ViLexNorm / README.md
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metadata
task_categories:
  - token-classification
language:
  - vi

Dataset Card for ViLexNorm

1. Dataset Summary

ViLexNorm is a Vietnamese lexical normalization corpus of 10,467 comment pairs, each comprising an original noisy social‑media comment and its normalized counterpart. In this unified version, all pairs are merged into one CSV with a type column indicating train / dev / test, and two additional columns input/output containing the tokenized forms.

2. Supported Tasks and Metrics

  • Primary Task: Sequence‑to‑sequence lexical normalization

  • Metric:

    • Error Reduction Rate (ERR) (van der Goot 2019)
    • Token‑level accuracy

3. Languages

  • Vietnamese

4. Dataset Structure

Column Type Description
original string The raw, unnormalized comment.
normalized string The corrected, normalized comment.
input list Tokenized original text (list of strings).
output list Tokenized normalized text (list of strings).
type string Split: train / validation / test.
dataset string Always ViLexNorm for provenance.

5. Data Fields

  • original (str): Noisy input sentence.
  • normalized (str): Human‑annotated normalized sentence.
  • input (List[str]): Token list of original.
  • output (List[str]): Token list of normalized.
  • type (str): Which split the example belongs to.
  • dataset (str): Always ViLexNorm.

6. Usage

from datasets import load_dataset

ds = load_dataset("visolex/ViLexNorm")

train = ds.filter(lambda ex: ex["type"] == "train")
val   = ds.filter(lambda ex: ex["type"] == "dev")
test  = ds.filter(lambda ex: ex["type"] == "test")

print(train[0])

7. Source & Links

8. Contact Information

9. Licensing and Citation

License

Released under CC BY‑NC‑SA 4.0 (Creative Commons Attribution‑NonCommercial‑ShareAlike 4.0 International).

How to Cite

@inproceedings{nguyen-etal-2024-vilexnorm,
  title     = {ViLexNorm: A Lexical Normalization Corpus for Vietnamese Social Media Text},
  author    = {Nguyen, Thanh-Nhi and Le, Thanh-Phong and Nguyen, Kiet},
  booktitle = {Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = mar,
  year      = {2024},
  address   = {St. Julian's, Malta},
  publisher = {Association for Computational Linguistics},
  url       = {https://aclanthology.org/2024.eacl-long.85},
  pages     = {1421--1437}
}