--- license: cc-by-sa-4.0 task_categories: - translation - text-generation language: - es - en tags: - machine-translation - parallel-corpus - spanish-english - domain-specific - legal-administrative - biomedical - heritage size_categories: - 10M3,000 characters | >3,000 characters | ### Example Usage To load the dataset: ```python from datasets import load_dataset # Load the complete dataset data = load_dataset("sinai-uja/ALIA-parallel-translation", trust_remote_code=True) # Load with streaming (recommended for this large corpus) data = load_dataset("sinai-uja/ALIA-parallel-translation", trust_remote_code=True, streaming=True) # Process in streaming mode for example in data['train']: print(f"ID: {example['id']}") print(f"Spanish: {example['text_es'][:100]}...") print(f"English: {example['text_en'][:100]}...") break ``` Example of filtering by domain: ```python from datasets import load_dataset # Load with streaming dataset = load_dataset("sinai-uja/ALIA-parallel-translation", streaming=True, split="train") # Filter biomedical domain (ID starts with '00') biomedical = dataset.filter(lambda x: x['id'].startswith('00')) # Filter legal-administrative domain (ID starts with '02') legal = dataset.filter(lambda x: x['id'].startswith('02')) # Filter heritage domain (ID starts with '01') heritage = dataset.filter(lambda x: x['id'].startswith('01')) # Filter by specific source (e.g., PubMed: '0003') pubmed = dataset.filter(lambda x: x['id'].startswith('0003')) # Filter by specific source (e.g., EURLEX: '0201') eurlex = dataset.filter(lambda x: x['id'].startswith('0201')) # Example: Process first 1000 biomedical samples count = 0 for example in biomedical: # Your processing here count += 1 if count >= 1000: break ``` Example of batch processing: ```python from datasets import load_dataset # Load full dataset (requires ~70GB RAM) data = load_dataset("sinai-uja/ALIA-parallel-translation") # Access by index example = data['train'][0] print(f"ID: {example['id']}") print(f"Spanish: {example['text_es'][:200]}...") print(f"English: {example['text_en'][:200]}...") # Get domain statistics biomedical_count = sum(1 for ex in data['train'] if ex['id'].startswith('00')) heritage_count = sum(1 for ex in data['train'] if ex['id'].startswith('01')) legal_count = sum(1 for ex in data['train'] if ex['id'].startswith('02')) print(f"Biomedical: {biomedical_count:,}") print(f"Heritage: {heritage_count:,}") print(f"Legal-Administrative: {legal_count:,}") ``` ## Dataset Creation ### Source Data The corpus integrates parallel texts from multiple authoritative sources across three specialized domains: **Biomedical Domain (ID prefix: 00-XX-XXXXXX)** - **IBECS (00-01-XXXXXX)**: Spanish bibliographic index of health sciences journal articles - **MedlinePlus (00-02-XXXXXX)**: Trusted health information from the U.S. National Library of Medicine - **PubMed (00-03-XXXXXX)**: Biomedical literature abstracts and articles from international journals **Heritage Domain (ID prefix: 01-XX-XXXXXX)** - **PCI**: Intangible Cultural Heritage (Patrimonio Cultural Inmaterial) documentation **Legal-Administrative Domain (ID prefix: 02-XX-XXXXXX)** - **EURLEX (02-01-XXXXXX)**: European Union legislation, regulations, and legal documents - **EUROPAT (02-02-XXXXXX)**: European Patent Office documentation and technical patent descriptions - **UNPC (02-03-XXXXXX)**: United Nations Parallel Corpus including resolutions, reports, and official documents All data come from official, publicly accessible, and authoritative sources in their respective domains. ### Data Collection and Processing The corpus was compiled from publicly available parallel texts from official and authoritative sources. The data collection focused on three specialized domains to support domain-specific machine translation research. Each source was assigned a systematic ID prefix to enable domain identification and filtering. Quality control procedures included: - Reformatting of corpus structure for consistency (particularly EURLEX) - Removal of noisy or poorly aligned segments - Deduplication of exact matches - Validation of parallel alignment at the segment level The final corpus is stored in Parquet format (Apache Arrow columnar storage) optimized for efficient access and processing at scale. ### Annotations This dataset contains **no manual annotations**. All content consists of naturally parallel texts from authoritative bilingual sources: **Structural Metadata:** - **Domain labels**: Automatically assigned based on source corpus and encoded in ID prefix - **Source identification**: Embedded in ID structure for provenance tracking - **Alignment level**: Varies by source (sentence, paragraph, or document-level) The corpus preserves the original parallel structure as published by official sources without additional interpretive layers. ### Personal and Sensitive Information The corpus has been subjected to cleaning processes to remove sensitive or identifiable information according to data protection regulations. Documents come from public official and scientific sources. Some texts may contain: **Biomedical Domain:** - Patient information is de-identified in accordance with HIPAA and GDPR standards - Research subjects appear only in aggregate statistical form - Names of researchers, physicians, and institutions in published scientific literature **Legal-Administrative Domain:** - Names of public officials, legislators, and judges in official contexts - References to public institutions and government organizations - Patent inventor names (as required by patent law) - Legal case references with participant anonymization where applicable **Heritage Domain:** - Names of cultural practitioners, artists, and heritage experts in official documentation - References to communities and geographical locations **User Responsibility:** Users are advised to apply additional privacy controls depending on the specific use case, particularly for applications involving personal data processing or sensitive domain applications (medical diagnosis, legal advice). ## Considerations for Using the Data ### Social Impact of Dataset This corpus represents a significant advance in democratizing access to domain-specific machine translation resources for Spanish-English language pairs. It contributes to: - **Improved Access to Specialized Information**: Facilitating cross-lingual access to legal, biomedical, and heritage documentation for researchers, professionals, and citizens - **Research Advancement**: Providing standardized large-scale resources for evaluating document-level translation approaches - **National AI Strategy**: Supporting Spain's strategic objective of developing foundational AI models in Spanish with ethical and transparency standards through the ALIA project - **Reduced Language Barriers**: Enabling better communication in critical domains like healthcare, law, patent documentation, and cultural preservation - **Professional Tool Development**: Supporting the creation of specialized translation tools for legal professionals, medical translators, and heritage workers - **Multilingual Science**: Facilitating Spanish-language participation in international scientific discourse ### Discussion of Biases The corpus reflects inherent biases from its source materials and domains: **Domain-Specific Biases:** **Biomedical Domain:** - Predominantly reflects Western medical perspectives and research traditions - Over-representation of clinical research from high-income countries - Potential under-representation of traditional or alternative medical practices - English source texts may reflect Anglo-American medical terminology **Legal-Administrative Domain:** - Reflects primarily EU and UN institutional language and legal frameworks - May not represent all legal traditions, particularly non-Western systems - Patent documentation biased toward European and international patent systems - Administrative language reflects specific bureaucratic conventions **Heritage Domain:** - Limited by availability of digitized and translated heritage documentation - Possible over-representation of officially recognized heritage over grassroots practices - May under-represent certain cultural perspectives or minority communities - Selection bias toward heritage deemed worthy of official documentation **Language Biases:** - **Spanish Varieties**: European Spanish may be over-represented compared to Latin American varieties, particularly in EU and PubMed sources - **Register**: Formal and technical register dominates across all domains - **Terminology**: Technical terminology may reflect specific translation conventions from source institutions - **Translation Direction**: Some sources may be originally in English with Spanish translations, potentially affecting naturalness **Temporal Biases:** - More recent documents are better represented due to digitization availability - Historical terminology evolution may not be fully captured - Contemporary issues and concepts may be over-represented **Socioeconomic Biases:** - Sources primarily from institutional and governmental contexts - May under-represent perspectives from developing regions - Professional and academic language dominates over colloquial usage ### Other Known Limitations **Data Quality:** - **OCR Errors**: Historical documents may contain optical character recognition errors - **Translation Quality**: Original translation quality varies by source and may not always meet professional standards - **Alignment Precision**: Some segments may have approximate rather than exact alignment - **Formatting Artifacts**: Residual formatting issues from document conversion processes **Temporal Coverage:** - Coverage varies significantly by source - More complete for recent years (2000-2025) than historical periods - Some domains have better temporal distribution than others **Domain Specificity:** - Vocabulary is limited to three specialized domains - Does not generalize to other Spanish-English translation tasks (e.g., news, social media, conversational) - Technical terminology may be too specialized for general-purpose translation **Text Level Variability:** - Not all sources provide consistent document-level segmentation - Some sources artificially segment continuous documents - Sentence-level alignments predominate despite document-level emphasis **Alignment Granularity:** - While document-level translation is prioritized, many sources only provide sentence-level alignments - Mixed granularity across sources may affect training consistency **Heritage Domain Limitations:** - Smallest domain by volume - May benefit from additional data collection or augmentation - Limited coverage of certain heritage types or regions **Source Diversity:** - Some domains dominated by specific sources (e.g., UNPC in legal-administrative) - Uneven distribution across source types - Potential for domain-specific overfitting during training --- **Contact:** [ALIA Project](https://www.alia.gob.es/) - [SINAI Research Group](https://sinai.ujaen.es) - [Universidad de Jaén](https://www.ujaen.es/) **More Information:** [SINAI Research Group](https://sinai.ujaen.es) | [ALIA-UJA Project](https://github.com/sinai-uja/ALIA-UJA)