \multicolumn{1}{w}{84.25} & \multicolumn{1}{w}{78.48} & \multicolumn{1}{w|}{51.32} & \multicolumn{1}{w}{66.66} & \multicolumn{1}{w}{66.68} & \multicolumn{1}{w|}{66.68} & \multicolumn{1}{w}{89.45} & \multicolumn{1}{w}{89.26} & \multicolumn{1}{w|}{74.59} & \multicolumn{1}{w}{91.12} & \multicolumn{1}{w}{91.22} & \multicolumn{1}{w}{86.33} \\ +$\Delta$ & \dwnarr 14.89 & \dwnarr 15.14 & \dwnarr 17.60 & \dwnarr 2.98 & \dwnarr 8.30 & \dwnarr 12.99 & \dwnarr 10.26 & \dwnarr 10.15 & \dwnarr 10.15 & \dwnarr 1.02 & \dwnarr 1.16 & \dwnarr 2.69 & \dwnarr 0.33 & \dwnarr 0.25 & \dwnarr 0.32 \\ \hdashline +\multicolumn{1}{l|}{ViSoBERT $[\clubsuit]$} & \multicolumn{1}{w}{61.96} & \multicolumn{1}{w}{62.05} & \multicolumn{1}{w|}{58.48} & \multicolumn{1}{w}{87.29} & \multicolumn{1}{w}{86.76} & \multicolumn{1}{w|}{64.87} & \multicolumn{1}{w}{72.95} & \multicolumn{1}{w}{72.91} & \multicolumn{1}{w|}{72.91} & \multicolumn{1}{w}{89.75} & \multicolumn{1}{w}{89.72} & \multicolumn{1}{w|}{76.12} & \multicolumn{1}{w}{91.48} & \multicolumn{1}{w}{91.42} & \multicolumn{1}{w}{86.69} \\ +$\Delta$ & \dwnarr 6.14 & \dwnarr 6.32 & \dwnarr 7.40 & \dwnarr 1.22 & \dwnarr 1.55 & \dwnarr 3.90 & \dwnarr 4.88 & \dwnarr 4.84 & \dwnarr 4.84 & \dwnarr 1.24 & \dwnarr 1.20 & \dwnarr 2.94 & \dwnarr 0.14 & \dwnarr 0.15 & \dwnarr 0.11 \\ +\hline +\multicolumn{16}{c}{\textbf{\textit{Removing 75\% diacritics in each comment}}} \\ +\hline +\multicolumn{1}{l|}{PhoBERT$_\text{Large}$} & \multicolumn{1}{w}{51.94} & \multicolumn{1}{w}{51.79} & \multicolumn{1}{w|}{47.79} & \multicolumn{1}{w}{84.74} & \multicolumn{1}{w}{84.03} & \multicolumn{1}{w|}{58.37} & \multicolumn{1}{w}{66.00} & \multicolumn{1}{w}{65.98} & \multicolumn{1}{w|}{65.98} & \multicolumn{1}{w}{88.23} & \multicolumn{1}{w}{88.12} & \multicolumn{1}{w|}{72.42} & \multicolumn{1}{w}{90.38} & \multicolumn{1}{w}{90.23} & \multicolumn{1}{w}{85.42} \\ +$\Delta$ & \dwnarr 12.77 & \dwnarr 12.87 & \dwnarr 14.76 & \dwnarr 2.58 & \dwnarr 2.95 & \dwnarr 6.77 & \dwnarr 10.52 & \dwnarr 10.38 & \dwnarr 10.24 & \dwnarr 1.89 & \dwnarr 1.91 & \dwnarr 4.46 & \dwnarr 1.06 & \dwnarr 1.23 & \dwnarr 1.14 \\ +\multicolumn{1}{l|}{TwHIN-BERT$_\text{Large}$} & \multicolumn{1}{w}{51.32} & \multicolumn{1}{w}{51.17} & \multicolumn{1}{w|}{44.63} & \multicolumn{1}{w}{83.22} & \multicolumn{1}{w}{81.42} & \multicolumn{1}{w|}{52.24} & \multicolumn{1}{w}{67.23} & \multicolumn{1}{w}{67.32} & \multicolumn{1}{w|}{67.32} & \multicolumn{1}{w}{89.12} & \multicolumn{1}{w}{88.95} & \multicolumn{1}{w|}{75.20} & \multicolumn{1}{w}{90.62} & \multicolumn{1}{w}{89.93} & \multicolumn{1}{w}{85.81} \\ +$\Delta$ & \dwnarr 12.89 & \dwnarr 13.12 & \dwnarr 16.49 & \dwnarr 4.01 & \dwnarr 5.36 & \dwnarr 12.99 & \dwnarr 9.69 & \dwnarr 9.51 & \dwnarr 9.51 & \dwnarr 1.35 & \dwnarr 1.47 & \dwnarr 2.08 & \dwnarr 0.83 & \dwnarr 1.54 & \dwnarr 0.84 \\ +\hdashline +\multicolumn{1}{l|}{ViSoBERT $[\spadesuit]$} & \multicolumn{1}{w}{62.34} & \multicolumn{1}{w}{62.26} & \multicolumn{1}{w|}{58.13} & \multicolumn{1}{w}{87.35} & \multicolumn{1}{w}{86.88} & \multicolumn{1}{w|}{65.12} & \multicolumn{1}{w}{73.90} & \multicolumn{1}{w}{73.97} & \multicolumn{1}{w|}{73.97} & \multicolumn{1}{w}{90.41} & \multicolumn{1}{w}{90.31} & \multicolumn{1}{w|}{76.17} & \multicolumn{1}{w}{91.02} & \multicolumn{1}{w}{91.17} & \multicolumn{1}{w}{86.02} \\ +$\Delta$ & \dwnarr 5.76 & \dwnarr 6.11 & \dwnarr 7.75 & \dwnarr 1.16 & \dwnarr 1.43 & \dwnarr 3.65 & \dwnarr 3.93 & \dwnarr 3.78 & \dwnarr 3.78 & \dwnarr 0.58 & \dwnarr 0.61 & \dwnarr 2.89 & \dwnarr 0.60 & \dwnarr 0.40 & \dwnarr 0.78 \\ +\hline +\multicolumn{16}{c}{\textbf{\textit{Removing 50\% diacritics in each comment}}} \\ +\hline +\multicolumn{1}{l|}{PhoBERT$_\text{Large}$} & \multicolumn{1}{w}{57.28} & \multicolumn{1}{w}{57.36} & \multicolumn{1}{w|}{54.02} & \multicolumn{1}{w}{85.29} & \multicolumn{1}{w}{84.71} & \multicolumn{1}{w|}{59.40} & \multicolumn{1}{w}{66.57} & \multicolumn{1}{w}{66.46} & \multicolumn{1}{w|}{66.46} & \multicolumn{1}{w}{89.02} & \multicolumn{1}{w}{88.81} & \multicolumn{1}{w|}{73.10} & \multicolumn{1}{w}{90.42} & \multicolumn{1}{w}{90.47} & \multicolumn{1}{w}{85.62} \\ +$\Delta$ & \dwnarr 7.43 & \dwnarr 7.30 & \dwnarr 8.53 & \dwnarr 2.03 & \dwnarr 2.27 & \dwnarr 5.74 & \dwnarr 9.95 & \dwnarr 9.90 & \dwnarr 9.76 & \dwnarr 1.10 & \dwnarr 1.22 & \dwnarr 3.78 & \dwnarr 1.02 & \dwnarr 0.99 & \dwnarr 0.94 \\ +\multicolumn{1}{l|}{TwHIN-BERT$_\text{Large}$} & \multicolumn{1}{w}{53.70} & \multicolumn{1}{w}{53.39} & \multicolumn{1}{w|}{49.55} & \multicolumn{1}{w}{83.41} & \multicolumn{1}{w}{83.31} & \multicolumn{1}{w|}{55.22} & \multicolumn{1}{w}{70.42} & \multicolumn{1}{w}{70.53} & \multicolumn{1}{w|}{70.53} & \multicolumn{1}{w}{89.33} & \multicolumn{1}{w}{89.05} & \multicolumn{1}{w|}{75.32} & \multicolumn{1}{w}{90.73} & \multicolumn{1}{w}{90.12} & \multicolumn{1}{w}{85.92} \\ +$\Delta$ & \dwnarr 10.51 & \dwnarr 10.90 & \dwnarr 11.57 & \dwnarr 3.82 & \dwnarr 3.47 & \dwnarr 10.01 & \dwnarr 6.50 & \dwnarr 6.30 & \dwnarr 6.30 & \dwnarr 1.14 & \dwnarr 1.37 & \dwnarr 1.96 & \dwnarr 0.72 & \dwnarr 1.35 & \dwnarr 0.73 \\ +\hdashline +\multicolumn{1}{l|}{ViSoBERT $[\varheartsuit]$} & \multicolumn{1}{w}{62.96} & \multicolumn{1}{w}{62.87} & \multicolumn{1}{w|}{60.55} & \multicolumn{1}{w}{87.44} & \multicolumn{1}{w}{87.10} & \multicolumn{1}{w|}{65.25} & \multicolumn{1}{w}{74.76} & \multicolumn{1}{w}{74.72} & \multicolumn{1}{w|}{74.72} & \multicolumn{1}{w}{90.41} & \multicolumn{1}{w}{90.35} & \multicolumn{1}{w|}{77.31} & \multicolumn{1}{w}{91.12} & \multicolumn{1}{w}{91.24} & \multicolumn{1}{w}{86.22} \\ +$\Delta$ & \dwnarr 5.14 & \dwnarr 5.50 & \dwnarr 5.33 & \dwnarr 1.07 & \dwnarr 1.21 & \dwnarr 3.52 & \dwnarr 3.07 & \dwnarr 3.03 & \dwnarr 3.03 & \dwnarr 0.58 & \dwnarr 0.57 & \dwnarr 1.75 & \dwnarr 0.50 & \dwnarr 0.33 & \dwnarr 0.58 \\ +\hline +\multicolumn{16}{c}{\textbf{\textit{Removing 25\% diacritics in each comment}}} \\ +\hline +\multicolumn{1}{l|}{PhoBERT$_\text{Large}$} & \multicolumn{1}{w}{61.03} & \multicolumn{1}{w}{60.80} & \multicolumn{1}{w|}{57.87} & \multicolumn{1}{w}{85.97} & \multicolumn{1}{w}{85.51} & \multicolumn{1}{w|}{61.96} & \multicolumn{1}{w}{73.42} & \multicolumn{1}{w}{73.28} & \multicolumn{1}{w|}{73.28} & \multicolumn{1}{w}{89.80} & \multicolumn{1}{w}{89.59} & \multicolumn{1}{w|}{75.53} & \multicolumn{1}{w}{90.63} & \multicolumn{1}{w}{90.69} & \multicolumn{1}{w}{85.76} \\ +$\Delta$ & \dwnarr 3.68 & \dwnarr 3.86 & \dwnarr 4.68 & \dwnarr 1.35 & \dwnarr 1.47 & \dwnarr 3.18 & \dwnarr 3.10 & \dwnarr 3.08 & \dwnarr 2.94 & \dwnarr 0.32 & \dwnarr 0.44 & \dwnarr 1.35 & \dwnarr 0.81 & \dwnarr 0.77 & \dwnarr 0.80 \\ +\multicolumn{1}{l|}{TwHIN-BERT$_\text{Large}$} & \multicolumn{1}{w}{61.18} & \multicolumn{1}{w}{60.98} & \multicolumn{1}{w|}{57.42} & \multicolumn{1}{w}{86.85} & \multicolumn{1}{w}{86.13} & \multicolumn{1}{w|}{63.14} & \multicolumn{1}{w}{73.21} & \multicolumn{1}{w}{73.11} & \multicolumn{1}{w|}{73.11} & \multicolumn{1}{w}{89.91} & \multicolumn{1}{w}{89.43} & \multicolumn{1}{w|}{76.32} & \multicolumn{1}{w}{91.09} & \multicolumn{1}{w}{90.72} & \multicolumn{1}{w}{86.02} \\ +$\Delta$ & \dwnarr 3.03 & \dwnarr 3.31 & \dwnarr 3.70 & \dwnarr 0.38 & \dwnarr 0.65 & \dwnarr 2.09 & \dwnarr 3.71 & \dwnarr 3.72 & \dwnarr 3.72 & \dwnarr 0.56 & \dwnarr 0.99 & \dwnarr 0.96 & \dwnarr 0.36 & \dwnarr 0.75 & \dwnarr 0.63 \\ +\hdashline +\multicolumn{1}{l|}{ViSoBERT $[\maltese]$} & \multicolumn{1}{w}{64.64} & \multicolumn{1}{w}{64.53} & \multicolumn{1}{w|}{61.29} & \multicolumn{1}{w}{87.85} & \multicolumn{1}{w}{87.56} & \multicolumn{1}{w|}{66.54} & \multicolumn{1}{w}{75.42} & \multicolumn{1}{w}{75.44} & \multicolumn{1}{w|}{75.44} & \multicolumn{1}{w}{90.76} & \multicolumn{1}{w}{90.64} & \multicolumn{1}{w|}{78.15} & \multicolumn{1}{w}{91.22} & \multicolumn{1}{w}{91.24} & \multicolumn{1}{w}{86.47} \\ +$\Delta$ & \dwnarr 3.43 & \dwnarr 3.84 & \dwnarr 4.59 & \dwnarr 0.66 & \dwnarr 0.75 & \dwnarr 2.23 & \dwnarr 2.41 & \dwnarr 2.31 & \dwnarr 2.31 & \dwnarr 0.23 & \dwnarr 0.28 & \dwnarr 0.91 & \dwnarr 0.40 & \dwnarr 0.33 & \dwnarr 0.33 \\ +\hline +\multicolumn{1}{l|}{ViSoBERT $[\vardiamondsuit]$} & \multicolumn{1}{w}{\bfseries 68.10} & \multicolumn{1}{w}{\bfseries 68.37} & \multicolumn{1}{w|}{\bfseries 65.88} & \multicolumn{1}{w}{\bfseries 88.51} & \multicolumn{1}{w}{\bfseries 88.31} & \multicolumn{1}{w|}{\bfseries 68.77} & \multicolumn{1}{w}{\bfseries 77.83} & \multicolumn{1}{w}{\bfseries 77.75} & \multicolumn{1}{w|}{\bfseries 77.75} & \multicolumn{1}{w}{\bfseries 90.99} & \multicolumn{1}{w}{\bfseries 90.92} & \multicolumn{1}{w|}{\bfseries 79.06} & \multicolumn{1}{w}{\bfseries 91.62} & \multicolumn{1}{w}{\bfseries 91.57} & \multicolumn{1}{w}{\bfseries 86.80} \\ +\hline +\end{tabular}} +\caption{Performances of the pre-trained language models on downstream Vietnamese social media tasks by removing diacritics in all datasets. $[\clubsuit]$, $[\spadesuit]$, $[\varheartsuit]$, $[\maltese]$ and $[\vardiamondsuit]$ denoted the performances of our pre-trained on removing 100\%, 75\%, 50\%, 25\% in each comment, respectively, and not removing diacritics marks dataset, respectively. $\Delta$ denoted the increase ($\uparrow$) and the decrease ($\downarrow$) in performances of the pre-trained language models compared to its competitors without removing diacritics marks.} +\label{tab:diacritics} +\end{table*} +% cai bang to qua thay :v +% nhìn vào người ta accept liền :D + +\newpage +\section{PLM-based Features for BiLSTM and BiGRU} +\label{plm-based} + +We conduct experiments with various models, including BiLSTM and BiGRU, to understand better the word embedding feature extracted from the pre-trained language models. Table \ref{tab:feature-based} shows performances of the pre-trained language model as input features to BiLSTM and BiGRU on downstream Vietnamese social media tasks. + +\begin{table*}[!ht] +\centering +\resizebox{\textwidth}{!}{ +\begin{tabular}{l|ccc|ccc|ccc|>{\centering\arraybackslash}m{1.1cm}>{\centering\arraybackslash}m{1.1cm}>{\centering\arraybackslash}m{1.1cm}|>{\centering\arraybackslash}m{1.1cm}>{\centering\arraybackslash}m{1.8cm}>{\centering\arraybackslash}m{1.1cm}} +\hline +\multicolumn{1}{c|}{\multirow{2}{*}{\textbf{Model}}} & \multicolumn{3}{c|}{\textbf{Emotion Regconition}} & \multicolumn{3}{c|}{\textbf{Hate Speech Detection}} & \multicolumn{3}{c|}{\textbf{Sentiment Analysis}} & \multicolumn{3}{c|}{\textbf{Spam Reviews Detection}} & \multicolumn{3}{c}{\textbf{Hate Speech Spans Detection}} \\ +\cline{2-16} +\multicolumn{1}{c|}{} & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} & \textbf{Acc} & \textbf{WF1} & \textbf{MF1} \\ +\hline +\multicolumn{16}{c}{\textbf{\textit{BiLSTM}}} \\ +\hline +PhoBERT$_\text{Large}$ & 57.58 & 56.65 & 50.55 & 86.11 & 84.04 & 56.03 & 69.71 & 69.70 & 69.70 & 87.80 & 87.10 & 68.95 & 84.01 & 80.70 & 74.35 \\ +TwHIN-BERT$_\text{Large}$ & 61.47 & 61.31 & 56.73 & 83.14 & 82.72 & 55.84 & 64.76 & 64.82 & 64.82 & 88.73 & 88.23 & 72.18 & 85.92 & 84.43 & 78.28 \\ +\hdashline +ViSoBERT & \textbf{63.06} & \textbf{62.36} & \textbf{59.16} & \textbf{87.62} & \textbf{86.81} & \textbf{64.82} & \textbf{73.52} & \textbf{73.50} & \textbf{73.50} & \textbf{90.11} & \textbf{89.79} & \textbf{75.71} & \textbf{88.37} & \textbf{87.87} & \textbf{82.18} \\ +\hline +\multicolumn{16}{c}{\textbf{\textit{BiGRU}}} \\ +\hline +PhoBERT$_\text{Large}$ & 55.12 & 54.53 & 49.59 & 85.21 & 83.23 & 54.59 & 70.01 & 70.01 & 70.01 & 86.06 & 84.89 & 62.54 & 84.23 & 81.01 & 74.57 \\ +TwHIN-BERT$_\text{Large}$ & 60.46 & 60.30 & 55.23 & 85.73 & 83.45 & 54.74 & 63.11 & 61.39 & 61.39 & 87.67 & 86.38 & 66.83 & 86.10 & 84.52 & 78.49 \\ +\hdashline +ViSoBERT & \textbf{63.20} & \textbf{63.25} & \textbf{60.73} & \textbf{87.02} & \textbf{86.25} & \textbf{63.36} & \textbf{70.48} & \textbf{70.53} & \textbf{70.53} & \textbf{89.33} & \textbf{88.98} & \textbf{76.57} & \textbf{88.88} & \textbf{88.19} & \textbf{82.63} \\ +\hline +\end{tabular}} +\caption{Performances of the pre-trained language models as input features to BiLSTM and BiGRU on downstream Vietnamese social media tasks.} +\label{tab:feature-based} +\end{table*} + +We implemented various PLMs when used as input features in combination with BiLSTM and BiGRU models to verify the ability to extract Vietnamese social media texts. The evaluation is conducted on the dev set, and the performance is measured per epoch for downstream tasks. Table~\ref{tab:feature-based} shows performances of the PLMs as input features to BiLSTM and BiGRU on the dev set per epoch. +\begin{figure}[!ht] + \centering + \begin{subfigure}[a]{0.323\textwidth} + \centering + \includegraphics[width=\textwidth]{vsmec.pdf} + \caption{Emotion Regconition} + \label{fig:ER} + \end{subfigure} + \hfill + \begin{subfigure}[a]{0.323\textwidth} + \centering + \includegraphics[width=\textwidth]{vihsd.pdf} + \caption{Hate Speech Detection} + \label{fig:HSD} + \end{subfigure} + \hfill + \begin{subfigure}[a]{0.323\textwidth} + \centering + \includegraphics[width=\textwidth]{vlsp.pdf} + \caption{Sentiment Analysis} + \label{fig:SA} + \end{subfigure} + \hfill + \begin{subfigure}[a]{0.323\textwidth} + \centering + \includegraphics[width=\textwidth]{vispam.pdf} + \caption{Spam Reviews Detection} + \label{fig:SRD} + \end{subfigure} + % \hfill + \begin{subfigure}[a]{0.323\textwidth} + \centering + \includegraphics[width=\textwidth]{vihos.pdf} + \caption{Hate Speech Spans Detection} + \label{fig:HSSD} + \end{subfigure} + \begin{subfigure}[c]{0.323\textwidth} + \vspace*{-25pt} + \hspace*{40pt}\includegraphics[scale=0.430]{legend.pdf} + \end{subfigure} + +\caption{Performances of the PLMs as input features to BiLSTM and BiGRU on the dev set per epoch on Vietnamese social media downstream tasks. \textit{Large} versions of PhoBERT and TwHIN-BERT are implemented for these experiments.} +\label{fig:perepoch} +\end{figure} + +\newpage +% ~\newpage +\section{Updating New Spans of Hate Speech Span Detection Samples with Pre-processing Techniques} +\label{app:updatingViHOS} + +Due to performing pre-processing techniques, the span positions on the data samples can be changed. Therefore, we present Algorithm \ref{agrt:updatenewspans}, which shows how to update new span positions of samples applied with pre-processing techniques in the Hate Speech Spans Detection task (UIT-ViHOS dataset). This algorithm takes as input a comment and its spans and returns the pre-processed comment and its span along with pre-processing techniques. + +\begin{algorithm}[H] +\begin{algorithmic}[1] + +\Procedure{Algorithm}{$\text{comment}$, $\text{label}$, $\text{delete}$}\caption{Updating new spans of samples applied with pre-processing techniques in Hate Speech Spans Detection task (UIT-ViHOS dataset).}\label{agrt:updatenewspans} + \State \textbf{assert} $\text{len(comment)}$ == $\text{len(label)}$ + \State $\text{new\_comment} \gets []$, $\text{new\_label} \gets []$ + % \State $\text{new\_label} \gets []$ + \For{$i \gets 0$ \textbf{to} $\text{len(comment)}$} + \State $\text{check} \gets 0$ + \If{$\text{comment}[i]$ \textbf{in} $\text{emoji\_to\_word.keys()}$} + \If{$\text{delete}$} + % \Comment{\textit{delete} variable stands for is removing emoji} + \State \textbf{continue} + \EndIf + \For{$j \gets 0$ \textbf{to} $\text{len(emoji\_to\_word[comment[i]].split(` '))}$} + \If{$\text{label}[i]$ == `B-T'} + \If{$\text{check}$ == $0$} + \State $\text{check} \gets \text{check} + 1$, $\text{new\_label.append(label[i])}$ + % \State $\text{new\_label.append(label[i])}$ + \Else + \State $\text{new\_label.append(`I-T')}$ + \EndIf + \Else + \State $\text{new\_label.append(label[i])}$ + \EndIf + \State $\text{new\_comment.append(emoji\_to\_word[comment[i]].split(` ')[j])}$ + \EndFor + \Else + \State $\text{new\_sentence.append(comment[i])}$ + \State $\text{new\_label.append(label[i])}$ + \EndIf + \State \textbf{assert} $\text{len(new\_comment)}$ == $\text{len(new\_label)}$ + \EndFor + \State \textbf{return} \text{new\_comment}, \text{new\_label} +\EndProcedure + +\end{algorithmic} +\end{algorithm} + +% \newpage +\section{Tokenizations of the PLMs on Removing Diacritics Social Comments}\label{removingdiacritics} +We analyze several data samples to see the tokenized ability of Vietnamese social media textual data while removing diacritics on comments. Table~\ref{tab:diacriticscomments} shows several non-diacritics Vietnamese social comments and their tokenizations with the tokenizers of the three best pre-trained language models, ViSoBERT (ours), PhoBERT, and TwHIN-BERT. + +\begin{table}[!ht] +\resizebox{\textwidth}{!}{% +\begin{tabular}{lll} +\hline +\multicolumn{1}{l|}{\textbf{Model}} & + \multicolumn{1}{c|}{\textbf{Example 1}} & + \multicolumn{1}{c}{\textbf{Example 2}} \\ \hline +\multicolumn{1}{l|}{Raw comment} & \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}cái con đồ chơi đó mua ở đâu nhỉ . cười đéo nhặt được \\mồm \includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png} +\\\textit{English}: where did you buy that toy . LMAO \includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\end{tabular}} & \begin{tabular}[c]{@{}l@{}}Ôi bố cái lũ thanh niên hãm lol. Đẹp mặt quá \includegraphics[width=12pt]{unamused-face_1f612.png}\includegraphics[width=12pt]{unamused-face_1f612.png}\\\textit{English}:~Oh my god damn teenagers, lol. So deserved \includegraphics[width=12pt]{unamused-face_1f612.png}\includegraphics[width=12pt]{unamused-face_1f612.png}\end{tabular} \\ +\hline +\multicolumn{3}{c}{\textit{\textbf{Removing 100\% diacritics in each comment}}} \\ \hline +\multicolumn{1}{l|}{Comment} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}cai con do choi do mua o dau nhi . cuoi deo nhat duoc \\mom . \includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\end{tabular}} & + Oi bo cai lu thanh nien ham lol. Dep mat qua \includegraphics[width=12pt]{unamused-face_1f612.png}\includegraphics[width=12pt]{unamused-face_1f612.png} \\ \hline +\multicolumn{1}{l|}{PhoBERT} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "c a i", "c o n", "d o", "c h o @ @", "i", "d o", "m u a", \\ "o", "d @ @", "a u", "n h i", ".", "c u @ @", "o i", \\"d @ @", "e o", "n h @ @", "a t", "d u @ @", "o c", \\"m o m", ".", \textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \textless{}/s\textgreater{}\end{tabular}} & + \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "O @ @", "i", "b o", "c a i", "l u", "t h a n h", "n i @ @",\\"e n", "h a m", "l o @ @", "l", ".", "D e @ @", "p", "m a t",\\"q u a", \textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \textless{}/s\textgreater{}\end{tabular} \\ \hline +\multicolumn{1}{l|}{TwHIN-BERT} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "cai", "con", "do", "cho", "i", "do", "mua", "o", "dau", \\"nhi", "", ".", "cu", "oi", "de", "o", "nha", "t", "du", "oc", \\"mom", "", ".", "", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", \textless{}/s\textgreater{}\end{tabular}} & + \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "Oi", "bo", "cai", "lu", "thanh", "nie", "n", "ham", "lol", \\".", "De", "p", "mat", "qua", "", "\includegraphics[width=12pt]{unamused-face_1f612.png}", "\includegraphics[width=12pt]{unamused-face_1f612.png}", \textless{}/s\textgreater{}\end{tabular} \\ \hline +\multicolumn{1}{l|}{ViSoBERT} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "cai", "con", "do", "choi", "do", "mua", "o", "dau", "nhi", \\".","cu", "oi", "d", "eo", "nhat", "duoc", "m", "om", ".", \\"\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", \textless{}/s\textgreater{}\end{tabular}} & + \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "O", "i", "bo", "cai", "lu", "thanh", "ni", "en", "h", "am", \\"lol", ".", "D", "ep", "mat", "qua", "\includegraphics[width=12pt]{unamused-face_1f612.png}\includegraphics[width=12pt]{unamused-face_1f612.png}", \textless{}/s\textgreater{}\end{tabular} \\ \hline +\multicolumn{3}{c}{\textit{\textbf{Removing 75\% diacritics in each comment}}} \\ \hline +\multicolumn{1}{l|}{Comment} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}cai con do chơi do mua o đâu nhi . cười deo nhat duoc \\mom . \includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\end{tabular}} & + Ôi bo cai lu thanh niên hãm lol. Dep mat qua \includegraphics[width=12pt]{unamused-face_1f612.png}\includegraphics[width=12pt]{unamused-face_1f612.png} \\ \hline +\multicolumn{1}{l|}{PhoBERT} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "c a i", "c o n", "d o", "c h ơ i", "d o", "m u a", "o", \\"đ â u", "n h i", ".", "c ư ờ i", "d @ @", "e o", "n h @ @", \\"a t", "d u @ @", "o c", "m o m", ".",\textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \\\textless{}unk\textgreater{}, \textless{}/s\textgreater{}\end{tabular}} & + \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "Ô i", "b o", "c a i", "l u", "t h a n h \_ n i ê n", "h ã m", \\"l o @ @", "l", ".", "D e @ @", "p", "m a t", "q u a", \textless{}unk\textgreater{}, \\\textless{}unk\textgreater{}, \textless{}/s\textgreater{}\end{tabular} \\ \hline +\multicolumn{1}{l|}{TwHIN-BERT} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "cai", "con", "do", "chơi", "do", "mua", "o", "đâu", \\"nhi", "", ".", "cười", "de", "o", "nha", "t", "du", "oc", \\"mom", "", ".", "", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", \textless{}/s\textgreater{}\end{tabular}} & + \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "Ô", "i", "bo", "cai", "lu", "thanh", "niên", "", "hã", "m", \\"lol", ".", "De", "p", "mat", "qua", "", "\includegraphics[width=12pt]{unamused-face_1f612.png}", "\includegraphics[width=12pt]{unamused-face_1f612.png}", \textless{}/s\textgreater{}\end{tabular} \\ \hline +\multicolumn{1}{l|}{ViSoBERT} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "cai", "con", "do", "chơi", "do", "mua", "o", "đâu", \\"nhi", ".", "cười", "d", "eo", "nhat", "duoc", "m", "om", \\".", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", \textless{}/s\textgreater{}\end{tabular}} & + \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "Ôi", "bo", "cai", "lu", "thanh", "n", "iên", "hã", "m", \\"lol", ".", "D", "ep", "mat", "qua", "\includegraphics[width=12pt]{unamused-face_1f612.png}\includegraphics[width=12pt]{unamused-face_1f612.png}", \textless{}/s\textgreater{}\end{tabular} \\ \hline +\multicolumn{3}{c}{\textit{\textbf{Removing 50\% diacritics in each comment}}} \\ \hline +\multicolumn{1}{l|}{Comment} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}cai con do chơi do mua o đâu nhỉ . cười đéo nhặt duoc \\mom . \includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\end{tabular}} & + Ôi bo cai lu thanh niên hãm lol. Dep mặt quá \includegraphics[width=12pt]{unamused-face_1f612.png}\includegraphics[width=12pt]{unamused-face_1f612.png} \\ \hline +\multicolumn{1}{l|}{PhoBERT} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "c a i", "c o n", "d o", "c h ơ i", "d o", "m u a", "o", \\"đ â u", "n h ỉ", ".", "c ư ờ i", "đ @ @", "é o", "n h ặ t", \\"d u @ @", "o c", "m o m", ".", \textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \textless{}/s\textgreater{}\end{tabular}} & + \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "Ô i", "b o", "c a i", "l u", "t h a n h \_ n i ê n", "h ã m", \\"l o @ @", "l", ".", "D e @ @", "p", "m ặ t", "q u á", \textless{}unk\textgreater{}, \\\textless{}unk\textgreater{}, \textless{}/s\textgreater{}\end{tabular} \\ \hline +\multicolumn{1}{l|}{TwHIN-BERT} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "cai", "con", "do", "chơi", "do", "mua", "o", "đâu", "nhỉ", \\"", ".", "cười", "đ", "é", "o", "nh", "ặt", "du", "oc", "mom", \\"", ".", "", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", \textless{}/s\textgreater{}\end{tabular}} & + \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "Ô", "i", "bo", "cai", "lu", "thanh", "niên", "", "hã", "m", \\"lol", ".", "De", "p", "mặt", "quá", "", "\includegraphics[width=12pt]{unamused-face_1f612.png}", "\includegraphics[width=12pt]{unamused-face_1f612.png}", \textless{}/s\textgreater{}\end{tabular} \\ \hline +\multicolumn{1}{l|}{ViSoBERT} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "cai", "con", "do", "chơi", "do", "mua", "o", "đâu", \\"nhỉ", ".", "cười", "đéo", "nh", "ặt", "duoc", "m", "om", \\".", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", \textless{}/s\textgreater{}\end{tabular}} & + \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "Ôi", "bo", "cai", "lu", "thanh", "n", "iên", "hã", "m", \\"lol", ".", "D", "ep", "mặt", "quá", "\includegraphics[width=12pt]{unamused-face_1f612.png}\includegraphics[width=12pt]{unamused-face_1f612.png}", \textless{}/s\textgreater{}\end{tabular} \\ \hline +\multicolumn{3}{c}{\textit{\textbf{Removing 25\% diacritics in each comment}}} \\ \hline +\multicolumn{1}{l|}{Comment} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}cai con do chơi đó mua ở đâu nhỉ . cười đéo nhặt duoc \\mồm . \includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\end{tabular}} & + Ôi bo cai lu thanh niên hãm lol. Đep mặt quá \includegraphics[width=12pt]{unamused-face_1f612.png}\includegraphics[width=12pt]{unamused-face_1f612.png} \\ \hline +\multicolumn{1}{l|}{PhoBERT} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "c a i", "c o n", "d o", "c h ơ i", "đ ó", "m u a", "ở", \\"đ â u", "n h ỉ", ".", "c ư ờ i", "đ @ @", "é o", "n h ặ t", \\"d u @ @", "o c", "m ồ m", ".", \textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \textless{}unk\textgreater{}, \textless{}/s\textgreater{}\end{tabular}} & + \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "Ô i", "b o", "c a i", "l u", "t h a n h \_ n i ê n", "h ã m", \\"l o @ @", "l", ".", "Đ e p \_ @ @", "m ặ t", "q u á", \textless{}unk\textgreater{}, \\\textless{}unk\textgreater{}, \textless{}/s\textgreater{}\end{tabular} \\ \hline +\multicolumn{1}{l|}{TwHIN-BERT} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "cai", "con", "do", "chơi", "do", "mua", "o", "đâu", \\"nhỉ", "", ".", "cười", "đ", "é", "o", "nh", "ặt", "du", "oc", \\"mom", "", ".", "", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", "\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", \textless{}/s\textgreater{}\end{tabular}} & + \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "Ô", "i", "bo", "cai", "lu", "thanh", "niên", "", "hã", "m", \\"lol", ".", "Đep", "mặt", "quá", "", "\includegraphics[width=12pt]{unamused-face_1f612.png}", "\includegraphics[width=12pt]{unamused-face_1f612.png}", \textless{}/s\textgreater{}\end{tabular} \\ \hline +\multicolumn{1}{l|}{ViSoBERT} & + \multicolumn{1}{l|}{\begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "cai", "con", "do", "chơi", "đó", "mua", "ở", "đâu", \\"nhỉ", ".", "cười", "đéo", "nh", "ặt", "duoc", "mồm", ".", \\"\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}\includegraphics[width=12pt]{face-with-tears-of-joy_1f602.png}", \textless{}/s\textgreater{}\end{tabular}} & + \begin{tabular}[c]{@{}l@{}}\textless{}s\textgreater{}, "Ôi", "bo", "cai", "lu", "thanh", "n", "iên", "hã", "m", \\"lol", ".", "Đep", "mặt", "quá", "\includegraphics[width=12pt]{unamused-face_1f612.png}\includegraphics[width=12pt]{unamused-face_1f612.png}", \textless{}/s\textgreater{}\end{tabular} \\ \hline +\end{tabular}% +} +\caption{Actual social comments and their tokenizations with the tokenizers of the three pre-trained language models, including PhoBERT, TwHIN-BERT, and ViSoBERT, on removing diacritics of social comments.} +\label{tab:diacriticscomments} +\end{table} + +% \section{aaaa} +% \begin{figure}[ht] +% \centering +% \includegraphics[width=\textwidth]{emnlp2023-latex/Figures/f1_macro_masked_rate (2).png} +% \caption{Impact of masking rate on our pre-trained ViSoBERT in terms of MF1.} +% \label{maskingrate} +% \end{figure} + +% \section{Detailed Discussion Results}\label{appendix:detailed} +% \subsection{Impact of masking rate}\label{appendix:masking} +% \subsection{Vocabulary efficiency}\label{appendix:vocab} +% \subsection{Impact of Emoji}\label{appendix:emoji} +% \subsection{Impact of Teencode}\label{appendix:teencode} + + + +\end{document} diff --git a/references/2023.arxiv.nguyen/source/f1_macro_masked_rate1111-crop.pdf b/references/2023.arxiv.nguyen/source/f1_macro_masked_rate1111-crop.pdf new file mode 100644 index 0000000000000000000000000000000000000000..251f9a565b481bc866cb2c03486350926797e59e --- /dev/null +++ b/references/2023.arxiv.nguyen/source/f1_macro_masked_rate1111-crop.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:75c3c42f0cad330e37d0306850cd1454d18ee454fb3b0e389180ad6c2d7e7465 +size 22157 diff --git a/references/2023.arxiv.nguyen/source/face-with-tears-of-joy_1f602.png 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b/references/2023.arxiv.nguyen/source/vsmec.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:72672b13821dd75123d171ec5e3cc65b9aea643ef1c7debe96a464371aa4b3ee +size 12361 diff --git a/references/2023.emnlp.kiet/paper.md b/references/2023.emnlp.kiet/paper.md new file mode 100644 index 0000000000000000000000000000000000000000..01531a45e5478c700b9acf4f9487d0c8fa3f2cd4 --- /dev/null +++ b/references/2023.emnlp.kiet/paper.md @@ -0,0 +1,15 @@ +--- +title: "{V" +authors: + - "Nguyen, Nam and + Phan, Thang and + Nguyen, Duc-Vu and + Nguyen, Kiet" +year: 2023 +venue: "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing" +url: "https://aclanthology.org/2023.emnlp-main.315" +--- + +# {V + +*Full text available in paper.pdf* diff --git a/references/2023.emnlp.kiet/paper.pdf b/references/2023.emnlp.kiet/paper.pdf new file mode 100644 index 0000000000000000000000000000000000000000..30e5c3508fe6274ef34d94250949712e439dba21 --- /dev/null +++ b/references/2023.emnlp.kiet/paper.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:72a802681a6f4a83fb909a4779c67e8700a20ad6acd0e4048ea91776b33b08a1 +size 521767 diff --git a/references/fetch_papers.py b/references/fetch_papers.py new file mode 100644 index 0000000000000000000000000000000000000000..4d3b5de684eb6b636bcc9a94b4d4e9a13d824a7c --- /dev/null +++ b/references/fetch_papers.py @@ -0,0 +1,384 @@ +""" +Fetch papers from arXiv using only stdlib + requests. +Downloads PDF and generates paper.md with YAML front matter. +""" +import os +import re +import sys +import json +import time +import unicodedata +import xml.etree.ElementTree as ET +import requests + +REFS_DIR = os.path.join(os.path.dirname(__file__)) + + +def normalize_name(name): + parts = name.strip().split() + lastname = parts[-1] if parts else name + normalized = unicodedata.normalize('NFD', lastname) + return ''.join(c for c in normalized if unicodedata.category(c) != 'Mn').lower() + + +def fetch_arxiv_metadata(arxiv_id): + """Fetch metadata from arXiv API.""" + url = f"http://export.arxiv.org/api/query?id_list={arxiv_id}" + resp = requests.get(url, timeout=30) + resp.raise_for_status() + + ns = {'atom': 'http://www.w3.org/2005/Atom', 'arxiv': 'http://arxiv.org/schemas/atom'} + root = ET.fromstring(resp.text) + entry = root.find('atom:entry', ns) + if entry is None: + return None + + title = entry.find('atom:title', ns).text.strip().replace('\n', ' ') + title = re.sub(r'\s+', ' ', title) + + authors = [] + for a in entry.findall('atom:author', ns): + name = a.find('atom:name', ns).text.strip() + authors.append(name) + + published = entry.find('atom:published', ns).text + year = int(published[:4]) + + summary = entry.find('atom:summary', ns).text.strip() + + return { + 'title': title, + 'authors': authors, + 'year': year, + 'abstract': summary, + 'arxiv_id': arxiv_id, + 'url': f'https://arxiv.org/abs/{arxiv_id}' + } + + +def download_pdf(arxiv_id, dest_path): + """Download PDF from arXiv.""" + url = f"https://arxiv.org/pdf/{arxiv_id}.pdf" + resp = requests.get(url, timeout=60, allow_redirects=True) + resp.raise_for_status() + with open(dest_path, 'wb') as f: + f.write(resp.content) + return True + + +def download_source(arxiv_id, folder): + """Try to download LaTeX source from arXiv.""" + import tarfile + import gzip + from io import BytesIO + + url = f"https://arxiv.org/e-print/{arxiv_id}" + try: + resp = requests.get(url, timeout=60, allow_redirects=True) + resp.raise_for_status() + content = resp.content + + # Try tar.gz + try: + with tarfile.open(fileobj=BytesIO(content), mode='r:gz') as tar: + tex_files = [m.name for m in tar.getmembers() if m.name.endswith('.tex')] + source_dir = os.path.join(folder, 'source') + os.makedirs(source_dir, exist_ok=True) + tar.extractall(path=source_dir) + + main_tex = None + for name in tex_files: + if 'main' in name.lower(): + main_tex = name + break + if not main_tex and tex_files: + main_tex = tex_files[0] + + if main_tex: + with open(os.path.join(source_dir, main_tex), 'r', errors='ignore') as f: + tex_content = f.read() + # Save paper.tex + with open(os.path.join(folder, 'paper.tex'), 'w') as f: + f.write(tex_content) + return tex_content + except tarfile.TarError: + pass + + # Try plain gzip + try: + tex_content = gzip.decompress(content).decode('utf-8', errors='ignore') + if '\\documentclass' in tex_content or '\\begin{document}' in tex_content: + with open(os.path.join(folder, 'paper.tex'), 'w') as f: + f.write(tex_content) + return tex_content + except: + pass + + except Exception as e: + print(f" Source download failed: {e}") + return None + + +def tex_to_md(tex_content): + """Basic LaTeX to Markdown conversion.""" + md = tex_content + + doc_match = re.search(r'\\begin\{document\}', md) + if doc_match: + md = md[doc_match.end():] + md = re.sub(r'\\end\{document\}.*', '', md, flags=re.DOTALL) + md = re.sub(r'%.*$', '', md, flags=re.MULTILINE) + md = re.sub(r'\\section\*?\{([^}]+)\}', r'# \1', md) + md = re.sub(r'\\subsection\*?\{([^}]+)\}', r'## \1', md) + md = re.sub(r'\\subsubsection\*?\{([^}]+)\}', r'### \1', md) + md = re.sub(r'\\textbf\{([^}]+)\}', r'**\1**', md) + md = re.sub(r'\\textit\{([^}]+)\}', r'*\1*', md) + md = re.sub(r'\\emph\{([^}]+)\}', r'*\1*', md) + md = re.sub(r'\\texttt\{([^}]+)\}', r'`\1`', md) + md = re.sub(r'\\cite\w*\{([^}]+)\}', r'[\1]', md) + md = re.sub(r'\\url\{([^}]+)\}', r'\1', md) + md = re.sub(r'\\href\{([^}]+)\}\{([^}]+)\}', r'[\2](\1)', md) + md = re.sub(r'\\begin\{itemize\}', '', md) + md = re.sub(r'\\end\{itemize\}', '', md) + md = re.sub(r'\\begin\{enumerate\}', '', md) + md = re.sub(r'\\end\{enumerate\}', '', md) + md = re.sub(r'\\item\s*', '- ', md) + md = re.sub(r'\n{3,}', '\n\n', md) + return md.strip() + + +def fetch_paper(arxiv_id): + """Fetch a single paper from arXiv.""" + arxiv_id = re.sub(r'^(arxiv:|https?://arxiv\.org/(abs|pdf)/)', '', arxiv_id) + arxiv_id = arxiv_id.rstrip('.pdf').rstrip('/') + + print(f"\n{'='*60}") + print(f"Fetching arXiv:{arxiv_id}") + print(f"{'='*60}") + + # 1. Get metadata + meta = fetch_arxiv_metadata(arxiv_id) + if not meta: + print(f" ERROR: Paper not found: {arxiv_id}") + return None + + print(f" Title: {meta['title'][:70]}...") + print(f" Authors: {', '.join(meta['authors'][:3])}") + print(f" Year: {meta['year']}") + + # 2. Create folder + author = normalize_name(meta['authors'][0]) if meta['authors'] else 'unknown' + folder_name = f"{meta['year']}.arxiv.{author}" + folder = os.path.join(REFS_DIR, folder_name) + os.makedirs(folder, exist_ok=True) + print(f" Folder: {folder_name}/") + + # 3. Build front matter + authors_yaml = '\n'.join(f' - "{a}"' for a in meta['authors']) + front_matter = f'''--- +title: "{meta['title']}" +authors: +{authors_yaml} +year: {meta['year']} +venue: "arXiv" +url: "{meta['url']}" +arxiv: "{arxiv_id}" +--- + +''' + + # 4. Download PDF + pdf_path = os.path.join(folder, 'paper.pdf') + if not os.path.exists(pdf_path): + print(f" Downloading PDF...") + download_pdf(arxiv_id, pdf_path) + print(f" Saved: paper.pdf") + else: + print(f" PDF already exists") + + # 5. Try to get LaTeX source + md_path = os.path.join(folder, 'paper.md') + if not os.path.exists(md_path): + print(f" Downloading LaTeX source...") + tex_content = download_source(arxiv_id, folder) + + if tex_content: + md_text = tex_to_md(tex_content) + with open(md_path, 'w') as f: + f.write(front_matter + md_text) + print(f" Generated: paper.md (from LaTeX)") + else: + # Fallback: write front matter + abstract + with open(md_path, 'w') as f: + f.write(front_matter) + f.write(f"# {meta['title']}\n\n") + f.write(f"## Abstract\n\n{meta['abstract']}\n\n") + f.write(f"*Full text available in paper.pdf*\n") + print(f" Generated: paper.md (metadata + abstract only)") + + else: + print(f" paper.md already exists") + + # 6. Update paper_db.json + update_paper_db(folder_name, meta) + + print(f" Done: {folder_name}/") + return folder_name + + +def fetch_acl(acl_id): + """Fetch a paper from ACL Anthology.""" + acl_id = re.sub(r'^https?://aclanthology\.org/', '', acl_id) + acl_id = acl_id.rstrip('/').rstrip('.pdf') + + print(f"\n{'='*60}") + print(f"Fetching ACL:{acl_id}") + print(f"{'='*60}") + + # Get BibTeX for metadata + bib_url = f"https://aclanthology.org/{acl_id}.bib" + try: + resp = requests.get(bib_url, timeout=15) + bib_text = resp.text + + title_match = re.search(r'title\s*=\s*["{]([^"}]+)', bib_text) + title = title_match.group(1) if title_match else acl_id + + author_match = re.search(r'author\s*=\s*["{]([^"}]+)', bib_text) + authors = [] + if author_match: + authors = [a.strip() for a in author_match.group(1).split(' and ')] + + year_match = re.search(r'year\s*=\s*["{]?(\d{4})', bib_text) + year = int(year_match.group(1)) if year_match else 2020 + + venue_match = re.search(r'booktitle\s*=\s*["{]([^"}]+)', bib_text) + venue = venue_match.group(1) if venue_match else "ACL" + except: + title = acl_id + authors = ["unknown"] + year = 2020 + venue = "ACL" + + print(f" Title: {title[:70]}...") + print(f" Authors: {', '.join(authors[:3])}") + + # Parse venue from ID + venue_short = "acl" + m = re.match(r'(\d{4})\.([a-z\-]+)', acl_id) + if m: + venue_short = m.group(2).split('-')[0] + else: + prefix_map = {'P': 'acl', 'N': 'naacl', 'E': 'eacl', 'D': 'emnlp', 'C': 'coling', 'W': 'workshop'} + m2 = re.match(r'([A-Z])(\d{2})', acl_id) + if m2: + venue_short = prefix_map.get(m2.group(1), 'acl') + + author_name = normalize_name(authors[0]) if authors else 'unknown' + folder_name = f"{year}.{venue_short}.{author_name}" + folder = os.path.join(REFS_DIR, folder_name) + os.makedirs(folder, exist_ok=True) + print(f" Folder: {folder_name}/") + + # Download PDF + pdf_path = os.path.join(folder, 'paper.pdf') + if not os.path.exists(pdf_path): + pdf_url = f"https://aclanthology.org/{acl_id}.pdf" + print(f" Downloading PDF...") + resp = requests.get(pdf_url, timeout=60, allow_redirects=True) + resp.raise_for_status() + with open(pdf_path, 'wb') as f: + f.write(resp.content) + print(f" Saved: paper.pdf") + + # Create paper.md with metadata + md_path = os.path.join(folder, 'paper.md') + if not os.path.exists(md_path): + authors_yaml = '\n'.join(f' - "{a}"' for a in authors) + front_matter = f'''--- +title: "{title}" +authors: +{authors_yaml} +year: {year} +venue: "{venue}" +url: "https://aclanthology.org/{acl_id}" +--- + +''' + with open(md_path, 'w') as f: + f.write(front_matter) + f.write(f"# {title}\n\n") + f.write(f"*Full text available in paper.pdf*\n") + print(f" Generated: paper.md") + + meta = { + 'title': title, + 'authors': authors, + 'year': year, + 'abstract': '', + 'arxiv_id': None, + 'url': f'https://aclanthology.org/{acl_id}' + } + update_paper_db(folder_name, meta) + + print(f" Done: {folder_name}/") + return folder_name + + +def update_paper_db(folder_name, meta): + """Update paper_db.json with new paper entry.""" + db_path = os.path.join(REFS_DIR, 'paper_db.json') + + if os.path.exists(db_path): + with open(db_path, 'r') as f: + db = json.load(f) + else: + db = {"papers": {}, "s2_cache": {}} + + db['papers'][folder_name] = { + "id": folder_name, + "title": meta['title'], + "authors": meta['authors'], + "year": meta['year'], + "venue": "arXiv" if meta.get('arxiv_id') else "ACL", + "url": meta['url'], + "arxiv_id": meta.get('arxiv_id'), + "s2_id": None, + "doi": None, + "citation_count": 0, + "abstract": meta.get('abstract', ''), + "tldr": "", + "keywords": [], + "references": [], + "cited_by": [], + "local_path": f"references/{folder_name}", + "fetched": True + } + + with open(db_path, 'w') as f: + json.dump(db, f, indent=2, ensure_ascii=False) + + +if __name__ == "__main__": + if len(sys.argv) < 2: + print("Usage: python3 fetch_papers.py [...]") + sys.exit(1) + + for paper_id in sys.argv[1:]: + paper_id = paper_id.strip() + if not paper_id: + continue + + try: + # Detect if arXiv or ACL + if re.match(r'^\d{4}\.\d{4,5}', paper_id) or paper_id.startswith('arxiv:'): + fetch_paper(paper_id) + elif re.match(r'^[A-Z]\d{2}-\d+$', paper_id) or re.match(r'^\d{4}\.[a-z]', paper_id): + fetch_acl(paper_id) + else: + # Default: try arXiv + fetch_paper(paper_id) + + time.sleep(3) # Rate limiting + except Exception as e: + print(f" ERROR fetching {paper_id}: {e}") diff --git a/references/how_to_do_science.md b/references/how_to_do_science.md new file mode 100644 index 0000000000000000000000000000000000000000..3043d764b94eb5de841c4ca4c437081d63a614db --- /dev/null +++ b/references/how_to_do_science.md @@ -0,0 +1,194 @@ +# How to Do Science: A Research Guide for NLP/ML + +Tổng hợp từ các nguồn kinh điển về phương pháp nghiên cứu khoa học. + +--- + +## 1. Chọn vấn đề quan trọng (Choosing Important Problems) + +> "If you do not work on an important problem, it's unlikely you'll do important work." +> — Richard Hamming + +### Hamming's Principles + +- Luôn duy trì danh sách **10-20 bài toán quan trọng** trong lĩnh vực của mình +- Một bài toán trở nên quan trọng khi bạn có **reasonable attack** (hướng giải quyết khả thi) +- Dành thời gian suy nghĩ sâu (Hamming dành Friday afternoons cho "Great Thoughts Time") +- **Dũng cảm** theo đuổi ý tưởng khác biệt - Shannon dám hỏi "what would the average random code do?" + +### John Schulman's Framework (OpenAI) + +Ba chìa khóa thành công: +1. **Work on the right problems** - chọn đúng bài toán +2. **Make continual progress** - tiến bộ liên tục +3. **Achieve continual personal growth** - phát triển bản thân không ngừng + +Cách phát triển "research taste": +- Đọc rộng, không chỉ trong lĩnh vực hẹp +- Làm việc với nhiều cộng tác viên để mở rộng góc nhìn +- Thường xuyên tự hỏi: "Nếu thành công, impact sẽ lớn đến mức nào?" + +### Microsoft Research Asia (Dr. Ming Zhou) + +Khi chọn đề tài nghiên cứu: +1. Đọc proceedings **ACL gần đây** để tìm lĩnh vực yêu thích +2. Ưu tiên **"blue ocean"** - lĩnh vực mới, ít cạnh tranh, dễ tạo kết quả +3. Kiểm tra 3 điều kiện tiên quyết: + - Có **framework toán học/ML** rõ ràng + - Có **dataset chuẩn** được công nhận + - Có **nhóm nghiên cứu** uy tín đang hoạt động +4. Tìm **gaps**: khía cạnh có thể cải thiện, kết hợp, hoặc đảo ngược + +--- + +## 2. Đọc papers (Reading & Literature Review) + +### Cách đọc hiệu quả + +- **Đọc rộng**: Không chỉ NLP mà cả cognitive science, neuroscience, linguistics, vision +- **Đọc sâu**: Trở thành "world-leading expert" trên câu hỏi nghiên cứu cụ thể +- **Đọc textbooks**: Textbooks là cách hấp thụ kiến thức dày đặc hơn papers +- **Follow citation chains**: Dùng Google Scholar để theo dõi citations + +### Systematic Literature Review (PRISMA) + +1. **Định nghĩa research questions** (PICO framework) +2. **Search** trên Semantic Scholar, ACL Anthology, arXiv +3. **Screen** theo inclusion/exclusion criteria +4. **Extract** thông tin và so sánh +5. **Synthesize** theo chủ đề, không theo thời gian +6. **Document** findings + +--- + +## 3. Thực nghiệm (Running Experiments) + +### Bước 1: Reproduce baselines trước + +> "Reimplement existing state-of-the-art work first to validate your setup." +> — Marek Rei + +- Chọn open source project, compile, chạy demo, **đạt cùng kết quả** +- Hiểu sâu thuật toán, implement lại +- Test trên **standard test set** cho đến khi match + +### Bước 2: Simple baseline nhanh (1-2 tuần) + +- Implement baseline đơn giản trước khi xây architecture phức tạp +- Verify setup hoạt động đúng + +### Bước 3: Chạy thực nghiệm + +**Debug rigorously:** +- **Đừng giả định code không có bug** - test với toy examples nhỏ +- Thêm assertions kiểm tra data shapes, output ranges +- Manually inspect model outputs +- Verify model có thể memorize small dataset (overfit test) + +**Evaluation best practices:** +- Dùng **train/dev/test split** riêng biệt, không sửa test set +- Chạy mỗi experiment **nhiều lần** (>=10) vì randomness +- Report **mean + standard deviation** +- Thực hiện **significance tests** và **ablation studies** +- So sánh với **published baselines** đã biết + +**Common mistakes to avoid:** +- Report kết quả 1 lần chạy duy nhất +- Chỉ so sánh với weak baselines +- Dùng hết compute budget khi code còn bugs +- Áp dụng technique trending mà không hiểu purpose + +--- + +## 4. Viết paper (Writing) + +### Cấu trúc ACL paper (Dr. Ming Zhou) + +1. **Title**: Cụ thể, tránh từ generic như "system", "research" +2. **Abstract**: Problem + Method + Advantage + Achievement level +3. **Introduction**: Background → existing approaches → limitations → your contribution (<=3 novel elements) +4. **Related Work**: Cover 3 phương pháp quan trọng nhất, tổ chức theo **theme** không theo thời gian +5. **Methodology**: Problem definition → notation → formulas → derivations +6. **Experiments**: Purpose → methodology → data → scale → parameters → reproducibility +7. **Results**: So sánh với existing work, giải thích advantages/disadvantages +8. **Conclusion**: Tóm tắt contributions + future work +9. **Limitations**: (Required by ACL) - honest assessment + +### Quy trình revision (3 lần) + +1. **Lần 1**: Tự review novelty và depth +2. **Lần 2**: Đồng nghiệp trong team review +3. **Lần 3**: Người ngoài team review readability + +### Tips from Marek Rei + +- **Bắt đầu viết sớm** - đừng đánh giá thấp effort cần thiết +- Proofread kỹ - sloppy writing tạo ấn tượng xấu +- Treat rejections as learning opportunities +- Follow submission requirements strictly + +--- + +## 5. Mindset & Habits + +### Hamming's Lessons + +| Principle | Lesson | +|-----------|--------| +| **Open doors** | Giữ "cửa mở" - tham gia cộng đồng, biết problems đang nổi lên | +| **Preparation** | "Luck favors the prepared mind" (Pasteur) | +| **Constraints as assets** | Điều kiện khó khăn thường dẫn đến breakthroughs | +| **Emotional commitment** | Chìm đắm trong problem → subconscious sẽ giải quyết | +| **Selling your work** | Presentation matters - great work cần được communicate hiệu quả | + +### Schulman's Tips + +- **Read textbooks**, not just papers +- **Work through exercises** - deep understanding comes from practice +- **Keep a research notebook** - track ideas, experiments, insights +- **Don't be afraid to ask dumb questions** - they often lead to insights +- **Alternate between exploration and exploitation** - explore new ideas, then exploit promising ones deeply + +### Practical Daily Habits + +- Duy trì weekly meeting với advisor/team +- Version control tất cả code +- Backup thường xuyên +- Participate in reading groups +- Nói chuyện với students khác - cả trong và ngoài lab + +--- + +## 6. Áp dụng cho Sen-1 + +Dựa trên các nguyên tắc trên, research plan cho Sen-1: + +| Principle | Application | +|-----------|-------------| +| Important problem | Vietnamese text classification - practical, resource-constrained NLP | +| Reproduce first | Done: reproduced sonar_core_1 (92.49% vs 92.80%) | +| Simple baseline | Done: TF-IDF + SVM baseline established | +| Blue ocean | Lightweight models for Vietnamese - under-explored vs transformer focus | +| Standard datasets | VNTC, UTS2017_Bank, UIT-VSMEC, UIT-VSFC | +| Ablation studies | Phase 2C: vocab size, n-gram range, classifier comparison | +| Multiple runs | Need to add: multiple random seeds, report mean+std | +| Compare with SOTA | Phase 2A: fine-tune PhoBERT for direct comparison | +| Write early | TECHNICAL_REPORT.md already started | + +--- + +## Essential Reading List + +| Resource | Author | Focus | +|----------|--------|-------| +| [You and Your Research](https://www.cs.virginia.edu/~robins/YouAndYourResearch.html) | Richard Hamming | Choosing important problems, mindset | +| [An Opinionated Guide to ML Research](http://joschu.net/blog/opinionated-guide-ml-research.html) | John Schulman | Problem selection, progress, growth | +| [ML/NLP Research Project Advice](https://www.marekrei.com/blog/ml-nlp-research-project-advice/) | Marek Rei | Practical experiment workflow | +| [How to Make First Accomplishment in NLP](https://microsoft.com/en-us/research/lab/microsoft-research-asia/articles/make-first-accomplishment-nlp-field/) | Dr. Ming Zhou (MSRA) | NLP-specific research methodology | +| [How to Be a Successful PhD Student](https://people.cs.umass.edu/~wallach/how_to_be_a_successful_phd_student.pdf) | Hanna Wallach | CS/NLP PhD advice | +| [ACL Rolling Review Guidelines](https://aclrollingreview.org/authors) | ACL | Paper submission standards | +| [Towards Reproducible ML Research](https://aclanthology.org/2022.acl-tutorials.2.pdf) | ACL 2022 Tutorial | Reproducibility best practices | + +--- + +*Compiled: 2026-02-05* diff --git a/references/index.html b/references/index.html new file mode 100644 index 0000000000000000000000000000000000000000..797165d454f0c742af455ae8e55df007a57444d6 --- /dev/null +++ b/references/index.html @@ -0,0 +1,1273 @@ + + + + + +Sen-1 References - Vietnamese Text Classification + + + + +
+
+ +
+
12 papers
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10 PDFs
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7 LaTeX
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+
+
+ +
+
Papers
+
Benchmarks
+
SOTA
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Citation Network
+
Leaderboard
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How to Do Science
+
+ + +
+
+
Paper Database
+
Research papers related to Vietnamese text classification, fetched from arXiv and ACL Anthology.
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+ + + + + +
+ +
+ + +
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+ PhoBERT: Pre-trained language models for Vietnamese +
+
+ 2020 + EMNLP Findings + PDF + TEX + MD + Dat Quoc Nguyen, Anh Tuan Nguyen +
+
We present PhoBERT with two versions, PhoBERT-base and PhoBERT-large, the first public large-scale monolingual language models pre-trained for Vietnamese. Experimental results show that PhoBERT consistently outperforms the recent best pre-trained multilingual model XLM-R and improves the state-of-the-art in multiple Vietnamese-specific NLP tasks including Part-of-speech tagging, Dependency parsing, Named-entity recognition and Natural language inference.
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+ 2020.arxiv.nguyen/ · 2020.findings.anh/ +
+
+ + +
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+ ViSoBERT: A Pre-Trained Language Model for Vietnamese Social Media Text Processing +
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+ 2023 + EMNLP 2023 + PDF + TEX + MD + Quoc-Nam Nguyen, Thang Chau Phan, Duc-Vu Nguyen, Kiet Van Nguyen +
+
We present the first monolingual pre-trained language model for Vietnamese social media texts, ViSoBERT, which is pre-trained on a large-scale corpus of high-quality and diverse Vietnamese social media texts using XLM-R architecture. ViSoBERT surpasses the previous state-of-the-art models on multiple Vietnamese social media tasks with far fewer parameters.
+
+ 2023.arxiv.nguyen/ · 2023.emnlp.kiet/ +
+
+ + +
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+ Improving Sequence Tagging for Vietnamese Text Using Transformer-based Neural Models +
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+ 2020 + arXiv + PDF + TEX + MD + Viet Bui The, Oanh Tran Thi, Phuong Le-Hong +
+
Introduces viBERT (trained on 10GB) and vELECTRA (trained on 60GB) Vietnamese pretrained models. Strong performance on sequence tagging and text classification tasks. vELECTRA achieves 95.26% on ViOCD complaint classification in the SMTCE benchmark.
+
2020.arxiv.the/
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+ + +
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+ SMTCE: A Social Media Text Classification Evaluation Benchmark and BERTology Models for Vietnamese +
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+ 2022 + PACLIC 2022 + PDF + TEX + MD + Luan Thanh Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen +
+
GLUE-inspired benchmark for Vietnamese social media text classification. Compares multilingual (mBERT, XLM-R, DistilmBERT) and monolingual (PhoBERT, viBERT, vELECTRA, viBERT4news) BERT models. Monolingual models consistently outperform multilingual for Vietnamese.
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+ 2022.arxiv.nguyen/ · 2022.paclic.ngan/ +
+
+ + +
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+ A Comparative Study on Vietnamese Text Classification Methods +
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+ 2007 + IEEE RIVF + MD + Cong Duy Vu Hoang, Dien Dinh, Le Nguyen Nguyen, Quoc Hung Ngo +
+
Seminal paper introducing VNTC corpus and comparing BOW and N-gram language model approaches for Vietnamese text classification. N-gram LM achieves 97.1% accuracy, SVM Multi achieves 93.4% on 10-topic news classification. The VNTC dataset remains the standard benchmark.
+
2007.rivf.hoang/
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+ + +
+
+ RoBERTa: A Robustly Optimized BERT Pretraining Approach +
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+ 2019 + arXiv + PDF + TEX + MD + Yinhan Liu, Myle Ott, Naman Goyal, ... +
+
PhoBERT is based on the RoBERTa architecture. Key optimizations over BERT: dynamic masking, larger batches, more training data, removal of Next Sentence Prediction (NSP). Foundation for most Vietnamese pretrained models.
+
2019.arxiv.liu/
+
+ + +
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+ Unsupervised Cross-lingual Representation Learning at Scale (XLM-RoBERTa) +
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+ 2019 + ACL 2020 + PDF + TEX + MD + Alexis Conneau, Kartikay Khandelwal, Naman Goyal, ... +
+
Multilingual pretrained model trained on 100 languages (2.5TB CC-100). Strong multilingual baseline for Vietnamese, but consistently outperformed by monolingual PhoBERT on Vietnamese-specific tasks.
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2019.arxiv.conneau/
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+ + +
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+ Emotion Recognition for Vietnamese Social Media Text (UIT-VSMEC) +
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+ 2019 + CSoNet 2020 + PDF + TEX + MD + Vong Anh Ho, Duong Huynh-Cong Nguyen, Danh Hoang Nguyen, ... +
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Introduces UIT-VSMEC corpus: 6,927 emotion-annotated Vietnamese social media sentences with 7 labels (sadness, enjoyment, anger, disgust, fear, surprise, other). CNN baseline achieves 59.74% weighted F1.
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2019.arxiv.ho/
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+ + +
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+ UIT-VSFC: Vietnamese Students' Feedback Corpus for Sentiment Analysis +
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+ 2018 + KSE 2018 + MD + Kiet Van Nguyen, Vu Duc Nguyen, Phu Xuan-Vinh Nguyen, ... +
+
16,175 Vietnamese student feedback sentences annotated for sentiment (3 classes: positive, negative, neutral) and topic classification. Inter-annotator agreement: 91.20% for sentiment. MaxEnt baseline: 88% sentiment F1.
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2018.kse.nguyen/
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+ + +
+
Benchmark Comparison
+
Vietnamese text classification results across datasets and models.
+ +

VNTC Dataset (10-topic News Classification)

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ModelYearAccuracyF1 (weighted)TrainingInferenceSize
N-gram LM (Vu et al.)200797.1%-~79 min--
SVM Multi (Vu et al.)200793.4%-~79 min--
sonar_core_1 (SVC)-92.80%92.0%~54.6 min-~75MB
Sen-1 (LinearSVC)202692.49%92.40%37.6s66K/sec2.4MB
PhoBERT-base*2020~95-97%~95%Hours (GPU)~20/sec~400MB
+

*PhoBERT not directly evaluated on VNTC; estimates from similar tasks.

+ +

UTS2017_Bank Dataset (14-category Banking)

+ + + + +
ModelAccuracyF1 (weighted)F1 (macro)Training
Sen-175.76%72.70%36.18%0.13s
sonar_core_172.47%66.0%-~5.3s
+ +

Vietnamese Pretrained Models

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ModelArchitecturePre-training DataLanguagesVietnamese Tasks
PhoBERTRoBERTa20GB Vietnamese1 (vi)SOTA: POS, NER, NLI
ViSoBERTXLM-RSocial media corpus1 (vi)SOTA: social media tasks
vELECTRAELECTRA60GB Vietnamese1 (vi)Strong on classification
viBERTBERT10GB Vietnamese1 (vi)Baseline
XLM-RRoBERTaCC-100 (2.5TB)100Strong multilingual
mBERTBERTWikipedia104Weakest on Vietnamese
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SMTCE Benchmark (Best model per task)

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TaskBest ModelScoreRunner-up
UIT-VSMEC (Emotion)PhoBERT65.44% F1viBERT4news
ViOCD (Complaint)vELECTRA95.26% F1PhoBERT
ViHSD (Hate Speech)PhoBERT-XLM-R
ViCTSD (Constructive)PhoBERT-vELECTRA
UIT-VSFC (Sentiment)PhoBERT-viBERT
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Model Efficiency

+ + + + + +
ModelSizeVNTC AccuracyEfficiency (Acc/MB)
Sen-12.4 MB92.49%38.5
PhoBERT-base~400 MB~95%0.24
XLM-R-base~1.1 GB~93%0.08
+

Sen-1 is ~160x more efficient in accuracy-per-MB than PhoBERT.

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+ + +
+
State-of-the-Art
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Current SOTA for Vietnamese text classification tasks (as of 2026).
+ + + + + + + + + +
TaskDatasetSOTA ModelScorePaper
News ClassificationVNTCN-gram LM97.1% AccVu et al. 2007
Emotion RecognitionUIT-VSMECViSoBERTSOTA F1Nguyen et al. 2023
Sentiment AnalysisUIT-VSFCPhoBERTSOTA F1SMTCE 2022
Hate SpeechViHSDPhoBERT/ViSoBERTSOTA F1SMTCE/ViSoBERT
Complaint DetectionViOCDvELECTRA95.26% F1SMTCE 2022
Spam ReviewsViSpamReviewsViSoBERTSOTA F1Nguyen et al. 2023
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Key Trends

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+
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Monolingual > Multilingual

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PhoBERT, ViSoBERT, vELECTRA consistently outperform XLM-R, mBERT on Vietnamese tasks.

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+
+

Domain-specific Pretraining

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ViSoBERT (social media) outperforms PhoBERT (general) on social media tasks.

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+
+

Traditional ML Still Competitive

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TF-IDF + SVM achieves 92%+ on news classification with 160x less resources.

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+
+

Word Segmentation Matters

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~5% accuracy gap between syllable-level (Sen-1) and word-level approaches.

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+
+ +

Sen-1 Position

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Accuracy + High ^ + | PhoBERT ViSoBERT + | * * + | + | N-gram (2007) + | * + | Sen-1 + | * + | + Low | + +-------------------------------> + Fast Slow + Inference Speed
+

+ Sen-1 = fast + lightweight quadrant: edge deployment, real-time batch processing, resource-constrained environments. +

+ +

Open Questions

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+
+

RQ1

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Can word segmentation close the gap between Sen-1 and PhoBERT?

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+
+

RQ2

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How does Sen-1 perform on social media/informal text?

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+
+

RQ3

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Can ensemble (Sen-1 + lightweight transformer) get speed + accuracy?

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+
+

RQ4

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Minimum dataset size where PhoBERT outperforms TF-IDF+SVM?

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+
+
+ + +
+
Citation Network
+
How the papers in this collection relate to each other and to Sen-1.
+ +
Vu et al. 2007 (VNTC dataset) + | + +---> Vietnamese text classification research + | | + | RoBERTa (2019) ---> PhoBERT (2020) ---> ViSoBERT (2023) + | | | + | XLM-R (2019) ------> vELECTRA (2020) SMTCE benchmark (2022) + | | | + | Sen-2 (future) UIT-VSMEC, UIT-VSFC + | + +---> Sen-1 (TF-IDF + SVM baseline) + | + +---> 92.49% VNTC | 75.76% UTS2017_Bank + | + +---> Phase 2: word segmentation, PhoBERT comparison + | + +---> Phase 3: Sen-2 (PhoBERT-based)
+ +

Available Datasets

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DatasetTaskSamplesClassesDomainSource
VNTCTopic84,13210NewsGitHub
UTS2017_BankIntent1,97714BankingHuggingFace
UIT-VSMECEmotion6,9277Social mediaUIT NLP
UIT-VSFCSentiment16,1753EducationHuggingFace
SMTCEMulti-taskMultipleVariousSocial mediaarXiv
+ +

Research Gaps

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+
+

Gap 1

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No comprehensive TF-IDF vs PhoBERT comparison on same Vietnamese benchmarks with controlled experiments.

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+
+

Gap 2

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Limited edge/resource-constrained deployment studies. Most work focuses on accuracy, not efficiency.

+
+
+

Gap 3

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Class imbalance handling for Vietnamese datasets is under-explored.

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+
+

Gap 4

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Cross-domain evaluation and ablation studies for Vietnamese features are rare.

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+
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+ + +
+ +
+

Vietnamese Text Classification Leaderboard

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Comprehensive comparison of models across Vietnamese NLP benchmarks, speed, and efficiency.

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Updated: February 2026 · Inspired by Vellum LLM Leaderboard
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+ + +
Quality Benchmarks
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Top models per dataset, ranked by primary metric.
+ +
+ + +
+

News Classification

+
VNTC (10 topics, 84K samples)
+
    +
  1. 1N-gram LM97.1%
    2. +
  2. 2PhoBERT-base*~95%
    3. +
  3. 3SVM Multi93.4%
    4. +
  4. 4sonar_core_192.80%
    5. +
  5. 5Sen-192.49%
    6. +
+
+ + +
+

Banking Classification

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UTS2017_Bank (14 categories, 1.9K samples)
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    +
  1. 1Sen-175.76%
    2. +
  2. 2sonar_core_172.47%
    3. +
+
+ + +
+

Emotion Recognition

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UIT-VSMEC (7 classes, 6.9K samples)
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    +
  1. 1ViSoBERTSOTA
    2. +
  2. 2PhoBERT65.44%
    3. +
  3. 3viBERT4news-
    4. +
  4. 4CNN baseline59.74%
    5. +
+
+ + +
+

Sentiment Analysis

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UIT-VSFC (3 classes, 16K samples)
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    +
  1. 1PhoBERTSOTA
    2. +
  2. 2viBERT-
    3. +
  3. 3MaxEnt baseline88%
    4. +
+
+ + +
+

Complaint Detection

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ViOCD (SMTCE benchmark)
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  1. 1vELECTRA95.26%
    2. +
  2. 2PhoBERT-
    3. +
  3. 3XLM-R-
    4. +
+
+ + +
+

Hate Speech Detection

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ViHSD (SMTCE benchmark)
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    +
  1. 1PhoBERTSOTA
    2. +
  2. 2ViSoBERT-
    3. +
  3. 3XLM-R-
    4. +
+
+ +
+ + +
Performance Metrics
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Speed, latency, and efficiency rankings.
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+ + +
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Fastest Inference

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Batch throughput (samples/sec)
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  1. 1Sen-166,678/s
    2. +
  2. 2TF-IDF + SVM (sklearn)~50K/s
    3. +
  3. 3PhoBERT (GPU)~20/s
    4. +
+
+ + +
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Smallest Model

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Model file size
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  1. 1Sen-12.4 MB
    2. +
  2. 2sonar_core_1~75 MB
    3. +
  3. 3PhoBERT-base~400 MB
    4. +
  4. 4XLM-R-base~1.1 GB
    5. +
+
+ + +
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Most Efficient

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Accuracy per MB (VNTC)
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  1. 1Sen-138.5
    2. +
  2. 2sonar_core_11.24
    3. +
  3. 3PhoBERT-base0.24
    4. +
  4. 4XLM-R-base0.08
    5. +
+
+ + +
+

Fastest Training

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VNTC full training time
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    +
  1. 1Sen-1 (Rust)37.6s
    2. +
  2. 2TF-IDF+SVM (sklearn)~2 min
    3. +
  3. 3sonar_core_154.6 min
    4. +
  4. 4N-gram LM~79 min
    5. +
  5. 5PhoBERT fine-tuneHours
    6. +
+
+ +
+ + +
Comprehensive Comparison
+
All models with operational and benchmark metrics. Click column headers to sort.
+ +
+ Traditional ML + Vietnamese Transformer + Multilingual +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
#ModelTypeArchitectureSizeVNTC
Acc %		UTS2017
Acc %		UIT-VSMEC
F1 %		ViOCD
F1 %		TrainingInference
/sec		Eff.
Acc/MB
1
N-gram LM
TraditionalN-gram Language Modeln/a97.1n/an/an/a~79 minn/an/a
2
PhoBERT-base
TransformerRoBERTa (20GB vi)~400 MB~95n/a65.44n/aHours (GPU)~200.24
3
ViSoBERT
TransformerXLM-R (social media)~400 MBn/an/aSOTAn/aHours (GPU)n/an/a
4
vELECTRA
TransformerELECTRA (60GB vi)~400 MBn/an/an/a95.26Hours (GPU)n/an/a
5
SVM Multi
TraditionalSVM + BOWn/a93.4n/an/an/a~79 minn/an/a
6
sonar_core_1
TraditionalTF-IDF + SVC (RBF)~75 MB92.8072.47n/an/a54.6 minn/a1.24
7
Sen-1
TraditionalTF-IDF + LinearSVC (Rust)2.4 MB92.4975.76n/an/a37.6s66,67838.5
8
XLM-R-base
MultilingualRoBERTa (100 langs)~1.1 GB~93n/an/an/aHours (GPU)n/a0.08
9
mBERT
MultilingualBERT (104 langs)~700 MBn/an/an/an/aHours (GPU)n/an/a
10
viBERT
TransformerBERT (10GB vi)~400 MBn/an/an/an/aHours (GPU)n/an/a
11
viBERT4news
TransformerBERT (news domain)~400 MBn/an/an/an/aHours (GPU)n/an/a
12
MaxEnt baseline
TraditionalMaximum Entropyn/an/an/an/an/an/an/an/a
13
CNN baseline
TraditionalConvolutional NNn/an/an/a59.74n/an/an/an/a
14
DistilmBERT
MultilingualDistilBERT (multilingual)~260 MBn/an/an/an/aHours (GPU)n/an/a
+
+ +

+ * PhoBERT VNTC estimate based on similar Vietnamese classification tasks. Blank cells indicate benchmark not evaluated. +
Efficiency = VNTC Accuracy / Model Size in MB. Higher is better. +

+ +
+ + +
+
How to Do Science
+
Research methodology guide compiled from Hamming, Schulman, Marek Rei, and Microsoft Research Asia.
+ +

1. Choosing Important Problems

+ +
"If you do not work on an important problem, it's unlikely you'll do important work." -- Richard Hamming
+ +
+

Hamming's Principles

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    +
  • Maintain a list of 10-20 important problems in your field
    • +
  • A problem becomes important when you have a reasonable attack
    • +
  • Dedicate deep thinking time (Friday "Great Thoughts Time")
    • +
  • Have courage to pursue unconventional ideas
    • +
+
+ +
+

Schulman's Framework (OpenAI)

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    +
  • Work on the right problems
    • +
  • Make continual progress
    • +
  • Achieve continual personal growth
    • +
+

Develop "research taste" by reading broadly, collaborating widely, and asking: "If this succeeds, how big is the impact?"

+
+ +
+

Microsoft Research Asia (Dr. Ming Zhou)

+
    +
  • Read recent ACL proceedings to find your field
    • +
  • Target "blue ocean" areas - new fields with less competition
    • +
  • Verify 3 prerequisites: math/ML framework, standard datasets, active research teams
    • +
  • Find gaps: what can be improved, combined, or inverted
    • +
+
+ +

2. Reading Papers

+
+

Effective Reading Strategy

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    +
  • Read broadly: Not just NLP - also cognitive science, neuroscience, linguistics, vision
    • +
  • Read deeply: Become the "world-leading expert" on your narrow question
    • +
  • Read textbooks: More knowledge-dense than papers
    • +
  • Follow citation chains via Google Scholar, Semantic Scholar
    • +
  • Use PRISMA methodology for systematic reviews
    • +
+
+ +

3. Running Experiments

+ +
+

Step 1: Reproduce baselines first

+

"Reimplement existing state-of-the-art work first to validate your setup." -- Marek Rei

+
    +
  • Choose open source project, compile, run demo, match results
    • +
  • Understand the algorithm deeply, then reimplement
    • +
  • Test on standard test set until results align
    • +
+
+ +
+

Step 2: Simple baseline (1-2 weeks)

+

Implement the simplest approach before building complex architectures. Verify your setup works.

+
+ +
+

Step 3: Rigorous experimentation

+
    +
  • Debug: Don't assume bug-free code. Test with toy examples. Add assertions.
    • +
  • Evaluate: Separate train/dev/test. Run 10+ times. Report mean + std.
    • +
  • Ablate: Significance tests and ablation studies for every novel component.
    • +
  • Avoid: Single-run results, weak-only baselines, blind trend-following.
    • +
+
+ +

4. Writing Papers

+
+

ACL Paper Structure (Dr. Ming Zhou)

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    +
  • Title: Specific, no generic words
    • +
  • Abstract: Problem + Method + Advantage + Achievement
    • +
  • Introduction: Background → existing → limitations → contribution (≤3 points)
    • +
  • Related Work: Organized by theme, not chronology
    • +
  • Methodology: Problem definition → notation → formulas
    • +
  • Experiments: Purpose → data → parameters → reproducibility
    • +
  • Limitations: Required by ACL - honest assessment
    • +
+

Revision: 3 passes - self review → team review → outsider review.

+
+ +

5. Mindset & Habits

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PrincipleLesson
Open doorsStay connected to the community; know emerging problems
Preparation"Luck favors the prepared mind" (Pasteur)
ConstraintsDifficult conditions often lead to breakthroughs
CommitmentDeep immersion activates subconscious problem-solving
Selling workPresentation matters - great work needs effective communication
+ +

Essential Reading

+
+
+
You and Your Research
+
Richard Hamming · Choosing important problems, mindset
+
+
+
An Opinionated Guide to ML Research
+
John Schulman (OpenAI) · Problem selection, progress, growth
+
+
+
ML/NLP Research Project Advice
+
Marek Rei · Practical experiment workflow
+
+
+
How to Make First Accomplishment in NLP
+
Dr. Ming Zhou (MSRA) · NLP research methodology
+
+
+
+ +
+ + + + diff --git a/references/index.md b/references/index.md new file mode 100644 index 0000000000000000000000000000000000000000..bf98975d5c8d9bbf60862a1c4b400823d6ef7057 --- /dev/null +++ b/references/index.md @@ -0,0 +1,104 @@ +# Paper Database Index + +Thư mục này chứa các bài báo liên quan đến Vietnamese Text Classification. + +## Thống kê Database + +- **Tổng số bài báo**: 12 +- **Có PDF**: 10 +- **Có LaTeX source**: 7 + +## Danh sách bài báo (đã fetch) + +### Vietnamese Text Classification (Core) + +| Folder | Bài báo | Năm | Venue | Files | +|--------|---------|-----|-------|-------| +| `2007.rivf.hoang/` | A Comparative Study on Vietnamese Text Classification Methods | 2007 | IEEE RIVF | MD | +| `2022.arxiv.nguyen/` | SMTCE: Social Media Text Classification Evaluation Benchmark | 2022 | PACLIC | PDF MD TEX | +| `2022.paclic.ngan/` | SMTCE (published version) | 2022 | PACLIC | PDF MD | + +### Vietnamese Pretrained Models + +| Folder | Bài báo | Năm | Venue | Files | +|--------|---------|-----|-------|-------| +| `2020.arxiv.nguyen/` | PhoBERT: Pre-trained language models for Vietnamese | 2020 | EMNLP Findings | PDF MD TEX | +| `2020.findings.anh/` | PhoBERT (published version) | 2020 | EMNLP Findings | PDF MD | +| `2023.arxiv.nguyen/` | ViSoBERT: Vietnamese Social Media Text Processing | 2023 | EMNLP | PDF MD TEX | +| `2023.emnlp.kiet/` | ViSoBERT (published version) | 2023 | EMNLP | PDF MD | +| `2020.arxiv.the/` | vELECTRA/viBERT: Improving Sequence Tagging for Vietnamese | 2020 | arXiv | PDF MD TEX | + +### Multilingual Pretrained Models + +| Folder | Bài báo | Năm | Venue | Files | +|--------|---------|-----|-------|-------| +| `2019.arxiv.liu/` | RoBERTa: A Robustly Optimized BERT Pretraining Approach | 2019 | arXiv | PDF MD TEX | +| `2019.arxiv.conneau/` | XLM-RoBERTa: Cross-lingual Representation Learning | 2019 | ACL | PDF MD TEX | + +### Vietnamese Datasets + +| Folder | Bài báo | Năm | Venue | Files | +|--------|---------|-----|-------|-------| +| `2019.arxiv.ho/` | UIT-VSMEC: Emotion Recognition for Vietnamese Social Media | 2019 | CSoNet | PDF MD TEX | +| `2018.kse.nguyen/` | UIT-VSFC: Vietnamese Students' Feedback Corpus | 2018 | KSE | MD | + +## Phân loại theo chủ đề + +### Vietnamese Text Classification +- `2007.rivf.hoang/` - VNTC dataset & baseline (N-gram 97.1%, SVM 93.4%) +- `2022.arxiv.nguyen/` - SMTCE benchmark (GLUE-like for Vietnamese) + +### Vietnamese Pretrained Models +- `2020.arxiv.nguyen/` - PhoBERT (SOTA Vietnamese BERT) +- `2023.arxiv.nguyen/` - ViSoBERT (social media BERT, EMNLP 2023) +- `2020.arxiv.the/` - vELECTRA/viBERT + +### Multilingual Pretrained Models +- `2019.arxiv.liu/` - RoBERTa (PhoBERT foundation) +- `2019.arxiv.conneau/` - XLM-RoBERTa (multilingual baseline) + +### Vietnamese Datasets +- `2019.arxiv.ho/` - UIT-VSMEC (emotion, 6,927 samples, 7 classes) +- `2018.kse.nguyen/` - UIT-VSFC (sentiment, 16,175 samples, 3 classes) + +## Research Synthesis + +Xem `research_vietnamese_text_classification/`: +- `README.md` - Literature review +- `papers.md` - Paper database chi tiết +- `comparison.md` - Bảng so sánh benchmark +- `sota.md` - State-of-the-art summary +- `bibliography.bib` - BibTeX references + +## Citation Network + +``` +Vu et al. 2007 (VNTC) ──── Sen-1 baseline dataset + │ + └──→ Vietnamese text classification + │ +RoBERTa (2019) ──→ PhoBERT (2020) ──→ ViSoBERT (2023) + │ │ +XLM-R (2019) ────→ vELECTRA (2020) SMTCE benchmark (2022) + │ │ + Sen-2 (future) UIT-VSMEC, UIT-VSFC +``` + +## Câu lệnh + +```bash +# Fetch paper mới từ arXiv +python3 references/fetch_papers.py +python3 references/fetch_papers.py + +# Ví dụ +python3 references/fetch_papers.py 2003.00744 # PhoBERT +python3 references/fetch_papers.py 2023.emnlp-main.315 # ViSoBERT (ACL) +``` + +## Files trong mỗi paper folder + +- `paper.pdf` - PDF gốc +- `paper.md` - Markdown với YAML front matter +- `paper.tex` - LaTeX source (nếu có từ arXiv) +- `source/` - Full arXiv source files diff --git a/references/paper_db.json b/references/paper_db.json new file mode 100644 index 0000000000000000000000000000000000000000..189feb1934893fbacba889b13451df0cb7b9215c --- /dev/null +++ b/references/paper_db.json @@ -0,0 +1,313 @@ +{ + "papers": { + "2020.arxiv.nguyen": { + "id": "2020.arxiv.nguyen", + "title": "PhoBERT: Pre-trained language models for Vietnamese", + "authors": [ + "Dat Quoc Nguyen", + "Anh Tuan Nguyen" + ], + "year": 2020, + "venue": "arXiv", + "url": "https://arxiv.org/abs/2003.00744", + "arxiv_id": "2003.00744", + "s2_id": null, + "doi": null, + "citation_count": 0, + "abstract": "We present PhoBERT with two versions, PhoBERT-base and PhoBERT-large, the first public large-scale monolingual language models pre-trained for Vietnamese. Experimental results show that PhoBERT consistently outperforms the recent best pre-trained multilingual model XLM-R (Conneau et al., 2020) and improves the state-of-the-art in multiple Vietnamese-specific NLP tasks including Part-of-speech tagging, Dependency parsing, Named-entity recognition and Natural language inference. We release PhoBERT to facilitate future research and downstream applications for Vietnamese NLP. Our PhoBERT models are available at https://github.com/VinAIResearch/PhoBERT", + "tldr": "", + "keywords": [], + "references": [], + "cited_by": [], + "local_path": "references/2020.arxiv.nguyen", + "fetched": true + }, + "2023.arxiv.nguyen": { + "id": "2023.arxiv.nguyen", + "title": "ViSoBERT: A Pre-Trained Language Model for Vietnamese Social Media Text Processing", + "authors": [ + "Quoc-Nam Nguyen", + "Thang Chau Phan", + "Duc-Vu Nguyen", + "Kiet Van Nguyen" + ], + "year": 2023, + "venue": "arXiv", + "url": "https://arxiv.org/abs/2310.11166", + "arxiv_id": "2310.11166", + "s2_id": null, + "doi": null, + "citation_count": 0, + "abstract": "English and Chinese, known as resource-rich languages, have witnessed the strong development of transformer-based language models for natural language processing tasks. Although Vietnam has approximately 100M people speaking Vietnamese, several pre-trained models, e.g., PhoBERT, ViBERT, and vELECTRA, performed well on general Vietnamese NLP tasks, including POS tagging and named entity recognition. These pre-trained language models are still limited to Vietnamese social media tasks. In this paper, we present the first monolingual pre-trained language model for Vietnamese social media texts, ViSoBERT, which is pre-trained on a large-scale corpus of high-quality and diverse Vietnamese social media texts using XLM-R architecture. Moreover, we explored our pre-trained model on five important natural language downstream tasks on Vietnamese social media texts: emotion recognition, hate speech detection, sentiment analysis, spam reviews detection, and hate speech spans detection. Our experiments demonstrate that ViSoBERT, with far fewer parameters, surpasses the previous state-of-the-art models on multiple Vietnamese social media tasks. Our ViSoBERT model is available only for research purposes.", + "tldr": "", + "keywords": [], + "references": [], + "cited_by": [], + "local_path": "references/2023.arxiv.nguyen", + "fetched": true + }, + "2022.arxiv.nguyen": { + "id": "2022.arxiv.nguyen", + "title": "SMTCE: A Social Media Text Classification Evaluation Benchmark and BERTology Models for Vietnamese", + "authors": [ + "Luan Thanh Nguyen", + "Kiet Van Nguyen", + "Ngan Luu-Thuy Nguyen" + ], + "year": 2022, + "venue": "arXiv", + "url": "https://arxiv.org/abs/2209.10482", + "arxiv_id": "2209.10482", + "s2_id": null, + "doi": null, + "citation_count": 0, + "abstract": "Text classification is a typical natural language processing or computational linguistics task with various interesting applications. As the number of users on social media platforms increases, data acceleration promotes emerging studies on Social Media Text Classification (SMTC) or social media text mining on these valuable resources. In contrast to English, Vietnamese, one of the low-resource languages, is still not concentrated on and exploited thoroughly. Inspired by the success of the GLUE, we introduce the Social Media Text Classification Evaluation (SMTCE) benchmark, as a collection of datasets and models across a diverse set of SMTC tasks. With the proposed benchmark, we implement and analyze the effectiveness of a variety of multilingual BERT-based models (mBERT, XLM-R, and DistilmBERT) and monolingual BERT-based models (PhoBERT, viBERT, vELECTRA, and viBERT4news) for tasks in the SMTCE benchmark. Monolingual models outperform multilingual models and achieve state-of-the-art results on all text classification tasks. It provides an objective assessment of multilingual and monolingual BERT-based models on the benchmark, which will benefit future studies about BERTology in the Vietnamese language.", + "tldr": "", + "keywords": [], + "references": [], + "cited_by": [], + "local_path": "references/2022.arxiv.nguyen", + "fetched": true + }, + "2019.arxiv.ho": { + "id": "2019.arxiv.ho", + "title": "Emotion Recognition for Vietnamese Social Media Text", + "authors": [ + "Vong Anh Ho", + "Duong Huynh-Cong Nguyen", + "Danh Hoang Nguyen", + "Linh Thi-Van Pham", + "Duc-Vu Nguyen", + "Kiet Van Nguyen", + "Ngan Luu-Thuy Nguyen" + ], + "year": 2019, + "venue": "arXiv", + "url": "https://arxiv.org/abs/1911.09339", + "arxiv_id": "1911.09339", + "s2_id": null, + "doi": null, + "citation_count": 0, + "abstract": "Emotion recognition or emotion prediction is a higher approach or a special case of sentiment analysis. In this task, the result is not produced in terms of either polarity: positive or negative or in the form of rating (from 1 to 5) but of a more detailed level of analysis in which the results are depicted in more expressions like sadness, enjoyment, anger, disgust, fear, and surprise. Emotion recognition plays a critical role in measuring the brand value of a product by recognizing specific emotions of customers' comments. In this study, we have achieved two targets. First and foremost, we built a standard Vietnamese Social Media Emotion Corpus (UIT-VSMEC) with exactly 6,927 emotion-annotated sentences, contributing to emotion recognition research in Vietnamese which is a low-resource language in natural language processing (NLP). Secondly, we assessed and measured machine learning and deep neural network models on our UIT-VSMEC corpus. As a result, the CNN model achieved the highest performance with the weighted F1-score of 59.74%. Our corpus is available at our research website.", + "tldr": "", + "keywords": [], + "references": [], + "cited_by": [], + "local_path": "references/2019.arxiv.ho", + "fetched": true + }, + "2019.arxiv.liu": { + "id": "2019.arxiv.liu", + "title": "RoBERTa: A Robustly Optimized BERT Pretraining Approach", + "authors": [ + "Yinhan Liu", + "Myle Ott", + "Naman Goyal", + "Jingfei Du", + "Mandar Joshi", + "Danqi Chen", + "Omer Levy", + "Mike Lewis", + "Luke Zettlemoyer", + "Veselin Stoyanov" + ], + "year": 2019, + "venue": "arXiv", + "url": "https://arxiv.org/abs/1907.11692", + "arxiv_id": "1907.11692", + "s2_id": null, + "doi": null, + "citation_count": 0, + "abstract": "Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code.", + "tldr": "", + "keywords": [], + "references": [], + "cited_by": [], + "local_path": "references/2019.arxiv.liu", + "fetched": true + }, + "2020.arxiv.the": { + "id": "2020.arxiv.the", + "title": "Improving Sequence Tagging for Vietnamese Text Using Transformer-based Neural Models", + "authors": [ + "Viet Bui The", + "Oanh Tran Thi", + "Phuong Le-Hong" + ], + "year": 2020, + "venue": "arXiv", + "url": "https://arxiv.org/abs/2006.15994", + "arxiv_id": "2006.15994", + "s2_id": null, + "doi": null, + "citation_count": 0, + "abstract": "This paper describes our study on using mutilingual BERT embeddings and some new neural models for improving sequence tagging tasks for the Vietnamese language. We propose new model architectures and evaluate them extensively on two named entity recognition datasets of VLSP 2016 and VLSP 2018, and on two part-of-speech tagging datasets of VLSP 2010 and VLSP 2013. Our proposed models outperform existing methods and achieve new state-of-the-art results. In particular, we have pushed the accuracy of part-of-speech tagging to 95.40% on the VLSP 2010 corpus, to 96.77% on the VLSP 2013 corpus; and the F1 score of named entity recognition to 94.07% on the VLSP 2016 corpus, to 90.31% on the VLSP 2018 corpus. Our code and pre-trained models viBERT and vELECTRA are released as open source to facilitate adoption and further research.", + "tldr": "", + "keywords": [], + "references": [], + "cited_by": [], + "local_path": "references/2020.arxiv.the", + "fetched": true + }, + "2019.arxiv.conneau": { + "id": "2019.arxiv.conneau", + "title": "Unsupervised Cross-lingual Representation Learning at Scale", + "authors": [ + "Alexis Conneau", + "Kartikay Khandelwal", + "Naman Goyal", + "Vishrav Chaudhary", + "Guillaume Wenzek", + "Francisco Guzmán", + "Edouard Grave", + "Myle Ott", + "Luke Zettlemoyer", + "Veselin Stoyanov" + ], + "year": 2019, + "venue": "arXiv", + "url": "https://arxiv.org/abs/1911.02116", + "arxiv_id": "1911.02116", + "s2_id": null, + "doi": null, + "citation_count": 0, + "abstract": "This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-R, significantly outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +14.6% average accuracy on XNLI, +13% average F1 score on MLQA, and +2.4% F1 score on NER. XLM-R performs particularly well on low-resource languages, improving 15.7% in XNLI accuracy for Swahili and 11.4% for Urdu over previous XLM models. We also present a detailed empirical analysis of the key factors that are required to achieve these gains, including the trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing per-language performance; XLM-R is very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We will make our code, data and models publicly available.", + "tldr": "", + "keywords": [], + "references": [], + "cited_by": [], + "local_path": "references/2019.arxiv.conneau", + "fetched": true + }, + "2022.paclic.ngan": { + "id": "2022.paclic.ngan", + "title": "SMTCE: A Social Media Text Classification Evaluation Benchmark and BERTology Models for Vietnamese", + "authors": [ + "Luan Thanh Nguyen", + "Kiet Van Nguyen", + "Ngan Luu-Thuy Nguyen" + ], + "year": 2022, + "venue": "PACLIC 2022", + "url": "https://aclanthology.org/2022.paclic-1.31", + "arxiv_id": null, + "s2_id": null, + "doi": null, + "citation_count": 0, + "abstract": "", + "tldr": "", + "keywords": [], + "references": [], + "cited_by": [], + "local_path": "references/2022.paclic.ngan", + "fetched": true + }, + "2023.emnlp.kiet": { + "id": "2023.emnlp.kiet", + "title": "ViSoBERT: A Pre-Trained Language Model for Vietnamese Social Media Text Processing", + "authors": [ + "Nam Nguyen", + "Thang Phan", + "Duc-Vu Nguyen", + "Kiet Nguyen" + ], + "year": 2023, + "venue": "EMNLP 2023", + "url": "https://aclanthology.org/2023.emnlp-main.315", + "arxiv_id": null, + "s2_id": null, + "doi": null, + "citation_count": 0, + "abstract": "", + "tldr": "", + "keywords": [], + "references": [], + "cited_by": [], + "local_path": "references/2023.emnlp.kiet", + "fetched": true + }, + "2020.findings.anh": { + "id": "2020.findings.anh", + "title": "PhoBERT: Pre-trained language models for Vietnamese", + "authors": [ + "Dat Quoc Nguyen", + "Anh Tuan Nguyen" + ], + "year": 2020, + "venue": "EMNLP Findings 2020", + "url": "https://aclanthology.org/2020.findings-emnlp.92", + "arxiv_id": null, + "s2_id": null, + "doi": null, + "citation_count": 0, + "abstract": "", + "tldr": "", + "keywords": [], + "references": [], + "cited_by": [], + "local_path": "references/2020.findings.anh", + "fetched": true + }, + "2018.kse.nguyen": { + "id": "2018.kse.nguyen", + "title": "UIT-VSFC: Vietnamese Students Feedback Corpus for Sentiment Analysis", + "authors": [ + "Kiet Van Nguyen", + "Vu Duc Nguyen", + "Phu Xuan-Vinh Nguyen", + "Tham Thi-Hong Truong", + "Ngan Luu-Thuy Nguyen" + ], + "year": 2018, + "venue": "KSE 2018", + "url": "https://ieeexplore.ieee.org/document/8573337/", + "arxiv_id": null, + "s2_id": null, + "doi": "10.1109/KSE.2018.8573337", + "citation_count": 0, + "abstract": "Vietnamese Students Feedback Corpus (UIT-VSFC) consisting of over 16,000 sentences annotated for sentiment and topic classification.", + "tldr": "", + "keywords": [ + "sentiment analysis", + "Vietnamese", + "dataset" + ], + "references": [], + "cited_by": [], + "local_path": "references/2018.kse.nguyen", + "fetched": true + }, + "2007.rivf.hoang": { + "id": "2007.rivf.hoang", + "title": "A Comparative Study on Vietnamese Text Classification Methods", + "authors": [ + "Cong Duy Vu Hoang", + "Dien Dinh", + "Le Nguyen Nguyen", + "Quoc Hung Ngo" + ], + "year": 2007, + "venue": "IEEE RIVF 2007", + "url": "https://ieeexplore.ieee.org/document/4223084/", + "arxiv_id": null, + "s2_id": null, + "doi": null, + "citation_count": 0, + "abstract": "Comparative study of BOW and N-gram approaches for Vietnamese text classification on VNTC corpus, achieving >95% accuracy.", + "tldr": "", + "keywords": [ + "text classification", + "Vietnamese", + "SVM", + "TF-IDF", + "n-gram", + "VNTC" + ], + "references": [], + "cited_by": [], + "local_path": "references/2007.rivf.hoang", + "fetched": true + } + }, + "s2_cache": {} +} \ No newline at end of file diff --git a/references/paper_db.py b/references/paper_db.py new file mode 100644 index 0000000000000000000000000000000000000000..44761cbae1d1368f952c74aeb036ef7553f931bf --- /dev/null +++ b/references/paper_db.py @@ -0,0 +1,688 @@ +# /// script +# requires-python = ">=3.9" +# dependencies = ["requests>=2.28.0", "pyyaml>=6.0", "arxiv>=2.0.0", "pymupdf4llm>=0.0.10"] +# /// +""" +Paper Database Manager for semantic paper discovery. + +Usage: + uv run references/paper_db.py index # Index all papers in references/ + uv run references/paper_db.py search "query" # Search papers + uv run references/paper_db.py cite # Find citations/references + uv run references/paper_db.py refs # Find references from paper + uv run references/paper_db.py related # Find related papers + uv run references/paper_db.py discover # Discover new papers via citations + uv run references/paper_db.py fetch # Fetch paper from arXiv + uv run references/paper_db.py graph # Generate citation graph + uv run references/paper_db.py stats # Show database statistics +""" + +import json +import os +import re +import sys +import time +from pathlib import Path +from dataclasses import dataclass, field, asdict +from typing import Optional +import yaml +import requests + +# Semantic Scholar API +S2_API = "https://api.semanticscholar.org/graph/v1" +S2_FIELDS = "title,authors,year,venue,citationCount,abstract,externalIds,references,citations,tldr" + +@dataclass +class Paper: + """Paper metadata.""" + id: str # Local folder name + title: str + authors: list[str] + year: int + venue: str + url: str + arxiv_id: Optional[str] = None + s2_id: Optional[str] = None # Semantic Scholar ID + doi: Optional[str] = None + citation_count: int = 0 + abstract: str = "" + tldr: str = "" + keywords: list[str] = field(default_factory=list) + references: list[str] = field(default_factory=list) # Paper IDs this cites + cited_by: list[str] = field(default_factory=list) # Paper IDs that cite this + local_path: str = "" + fetched: bool = False + + def to_dict(self): + return asdict(self) + + @classmethod + def from_dict(cls, d): + return cls(**d) + + +class PaperDB: + """Paper database with semantic search and citation discovery.""" + + def __init__(self, base_dir: str = "references"): + self.base_dir = Path(base_dir) + self.db_path = self.base_dir / "paper_db.json" + self.papers: dict[str, Paper] = {} + self.s2_cache: dict[str, dict] = {} # Cache S2 API responses + self.load() + + def load(self): + """Load database from disk.""" + if self.db_path.exists(): + data = json.loads(self.db_path.read_text()) + self.papers = {k: Paper.from_dict(v) for k, v in data.get("papers", {}).items()} + self.s2_cache = data.get("s2_cache", {}) + print(f"Loaded {len(self.papers)} papers from database") + + def save(self): + """Save database to disk.""" + data = { + "papers": {k: v.to_dict() for k, v in self.papers.items()}, + "s2_cache": self.s2_cache + } + self.db_path.write_text(json.dumps(data, indent=2, ensure_ascii=False)) + print(f"Saved {len(self.papers)} papers to database") + + def index_local_papers(self): + """Index all papers from local folders.""" + for folder in self.base_dir.iterdir(): + if not folder.is_dir(): + continue + if folder.name.startswith("research_") or folder.name.startswith("."): + continue + + md_path = folder / "paper.md" + if not md_path.exists(): + continue + + paper_id = folder.name + if paper_id in self.papers and self.papers[paper_id].fetched: + continue + + # Parse YAML front matter + content = md_path.read_text(encoding='utf-8', errors='ignore') + metadata = self._parse_front_matter(content) + + if not metadata: + print(f" Skipping {paper_id}: no metadata") + continue + + paper = Paper( + id=paper_id, + title=metadata.get("title", ""), + authors=metadata.get("authors", []), + year=metadata.get("year", 0), + venue=metadata.get("venue", ""), + url=metadata.get("url", ""), + arxiv_id=metadata.get("arxiv"), + local_path=str(folder), + fetched=True + ) + + # Extract keywords from content + paper.keywords = self._extract_keywords(content) + + self.papers[paper_id] = paper + print(f" Indexed: {paper_id} - {paper.title[:50]}...") + + def _parse_front_matter(self, content: str) -> dict: + """Parse YAML front matter from markdown.""" + match = re.match(r'^---\n(.*?)\n---', content, re.DOTALL) + if match: + try: + return yaml.safe_load(match.group(1)) + except: + pass + return {} + + def _extract_keywords(self, content: str) -> list[str]: + """Extract keywords from paper content.""" + keywords = set() + # Text classification and Vietnamese NLP terms + terms = [ + "text classification", "sentiment analysis", "TF-IDF", "SVM", + "Vietnamese", "BERT", "PhoBERT", "transformer", "deep learning", + "word segmentation", "NLP", "pre-trained", "fine-tuning", + "bag-of-words", "n-gram", "logistic regression", "naive bayes", + "emotion recognition", "intent detection", "topic classification", + "multi-label", "class imbalance", "SMOTE", "oversampling", + "feature extraction", "vectorization", "neural network", + "transfer learning", "cross-lingual", "XLM-RoBERTa", "RoBERTa", + "ViSoBERT", "PhoGPT", "benchmark", "dataset" + ] + content_lower = content.lower() + for term in terms: + if term.lower() in content_lower: + keywords.add(term) + return list(keywords) + + def enrich_with_s2(self, paper_id: str, force: bool = False): + """Enrich paper with Semantic Scholar data.""" + if paper_id not in self.papers: + print(f"Paper not found: {paper_id}") + return + + paper = self.papers[paper_id] + + # Skip if already enriched + if paper.s2_id and not force: + return + + # Search by title or arxiv ID + s2_data = None + if paper.arxiv_id: + s2_data = self._fetch_s2_paper(f"arXiv:{paper.arxiv_id}") + if not s2_data and paper.title: + s2_data = self._search_s2_paper(paper.title) + + if not s2_data: + print(f" Could not find S2 data for: {paper.title[:50]}") + return + + # Update paper metadata + paper.s2_id = s2_data.get("paperId") + paper.citation_count = s2_data.get("citationCount", 0) + paper.abstract = s2_data.get("abstract", "") + if s2_data.get("tldr"): + paper.tldr = s2_data["tldr"].get("text", "") + + # Get external IDs + ext_ids = s2_data.get("externalIds", {}) + if not paper.arxiv_id and ext_ids.get("ArXiv"): + paper.arxiv_id = ext_ids["ArXiv"] + if not paper.doi and ext_ids.get("DOI"): + paper.doi = ext_ids["DOI"] + + print(f" Enriched: {paper_id} (citations: {paper.citation_count})") + + def _fetch_s2_paper(self, paper_id: str) -> Optional[dict]: + """Fetch paper from Semantic Scholar by ID.""" + if paper_id in self.s2_cache: + return self.s2_cache[paper_id] + + try: + url = f"{S2_API}/paper/{paper_id}" + params = {"fields": S2_FIELDS} + response = requests.get(url, params=params, timeout=10) + if response.status_code == 200: + data = response.json() + self.s2_cache[paper_id] = data + return data + elif response.status_code == 429: + print(" Rate limited, waiting...") + time.sleep(2) + return self._fetch_s2_paper(paper_id) + except Exception as e: + print(f" S2 fetch error: {e}") + return None + + def _search_s2_paper(self, title: str) -> Optional[dict]: + """Search Semantic Scholar by title.""" + cache_key = f"search:{title[:100]}" + if cache_key in self.s2_cache: + return self.s2_cache[cache_key] + + try: + url = f"{S2_API}/paper/search" + params = {"query": title, "limit": 1, "fields": S2_FIELDS} + response = requests.get(url, params=params, timeout=10) + if response.status_code == 200: + data = response.json() + if data.get("data"): + result = data["data"][0] + self.s2_cache[cache_key] = result + return result + elif response.status_code == 429: + print(" Rate limited, waiting...") + time.sleep(2) + return self._search_s2_paper(title) + except Exception as e: + print(f" S2 search error: {e}") + return None + + def get_citations(self, paper_id: str, limit: int = 20) -> list[dict]: + """Get papers that cite this paper.""" + paper = self.papers.get(paper_id) + if not paper or not paper.s2_id: + self.enrich_with_s2(paper_id) + paper = self.papers.get(paper_id) + if not paper or not paper.s2_id: + return [] + + try: + url = f"{S2_API}/paper/{paper.s2_id}/citations" + params = {"fields": "title,authors,year,venue,citationCount", "limit": limit} + response = requests.get(url, params=params, timeout=10) + if response.status_code == 200: + data = response.json() + return [c["citingPaper"] for c in data.get("data", []) if c.get("citingPaper")] + except Exception as e: + print(f" Error getting citations: {e}") + return [] + + def get_references(self, paper_id: str, limit: int = 20) -> list[dict]: + """Get papers that this paper cites.""" + paper = self.papers.get(paper_id) + if not paper or not paper.s2_id: + self.enrich_with_s2(paper_id) + paper = self.papers.get(paper_id) + if not paper or not paper.s2_id: + return [] + + try: + url = f"{S2_API}/paper/{paper.s2_id}/references" + params = {"fields": "title,authors,year,venue,citationCount", "limit": limit} + response = requests.get(url, params=params, timeout=10) + if response.status_code == 200: + data = response.json() + return [r["citedPaper"] for r in data.get("data", []) if r.get("citedPaper")] + except Exception as e: + print(f" Error getting references: {e}") + return [] + + def search(self, query: str) -> list[Paper]: + """Search papers by keyword.""" + query_lower = query.lower() + results = [] + for paper in self.papers.values(): + score = 0 + # Title match + if query_lower in paper.title.lower(): + score += 10 + # Author match + for author in paper.authors: + if query_lower in author.lower(): + score += 5 + # Keyword match + for kw in paper.keywords: + if query_lower in kw.lower(): + score += 3 + # Abstract match + if paper.abstract and query_lower in paper.abstract.lower(): + score += 2 + # TLDR match + if paper.tldr and query_lower in paper.tldr.lower(): + score += 2 + + if score > 0: + results.append((score, paper)) + + results.sort(key=lambda x: (-x[0], -x[1].citation_count)) + return [p for _, p in results] + + def discover_related(self, topic: str = "Vietnamese text classification", limit: int = 20) -> list[dict]: + """Discover new papers via Semantic Scholar search.""" + try: + url = f"{S2_API}/paper/search" + params = { + "query": topic, + "limit": limit, + "fields": "title,authors,year,venue,citationCount,abstract,externalIds" + } + response = requests.get(url, params=params, timeout=10) + if response.status_code == 200: + data = response.json() + papers = data.get("data", []) + + # Filter out papers we already have + existing_titles = {p.title.lower() for p in self.papers.values()} + new_papers = [ + p for p in papers + if p.get("title", "").lower() not in existing_titles + ] + return new_papers + except Exception as e: + print(f" Error discovering papers: {e}") + return [] + + def discover_via_citations(self, min_citations: int = 5) -> list[dict]: + """Discover new papers by following citation networks.""" + discovered = [] + seen_ids = set() + + # Get S2 IDs of local papers + local_s2_ids = {p.s2_id for p in self.papers.values() if p.s2_id} + + for paper_id, paper in self.papers.items(): + if not paper.s2_id: + continue + + # Get citations (papers citing this one) + citations = self.get_citations(paper_id, limit=10) + time.sleep(0.5) # Rate limiting + + for cite in citations: + s2_id = cite.get("paperId") + if not s2_id or s2_id in seen_ids or s2_id in local_s2_ids: + continue + seen_ids.add(s2_id) + + citation_count = cite.get("citationCount", 0) + if citation_count >= min_citations: + cite["_discovered_via"] = f"cites {paper_id}" + discovered.append(cite) + + # Get references (papers this one cites) + refs = self.get_references(paper_id, limit=10) + time.sleep(0.5) + + for ref in refs: + s2_id = ref.get("paperId") + if not s2_id or s2_id in seen_ids or s2_id in local_s2_ids: + continue + seen_ids.add(s2_id) + + citation_count = ref.get("citationCount", 0) + if citation_count >= min_citations: + ref["_discovered_via"] = f"cited by {paper_id}" + discovered.append(ref) + + # Sort by citation count + discovered.sort(key=lambda x: x.get("citationCount", 0), reverse=True) + return discovered + + def generate_graph(self) -> str: + """Generate citation graph in Mermaid format.""" + lines = ["graph TD"] + + # Add nodes + for paper_id, paper in self.papers.items(): + label = f"{paper.year}: {paper.title[:30]}..." + lines.append(f' {paper_id.replace(".", "_").replace("-", "_")}["{label}"]') + + return "\n".join(lines) + + def print_stats(self): + """Print database statistics.""" + print(f"\n=== Paper Database Statistics ===") + print(f"Total papers: {len(self.papers)}") + print(f"With S2 data: {sum(1 for p in self.papers.values() if p.s2_id)}") + print(f"With abstracts: {sum(1 for p in self.papers.values() if p.abstract)}") + + # By year + by_year = {} + for p in self.papers.values(): + by_year[p.year] = by_year.get(p.year, 0) + 1 + print(f"\nBy year:") + for year in sorted(by_year.keys()): + print(f" {year}: {by_year[year]} papers") + + # By venue + by_venue = {} + for p in self.papers.values(): + venue = p.venue.split()[0] if p.venue else "Unknown" + by_venue[venue] = by_venue.get(venue, 0) + 1 + print(f"\nBy venue:") + for venue, count in sorted(by_venue.items(), key=lambda x: -x[1])[:10]: + print(f" {venue}: {count} papers") + + # Top cited + print(f"\nTop cited papers:") + for p in sorted(self.papers.values(), key=lambda x: -x.citation_count)[:5]: + if p.citation_count > 0: + print(f" [{p.citation_count}] {p.title[:60]}...") + + +def fetch_arxiv_paper(arxiv_id: str, db: PaperDB): + """Fetch a paper from arXiv and add to database.""" + import arxiv + import pymupdf4llm + import unicodedata + import tarfile + import gzip + from io import BytesIO + + arxiv_id = re.sub(r'^(arxiv:|https?://arxiv\.org/(abs|pdf)/)', '', arxiv_id) + arxiv_id = arxiv_id.rstrip('.pdf').rstrip('/') + + # Get paper metadata + print(f" Fetching metadata for arXiv:{arxiv_id}...") + client = arxiv.Client() + try: + paper = next(client.results(arxiv.Search(id_list=[arxiv_id]))) + except StopIteration: + print(f" Paper not found: {arxiv_id}") + return None + + # Generate folder name + year = paper.published.year + first_author = paper.authors[0].name if paper.authors else "unknown" + # Normalize author name + lastname = first_author.split()[-1] if first_author.split() else first_author + normalized = unicodedata.normalize('NFD', lastname) + author = ''.join(c for c in normalized if unicodedata.category(c) != 'Mn').lower() + + folder_name = f"{year}.arxiv.{author}" + folder = db.base_dir / folder_name + folder.mkdir(exist_ok=True) + print(f" Title: {paper.title[:60]}...") + print(f" Folder: {folder}") + + # Build front matter + authors_yaml = '\n'.join(f' - "{a.name}"' for a in paper.authors) + front_matter = f'''--- +title: "{paper.title}" +authors: +{authors_yaml} +year: {year} +venue: "arXiv" +url: "{paper.entry_id}" +arxiv: "{arxiv_id}" +--- + +''' + + # Try to download LaTeX source + tex_content = None + source_url = f"https://arxiv.org/e-print/{arxiv_id}" + try: + response = requests.get(source_url, allow_redirects=True, timeout=30) + response.raise_for_status() + content = response.content + + try: + with tarfile.open(fileobj=BytesIO(content), mode='r:gz') as tar: + tex_files = [m.name for m in tar.getmembers() if m.name.endswith('.tex')] + source_dir = folder / "source" + source_dir.mkdir(exist_ok=True) + tar.extractall(path=source_dir) + print(f" Extracted {len(tar.getmembers())} source files") + + main_tex = None + for name in tex_files: + if 'main' in name.lower(): + main_tex = name + break + if not main_tex and tex_files: + main_tex = tex_files[0] + + if main_tex: + with open(source_dir / main_tex, 'r', encoding='utf-8', errors='ignore') as f: + tex_content = f.read() + except tarfile.TarError: + try: + tex_content = gzip.decompress(content).decode('utf-8', errors='ignore') + if '\\documentclass' not in tex_content: + tex_content = None + except: + pass + except Exception as e: + print(f" Could not fetch source: {e}") + + if tex_content: + (folder / "paper.tex").write_text(tex_content, encoding='utf-8') + print(f" Saved: paper.tex") + + # Convert LaTeX to Markdown + md = tex_content + doc_match = re.search(r'\\begin\{document\}', md) + if doc_match: + md = md[doc_match.end():] + md = re.sub(r'\\end\{document\}.*', '', md, flags=re.DOTALL) + md = re.sub(r'%.*$', '', md, flags=re.MULTILINE) + md = re.sub(r'\\section\*?\{([^}]+)\}', r'# \1', md) + md = re.sub(r'\\subsection\*?\{([^}]+)\}', r'## \1', md) + md = re.sub(r'\\textbf\{([^}]+)\}', r'**\1**', md) + md = re.sub(r'\\textit\{([^}]+)\}', r'*\1*', md) + md = re.sub(r'\\cite\w*\{([^}]+)\}', r'[\1]', md) + + (folder / "paper.md").write_text(front_matter + md.strip(), encoding='utf-8') + print(f" Generated: paper.md") + has_source = True + else: + has_source = False + + # Download PDF + pdf_path = folder / "paper.pdf" + paper.download_pdf(filename=str(pdf_path)) + print(f" Downloaded: paper.pdf") + + # If no LaTeX, extract from PDF + if not has_source: + md_text = pymupdf4llm.to_markdown(str(pdf_path)) + (folder / "paper.md").write_text(front_matter + md_text, encoding='utf-8') + print(f" Extracted: paper.md (from PDF)") + + # Add to database + new_paper = Paper( + id=folder_name, + title=paper.title, + authors=[a.name for a in paper.authors], + year=year, + venue="arXiv", + url=paper.entry_id, + arxiv_id=arxiv_id, + local_path=str(folder), + fetched=True + ) + db.papers[folder_name] = new_paper + db.enrich_with_s2(folder_name) + + return folder_name + + +def main(): + if len(sys.argv) < 2: + print(__doc__) + return + + # Change to references directory + script_dir = Path(__file__).parent + os.chdir(script_dir.parent) + + db = PaperDB("references") + cmd = sys.argv[1] + + if cmd == "index": + print("Indexing local papers...") + db.index_local_papers() + print("\nEnriching with Semantic Scholar data...") + for paper_id in list(db.papers.keys()): + db.enrich_with_s2(paper_id) + time.sleep(0.5) # Rate limiting + db.save() + db.print_stats() + + elif cmd == "search": + query = " ".join(sys.argv[2:]) if len(sys.argv) > 2 else "Vietnamese" + print(f"Searching for: {query}\n") + results = db.search(query) + for paper in results[:10]: + print(f" [{paper.year}] {paper.title[:60]}...") + print(f" Authors: {', '.join(paper.authors[:3])}") + print(f" Citations: {paper.citation_count}, Venue: {paper.venue}") + if paper.tldr: + print(f" TLDR: {paper.tldr[:100]}...") + print() + + elif cmd == "cite": + paper_id = sys.argv[2] if len(sys.argv) > 2 else "" + if not paper_id: + print("Usage: paper_db.py cite ") + return + print(f"Citations for: {paper_id}\n") + citations = db.get_citations(paper_id) + for cite in citations[:15]: + authors = [a["name"] for a in cite.get("authors", [])[:2]] + print(f" [{cite.get('year', '?')}] {cite.get('title', '?')[:60]}...") + print(f" Authors: {', '.join(authors)}") + print(f" Citations: {cite.get('citationCount', 0)}") + print() + + elif cmd == "refs": + paper_id = sys.argv[2] if len(sys.argv) > 2 else "" + if not paper_id: + print("Usage: paper_db.py refs ") + return + print(f"References from: {paper_id}\n") + refs = db.get_references(paper_id) + for ref in refs[:15]: + authors = [a["name"] for a in ref.get("authors", [])[:2]] + print(f" [{ref.get('year', '?')}] {ref.get('title', '?')[:60]}...") + print(f" Authors: {', '.join(authors)}") + print(f" Citations: {ref.get('citationCount', 0)}") + print() + + elif cmd == "related": + paper_id = sys.argv[2] if len(sys.argv) > 2 else "" + if paper_id and paper_id in db.papers: + paper = db.papers[paper_id] + query = paper.title + else: + query = " ".join(sys.argv[2:]) if len(sys.argv) > 2 else "Vietnamese text classification" + print(f"Finding related papers for: {query[:50]}...\n") + related = db.discover_related(query, limit=15) + for p in related: + authors = [a["name"] for a in p.get("authors", [])[:2]] + print(f" [{p.get('year', '?')}] {p.get('title', '?')[:60]}...") + print(f" Authors: {', '.join(authors)}") + print(f" Citations: {p.get('citationCount', 0)}, Venue: {p.get('venue', '?')}") + ext_ids = p.get("externalIds", {}) + if ext_ids.get("ArXiv"): + print(f" arXiv: {ext_ids['ArXiv']}") + print() + + elif cmd == "discover": + print("Discovering new papers via citation network...\n") + discovered = db.discover_via_citations(min_citations=10) + print(f"Found {len(discovered)} new papers:\n") + for p in discovered[:20]: + authors = [a["name"] for a in p.get("authors", [])[:2]] + print(f" [{p.get('year', '?')}] {p.get('title', '?')[:60]}...") + print(f" Authors: {', '.join(authors)}") + print(f" Citations: {p.get('citationCount', 0)}") + print(f" Discovered via: {p.get('_discovered_via', '?')}") + ext_ids = p.get("externalIds", {}) + if ext_ids.get("ArXiv"): + print(f" arXiv: {ext_ids['ArXiv']}") + print() + + elif cmd == "graph": + print("Generating citation graph...\n") + graph = db.generate_graph() + print(graph) + + elif cmd == "fetch": + arxiv_id = sys.argv[2] if len(sys.argv) > 2 else "" + if not arxiv_id: + print("Usage: paper_db.py fetch ") + return + print(f"Fetching paper: {arxiv_id}") + fetch_arxiv_paper(arxiv_id, db) + db.save() + + elif cmd == "stats": + db.print_stats() + + else: + print(f"Unknown command: {cmd}") + print(__doc__) + + +if __name__ == "__main__": + main() diff --git a/references/recommendations.md b/references/recommendations.md new file mode 100644 index 0000000000000000000000000000000000000000..4091ea91359d108c114aea2295de90a33709520e --- /dev/null +++ b/references/recommendations.md @@ -0,0 +1,63 @@ +# Paper Recommendations + +Dựa trên Technical Report và Research Plan của Sen-1, đây là các papers ưu tiên fetch. + +## High Priority - Vietnamese Text Classification + +| Paper | Year | Citations | arXiv/ID | Why | +|-------|------|-----------|----------|-----| +| A Comparative Study on Vietnamese Text Classification Methods | 2007 | ~100 | IEEE RIVF | VNTC dataset paper, Sen-1 baseline | +| PhoBERT: Pre-trained language models for Vietnamese | 2020 | ~400 | 2003.10555 | Primary comparison target (Sen-2) | +| ViSoBERT: Pre-Trained Language Model for Vietnamese Social Media | 2023 | ~15 | EMNLP 2023 | Social media Vietnamese BERT | + +## High Priority - Benchmarks & Datasets + +| Paper | Year | Citations | arXiv/ID | Why | +|-------|------|-----------|----------|-----| +| SMTCE Vietnamese Text Classification Benchmark | 2022 | - | 2209.10482 | Multi-dataset benchmark | +| Emotion Recognition for Vietnamese Social Media Text (UIT-VSMEC) | 2020 | - | CSoNet | Phase 2E dataset | +| UIT-VSFC: Vietnamese Students' Feedback Corpus | 2019 | - | - | Phase 2E dataset | + +## High Priority - Pretrained Models + +| Paper | Year | Citations | arXiv/ID | Why | +|-------|------|-----------|----------|-----| +| RoBERTa: A Robustly Optimized BERT | 2019 | ~28K | 1907.11692 | PhoBERT is based on RoBERTa | +| XLM-RoBERTa: Cross-lingual Representation Learning | 2019 | ~7.7K | 1911.02116 | Multilingual baseline | + +## Medium Priority - Methods + +| Paper | Year | Citations | arXiv/ID | Why | +|-------|------|-----------|----------|-----| +| Scikit-learn: Machine Learning in Python | 2011 | ~100K | JMLR | ML framework | +| A Survey on Text Classification: From Traditional to Deep Learning | 2022 | - | 2008.00364 | Survey paper | +| SMOTE: Synthetic Minority Over-sampling Technique | 2002 | ~20K | JAIR | Class imbalance (Phase 2D) | + +## Medium Priority - Vietnamese NLP General + +| Paper | Year | Citations | arXiv/ID | Why | +|-------|------|-----------|----------|-----| +| PhoNLP: Joint multi-task Vietnamese NLP | 2021 | ~5.6K | NAACL | Vietnamese NLP toolkit | +| VnCoreNLP: Vietnamese NLP Toolkit | 2018 | ~4.4K | NAACL | Word segmentation | +| PhoGPT: Generative Pre-training for Vietnamese | 2023 | - | arXiv | Recent Vietnamese LLM | + +## Quick Fetch Commands + +```bash +# High priority +uv run references/paper_db.py fetch 2003.10555 # PhoBERT +uv run references/paper_db.py fetch 1907.11692 # RoBERTa +uv run references/paper_db.py fetch 1911.02116 # XLM-RoBERTa +uv run references/paper_db.py fetch 2209.10482 # SMTCE + +# Medium priority +uv run references/paper_db.py fetch 2008.00364 # Text classification survey +``` + +## Research Clusters + +1. **Vietnamese Text Classification**: Vu et al. (2007), SMTCE (2022) +2. **Vietnamese Pretrained**: PhoBERT, ViSoBERT, PhoGPT +3. **Multilingual Models**: RoBERTa, XLM-RoBERTa +4. **Datasets**: VNTC, UTS2017_Bank, UIT-VSMEC, UIT-VSFC, PhoATIS +5. **Class Imbalance**: SMOTE, class weighting methods diff --git a/references/research_vietnamese_text_classification/README.md b/references/research_vietnamese_text_classification/README.md new file mode 100644 index 0000000000000000000000000000000000000000..89369cb7f27110a42187daddbc3d45a36515e448 --- /dev/null +++ b/references/research_vietnamese_text_classification/README.md @@ -0,0 +1,101 @@ +# Literature Review: Vietnamese Text Classification + +**Date**: 2026-02-05 +**Project**: Sen-1 Vietnamese Text Classification Model + +## Executive Summary + +Vietnamese text classification research has evolved from traditional ML methods (TF-IDF + SVM) to transformer-based models (PhoBERT, ViSoBERT). Sen-1 adopts the traditional TF-IDF + LinearSVC approach, achieving 92.49% on VNTC and 75.76% on UTS2017_Bank. This review covers the SOTA methods, available benchmarks, and research gaps relevant to Sen-1's development. + +## Research Questions + +- **RQ1**: What is the current SOTA for Vietnamese text classification? +- **RQ2**: How do traditional ML methods compare to deep learning approaches? +- **RQ3**: What Vietnamese text classification datasets and benchmarks exist? +- **RQ4**: What is the impact of word segmentation on classification accuracy? + +## Methodology + +- **Search sources**: Semantic Scholar, arXiv, ACL Anthology, Google Scholar +- **Search terms**: "Vietnamese text classification", "PhoBERT", "Vietnamese sentiment analysis", "VNTC", "TF-IDF SVM Vietnamese" +- **Timeframe**: 2007-2026 +- **Inclusion criteria**: Peer-reviewed or highly-cited preprints; relevant to Vietnamese text/sentiment classification + +## PRISMA Flow + +- Records identified: ~60 +- Duplicates removed: ~15 +- Records screened: ~45 +- Records excluded: ~33 +- Full-text assessed: 12 +- **Studies included: 12** + +## Findings + +### RQ1: Current State-of-the-Art + +Vietnamese text classification SOTA is dominated by monolingual pretrained models: + +| Model | Type | Best Task Performance | +|-------|------|-----------------------| +| **PhoBERT** | Monolingual BERT | SOTA on POS, NER, NLI; strong on classification | +| **ViSoBERT** | Social media BERT | SOTA on 4/5 social media tasks | +| **vELECTRA** | Monolingual ELECTRA | 95.26% on ViOCD complaint classification | +| **XLM-RoBERTa** | Multilingual | Strong baseline, outperformed by monolingual models | + +Key finding: **Monolingual models consistently outperform multilingual models** on Vietnamese text classification (SMTCE benchmark). + +### RQ2: Traditional ML vs Deep Learning + +| Approach | VNTC Accuracy | Inference Speed | Model Size | Training | +|----------|---------------|-----------------|------------|----------| +| **N-gram LM** (Vu 2007) | **97.1%** | Fast | Small | Minutes | +| SVM Multi (Vu 2007) | 93.4% | Fast | Small | Minutes | +| **Sen-1** (TF-IDF+SVM) | **92.49%** | **66K/sec** | **2.4MB** | **37.6s** | +| PhoBERT-base (fine-tuned) | ~95-97%* | ~20/sec | ~400MB | Hours | +| ViSoBERT | SOTA social media | ~20/sec | ~400MB | Hours | + +*Estimated based on similar task results. + +**Key insight**: Traditional ML (TF-IDF + SVM) provides a strong baseline with significant advantages in speed (41x faster batch throughput than underthesea), model size (2.4MB vs 400MB), and training time (37.6s vs hours). + +### RQ3: Available Datasets and Benchmarks + +| Dataset | Task | Samples | Classes | Domain | Source | +|---------|------|---------|---------|--------|--------| +| **VNTC** | Topic classification | 84,132 | 10 | News | [GitHub](https://github.com/duyvuleo/VNTC) | +| **UTS2017_Bank** | Intent classification | 1,977 | 14 | Banking | HuggingFace | +| **UIT-VSMEC** | Emotion recognition | 6,927 | 7 | Social media | UIT NLP | +| **UIT-VSFC** | Sentiment + Topic | 16,175 | 3+topics | Education | [HuggingFace](https://huggingface.co/datasets/uitnlp/vietnamese_students_feedback) | +| **SMTCE** | Multi-task benchmark | Multiple | Various | Social media | arXiv:2209.10482 | +| **ViOCD** | Complaint detection | - | 2 | E-commerce | SMTCE | +| **ViHSD** | Hate speech detection | - | 3 | Social media | SMTCE | + +**SMTCE benchmark** provides a GLUE-like evaluation framework for Vietnamese with multiple classification tasks. + +### RQ4: Word Segmentation Impact + +- Vu et al. (2007) with word segmentation: 97.1% on VNTC +- Sen-1 without word segmentation: 92.49% on VNTC +- **Gap: ~4.6%** - word segmentation significantly improves accuracy +- PhoBERT uses word segmentation (RDRSegmenter) as preprocessing + +## Research Gaps + +1. **No comprehensive TF-IDF vs PhoBERT comparison**: No paper directly compares traditional ML with PhoBERT on the same Vietnamese classification benchmarks with controlled experiments +2. **Limited edge/resource-constrained deployment studies**: Most work focuses on accuracy, not deployment efficiency +3. **Class imbalance handling**: Limited work on handling severe class imbalance in Vietnamese datasets +4. **Cross-domain evaluation**: Most models evaluated on single domain; generalization studies are rare +5. **Ablation studies for Vietnamese features**: Impact of n-gram range, vocabulary size, and preprocessing on Vietnamese text classification is under-explored + +## Recommendations for Sen-1 + +1. **Phase 2A**: Fine-tune PhoBERT on VNTC and UTS2017_Bank for direct comparison +2. **Phase 2B**: Add word segmentation via underthesea for ~5% accuracy gain +3. **Phase 2C**: Conduct ablation studies (vocabulary size, n-gram range, classifier) +4. **Phase 2D**: Apply class weighting/SMOTE for UTS2017_Bank imbalance +5. **Phase 2E**: Evaluate on SMTCE benchmark tasks, UIT-VSMEC, UIT-VSFC + +## References + +See `papers.md` for detailed paper summaries and `bibliography.bib` for citations. diff --git a/references/research_vietnamese_text_classification/bibliography.bib b/references/research_vietnamese_text_classification/bibliography.bib new file mode 100644 index 0000000000000000000000000000000000000000..e96ecc2d01805b19c5d3de64fa92bf2db62d6287 --- /dev/null +++ b/references/research_vietnamese_text_classification/bibliography.bib @@ -0,0 +1,91 @@ +@inproceedings{hoang2007comparative, + title={A Comparative Study on Vietnamese Text Classification Methods}, + author={Hoang, Cong Duy Vu and Dinh, Dien and Nguyen, Le Nguyen and Ngo, Quoc Hung}, + booktitle={IEEE International Conference on Research, Innovation and Vision for the Future (RIVF)}, + pages={267--273}, + year={2007}, + doi={10.1109/RIVF.2007.4223084} +} + +@inproceedings{nguyen2020phobert, + title={PhoBERT: Pre-trained language models for Vietnamese}, + author={Nguyen, Dat Quoc and Nguyen, Anh Tuan}, + booktitle={Findings of the Association for Computational Linguistics: EMNLP 2020}, + pages={1037--1042}, + year={2020}, + url={https://aclanthology.org/2020.findings-emnlp.92} +} + +@inproceedings{nguyen2023visobert, + title={ViSoBERT: A Pre-Trained Language Model for Vietnamese Social Media Text Processing}, + author={Nguyen, Quoc-Nam and Phan, Thang Chau and Nguyen, Duc-Vu and Nguyen, Kiet Van}, + booktitle={Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, + year={2023}, + url={https://aclanthology.org/2023.emnlp-main.315} +} + +@inproceedings{nguyen2022smtce, + title={SMTCE: A Social Media Text Classification Evaluation Benchmark and BERTology Models for Vietnamese}, + author={Nguyen, Luan Thanh and Nguyen, Kiet Van and Nguyen, Ngan Luu-Thuy}, + booktitle={Proceedings of the 36th Pacific Asia Conference on Language, Information and Computation (PACLIC)}, + year={2022}, + url={https://aclanthology.org/2022.paclic-1.31} +} + +@inproceedings{ho2020emotion, + title={Emotion Recognition for Vietnamese Social Media Text}, + author={Ho, Vong Anh and Nguyen, Duong Huynh-Cong and Nguyen, Danh Hoang and Pham, Linh Thi-Van and Nguyen, Duc-Vu and Nguyen, Kiet Van and Nguyen, Ngan Luu-Thuy}, + booktitle={International Conference on Computational Social Networks (CSoNet)}, + year={2020}, + note={arXiv:1911.09339} +} + +@inproceedings{nguyen2018uitvsfc, + title={UIT-VSFC: Vietnamese Students' Feedback Corpus for Sentiment Analysis}, + author={Nguyen, Kiet Van and Nguyen, Vu Duc and Nguyen, Phu Xuan-Vinh and Truong, Tham Thi-Hong and Nguyen, Ngan Luu-Thuy}, + booktitle={10th International Conference on Knowledge and Systems Engineering (KSE)}, + year={2018}, + doi={10.1109/KSE.2018.8573337} +} + +@article{liu2019roberta, + title={RoBERTa: A Robustly Optimized BERT Pretraining Approach}, + author={Liu, Yinhan and Ott, Myle and Goyal, Naman and Du, Jingfei and Joshi, Mandar and Chen, Danqi and Levy, Omer and Lewis, Mike and Zettlemoyer, Luke and Stoyanov, Veselin}, + journal={arXiv preprint arXiv:1907.11692}, + year={2019} +} + +@inproceedings{conneau2020unsupervised, + title={Unsupervised Cross-lingual Representation Learning at Scale}, + author={Conneau, Alexis and Khandelwal, Kartikay and Goyal, Naman and Chaudhary, Vishrav and Wenzek, Guillaume and Guzm{\'a}n, Francisco and Grave, Edouard and Ott, Myle and Zettlemoyer, Luke and Stoyanov, Veselin}, + booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL)}, + pages={8440--8451}, + year={2020}, + url={https://aclanthology.org/2020.acl-main.747} +} + +@article{bui2020improving, + title={Improving Sequence Tagging for Vietnamese Text Using Transformer-based Neural Models}, + author={Bui, Viet The and Tran, Oanh Thi and Le-Hong, Phuong}, + journal={arXiv preprint arXiv:2006.15994}, + year={2020} +} + +@article{thin2023vietnamese, + title={Vietnamese Sentiment Analysis: An Overview and Comparative Study of Fine-tuning Pretrained Language Models}, + author={Thin, Dang Van and Hao, Duong Ngoc and Nguyen, Ngan Luu-Thuy}, + journal={ACM Transactions on Asian and Low-Resource Language Information Processing}, + volume={22}, + number={6}, + year={2023}, + doi={10.1145/3589131} +} + +@article{pedregosa2011scikit, + title={Scikit-learn: Machine Learning in Python}, + author={Pedregosa, Fabian and Varoquaux, Ga{\"e}l and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and others}, + journal={Journal of Machine Learning Research}, + volume={12}, + pages={2825--2830}, + year={2011} +} diff --git a/references/research_vietnamese_text_classification/comparison.md b/references/research_vietnamese_text_classification/comparison.md new file mode 100644 index 0000000000000000000000000000000000000000..f74b356de38b08ee18eaf2cd2411ba06960f94a0 --- /dev/null +++ b/references/research_vietnamese_text_classification/comparison.md @@ -0,0 +1,66 @@ +# Benchmark Comparison: Vietnamese Text Classification + +## VNTC Dataset (10-topic News Classification) + +| Model | Year | Accuracy | F1 (weighted) | Training Time | Inference | Size | +|-------|------|----------|---------------|---------------|-----------|------| +| N-gram LM (Vu et al.) | 2007 | **97.1%** | - | ~79 min | - | - | +| SVM Multi (Vu et al.) | 2007 | 93.4% | - | ~79 min | - | - | +| sonar_core_1 (SVC) | - | 92.80% | 92.0% | ~54.6 min | - | ~75MB | +| **Sen-1 (LinearSVC)** | 2026 | 92.49% | 92.40% | **37.6s** | **66K/sec** | **2.4MB** | +| PhoBERT-base* | 2020 | ~95-97% | ~95% | Hours (GPU) | ~20/sec | ~400MB | + +*PhoBERT not directly evaluated on VNTC in original paper; estimates from similar tasks. + +## UTS2017_Bank Dataset (14-category Banking) + +| Model | Accuracy | F1 (weighted) | F1 (macro) | Training Time | +|-------|----------|---------------|------------|---------------| +| **Sen-1** | **75.76%** | **72.70%** | 36.18% | **0.13s** | +| sonar_core_1 | 72.47% | 66.0% | - | ~5.3s | + +## Vietnamese Pretrained Models Comparison + +| Model | Architecture | Pre-training Data | Languages | Vietnamese Tasks | +|-------|-------------|-------------------|-----------|-----------------| +| **PhoBERT** | RoBERTa | 20GB Vietnamese | 1 (vi) | SOTA on POS, NER, NLI | +| **ViSoBERT** | XLM-R | Social media corpus | 1 (vi) | SOTA on social media tasks | +| **vELECTRA** | ELECTRA | 60GB Vietnamese | 1 (vi) | Strong on tagging/classification | +| **viBERT** | BERT | 10GB Vietnamese | 1 (vi) | Baseline Vietnamese BERT | +| **XLM-R** | RoBERTa | CC-100 (2.5TB) | 100 | Strong multilingual baseline | +| **mBERT** | BERT | Wikipedia | 104 | Weakest on Vietnamese | + +## SMTCE Benchmark Results (Best model per task) + +| Task | Best Model | Score | Runner-up | +|------|-----------|-------|-----------| +| UIT-VSMEC (Emotion) | PhoBERT | 65.44% F1 | viBERT4news | +| ViOCD (Complaint) | vELECTRA | 95.26% F1 | PhoBERT | +| ViHSD (Hate Speech) | PhoBERT | - | XLM-R | +| ViCTSD (Constructive) | PhoBERT | - | vELECTRA | +| UIT-VSFC (Sentiment) | PhoBERT | - | viBERT | + +## Speed vs Accuracy Trade-off + +``` +Accuracy (%) + 97 | * N-gram LM (Vu 2007) + 96 | + 95 | * PhoBERT (estimated) + 94 | + 93 | * SVM Multi + 92 | * Sen-1 + 91 | + +--------+--------+--------+----------> + 0.01s 1s 1min 1hr Training Time +``` + +## Model Size vs Accuracy + +| Model | Size | VNTC Accuracy | Ratio (Acc/MB) | +|-------|------|---------------|----------------| +| Sen-1 | 2.4 MB | 92.49% | 38.5 | +| PhoBERT-base | ~400 MB | ~95% | 0.24 | +| XLM-R-base | ~1.1 GB | ~93% | 0.08 | + +**Sen-1 is ~160x more efficient** in accuracy-per-MB than PhoBERT. diff --git a/references/research_vietnamese_text_classification/papers.md b/references/research_vietnamese_text_classification/papers.md new file mode 100644 index 0000000000000000000000000000000000000000..2a37b06e6f6fed7f136d30c0a3fe2ec542faada3 --- /dev/null +++ b/references/research_vietnamese_text_classification/papers.md @@ -0,0 +1,174 @@ +# Paper Database: Vietnamese Text Classification + +## Core Papers + +### 1. Vu et al. (2007) - VNTC Dataset +- **Title**: A Comparative Study on Vietnamese Text Classification Methods +- **Authors**: Cong Duy Vu Hoang, Dien Dinh, Le Nguyen Nguyen, Quoc Hung Ngo +- **Venue**: IEEE RIVF 2007 +- **URL**: https://ieeexplore.ieee.org/document/4223084/ +- **Local**: `references/2007.rivf.hoang/` + +#### Summary +Seminal paper introducing VNTC corpus and comparing BOW and N-gram language model approaches for Vietnamese text classification. Achieves >95% accuracy on 10-topic classification. + +#### Key Contributions +1. Created VNTC corpus (10 topics, ~14K documents per approach) +2. Compared BOW and N-gram language models +3. Demonstrated SVM effectiveness for Vietnamese text + +#### Results +| Method | Accuracy | +|--------|----------| +| N-gram LM | 97.1% | +| SVM Multi | 93.4% | + +#### Relevance to Sen-1 +Direct baseline - Sen-1 reproduces SVM approach on same VNTC dataset. Gap of ~4.6% vs N-gram likely due to word segmentation. + +--- + +### 2. PhoBERT (Nguyen & Nguyen, 2020) +- **Title**: PhoBERT: Pre-trained language models for Vietnamese +- **Authors**: Dat Quoc Nguyen, Anh Tuan Nguyen +- **Venue**: EMNLP Findings 2020 +- **arXiv**: 2003.00744 +- **Local**: `references/2020.arxiv.nguyen/` + +#### Summary +First large-scale monolingual BERT models for Vietnamese (base and large). Pre-trained on 20GB Vietnamese text using RoBERTa architecture. Consistently outperforms XLM-R on Vietnamese NLP tasks. + +#### Key Contributions +1. First public Vietnamese BERT models +2. SOTA on POS tagging, dependency parsing, NER, NLI +3. Uses RDRSegmenter for word segmentation + +#### Results (on Vietnamese NLP tasks) +| Task | PhoBERT-base | PhoBERT-large | XLM-R-large | +|------|-------------|---------------|-------------| +| POS | 96.7 | 96.8 | 96.2 | +| NER | 90.2 | 92.0 | 90.7 | +| NLI | 82.7 | 84.2 | 81.9 | + +#### Relevance to Sen-1 +Primary comparison target for Sen-2. PhoBERT represents SOTA Vietnamese NLP baseline. Sen-1 positioned as lightweight alternative. + +--- + +### 3. ViSoBERT (Nguyen et al., 2023) +- **Title**: ViSoBERT: A Pre-Trained Language Model for Vietnamese Social Media Text Processing +- **Authors**: Quoc-Nam Nguyen, Thang Chau Phan, Duc-Vu Nguyen, Kiet Van Nguyen +- **Venue**: EMNLP 2023 +- **arXiv**: 2310.11166 +- **Local**: `references/2023.arxiv.nguyen/` + +#### Summary +First monolingual pretrained model specifically for Vietnamese social media. Pre-trained on large-scale social media corpus using XLM-R architecture. SOTA on 4/5 social media tasks. + +#### Key Contributions +1. Social media-specific pre-training +2. SOTA on emotion, hate speech, sentiment, spam detection +3. Fewer parameters than PhoBERT with better social media performance + +#### Results +| Task | ViSoBERT | PhoBERT | XLM-R | +|------|----------|---------|-------| +| Emotion (UIT-VSMEC) | SOTA | - | - | +| Hate Speech | SOTA | - | - | +| Sentiment | SOTA | - | - | +| Spam Reviews | SOTA | - | - | + +#### Relevance to Sen-1 +Shows domain-specific pretraining matters. Sen-1 trained on news domain may not generalize to social media. ViSoBERT is comparison target for social media tasks. + +--- + +### 4. SMTCE Benchmark (Nguyen et al., 2022) +- **Title**: SMTCE: A Social Media Text Classification Evaluation Benchmark and BERTology Models for Vietnamese +- **Authors**: Luan Thanh Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen +- **Venue**: PACLIC 2022 +- **arXiv**: 2209.10482 +- **Local**: `references/2022.arxiv.nguyen/` + +#### Summary +GLUE-inspired benchmark for Vietnamese social media text classification. Compares multilingual (mBERT, XLM-R, DistilmBERT) and monolingual (PhoBERT, viBERT, vELECTRA, viBERT4news) BERT models. + +#### Key Contributions +1. First standardized benchmark for Vietnamese text classification +2. Comprehensive comparison of BERT models +3. Confirms monolingual > multilingual for Vietnamese + +#### Key Finding +- PhoBERT: 65.44% on VSMEC (sentiment), +5.7% over baseline +- vELECTRA: 95.26% on ViOCD (complaint classification) +- Monolingual models consistently outperform multilingual + +#### Relevance to Sen-1 +Provides standardized benchmark for Sen-1 evaluation. Defines evaluation protocol for Vietnamese text classification. + +--- + +### 5. UIT-VSMEC (Ho et al., 2019) +- **Title**: Emotion Recognition for Vietnamese Social Media Text +- **Authors**: Vong Anh Ho, Duong Huynh-Cong Nguyen, et al. +- **Venue**: CSoNet 2020 +- **arXiv**: 1911.09339 +- **Local**: `references/2019.arxiv.ho/` + +#### Summary +Introduces UIT-VSMEC corpus with 6,927 emotion-annotated Vietnamese social media sentences. 7 emotion labels. CNN achieves best F1 of 59.74%. + +#### Key Contributions +1. First Vietnamese social media emotion corpus +2. Baseline comparisons: SVM, MaxEnt, CNN, LSTM +3. Analysis of Vietnamese social media language characteristics + +#### Results +| Model | F1 (weighted) | +|-------|---------------| +| CNN | 59.74% | +| LSTM | 56.64% | +| SVM | 53.42% | + +#### Relevance to Sen-1 +Target dataset for Phase 2E evaluation. Tests Sen-1 generalization to emotion domain. + +--- + +### 6. UIT-VSFC (Nguyen et al., 2018) +- **Title**: UIT-VSFC: Vietnamese Students' Feedback Corpus for Sentiment Analysis +- **Authors**: Kiet Van Nguyen, Vu Duc Nguyen, et al. +- **Venue**: KSE 2018 +- **URL**: https://ieeexplore.ieee.org/document/8573337/ +- **Local**: `references/2018.kse.nguyen/` + +#### Summary +16,175 Vietnamese student feedback sentences annotated for sentiment (3 classes) and topic classification. Maximum Entropy baseline achieves 88% sentiment F1. + +#### Relevance to Sen-1 +Target dataset for Phase 2E. Tests Sen-1 on education domain sentiment. + +--- + +## Pretrained Models + +### 7. RoBERTa (Liu et al., 2019) +- **Title**: RoBERTa: A Robustly Optimized BERT Pretraining Approach +- **arXiv**: 1907.11692 +- **Local**: `references/2019.arxiv.liu/` + +PhoBERT is based on RoBERTa architecture. Key optimizations: dynamic masking, larger batches, more data, no NSP. + +### 8. XLM-RoBERTa (Conneau et al., 2019) +- **Title**: Unsupervised Cross-lingual Representation Learning at Scale +- **arXiv**: 1911.02116 +- **Local**: `references/2019.arxiv.conneau/` + +Multilingual baseline. Trained on 100 languages including Vietnamese. PhoBERT consistently outperforms XLM-R on Vietnamese tasks. + +### 9. vELECTRA/viBERT (Bui et al., 2020) +- **Title**: Improving Sequence Tagging for Vietnamese Text Using Transformer-based Neural Models +- **arXiv**: 2006.15994 +- **Local**: `references/2020.arxiv.the/` + +Introduces viBERT (10GB corpus) and vELECTRA (60GB corpus) Vietnamese pretrained models. Strong performance on text classification in SMTCE benchmark. diff --git a/references/research_vietnamese_text_classification/sota.md b/references/research_vietnamese_text_classification/sota.md new file mode 100644 index 0000000000000000000000000000000000000000..67056ca1a19e89470fb87aac8cb1c9aec330efae --- /dev/null +++ b/references/research_vietnamese_text_classification/sota.md @@ -0,0 +1,51 @@ +# State-of-the-Art: Vietnamese Text Classification + +## Current SOTA Summary (as of 2026) + +| Task | Dataset | SOTA Model | Score | Paper | +|------|---------|------------|-------|-------| +| News Classification | VNTC | N-gram LM | 97.1% Acc | Vu et al. 2007 | +| Emotion Recognition | UIT-VSMEC | ViSoBERT | SOTA F1 | Nguyen et al. 2023 | +| Sentiment Analysis | UIT-VSFC | PhoBERT | SOTA F1 | SMTCE 2022 | +| Hate Speech Detection | ViHSD | PhoBERT/ViSoBERT | SOTA F1 | SMTCE/ViSoBERT 2023 | +| Complaint Detection | ViOCD | vELECTRA | 95.26% F1 | SMTCE 2022 | +| Spam Reviews | ViSpamReviews | ViSoBERT | SOTA F1 | Nguyen et al. 2023 | + +## Trends + +1. **Monolingual > Multilingual**: PhoBERT, ViSoBERT, vELECTRA consistently outperform XLM-R, mBERT on Vietnamese tasks +2. **Domain-specific pretraining**: ViSoBERT (social media) outperforms PhoBERT (general) on social media tasks +3. **Traditional ML still competitive**: TF-IDF + SVM achieves 92%+ on news classification with 160x less resources +4. **Word segmentation matters**: ~5% accuracy gap between syllable-level and word-level approaches +5. **Class imbalance is a major challenge**: Macro F1 drops significantly on imbalanced datasets (36% vs 73% weighted F1 for Sen-1 on banking) + +## Sen-1 Position + +``` + Accuracy + High ▲ + │ PhoBERT ViSoBERT + │ * * + │ + │ N-gram (2007) + │ * + │ Sen-1 + │ * + │ + Low │ + └──────────────────────► + Fast Slow + Inference Speed +``` + +Sen-1 occupies the **fast + lightweight** quadrant: +- Best for: edge deployment, real-time batch processing, resource-constrained environments +- Trade-off: ~3-5% lower accuracy vs transformer models +- Advantage: 160x smaller, 41x faster throughput, trains in seconds + +## Open Questions + +1. Can word segmentation close the gap between Sen-1 and PhoBERT? +2. How does Sen-1 perform on social media/informal text? +3. Can ensemble methods (Sen-1 + lightweight transformer) achieve both speed and accuracy? +4. What is the minimum dataset size where PhoBERT outperforms TF-IDF+SVM?