SEACrowd/vilexnorm
收藏ViLexNorm 数据集概述
基本信息
- 名称: ViLexNorm
- 语言: 越南语 (vie)
- 任务类别: 多词法规范化 (multilexnorm)
- 标签: 多词法规范化 (multilexnorm)
- 许可证: Creative Commons Attribution Non Commercial Share Alike 4.0 (cc-by-nc-sa-4.0)
数据集描述
- 内容: 包含10,467对越南语评论，用于词法规范化任务。
- 划分: 数据集分为训练集、开发集和测试集，比例为8:1:1。
使用方法
使用 datasets 库
python from datasets import load_dataset dset = datasets.load_dataset("SEACrowd/vilexnorm", trust_remote_code=True)
使用 seacrowd 库
python import seacrowd as sc
使用默认配置加载数据集
dset = sc.load_dataset("vilexnorm", schema="seacrowd")
查看数据集的所有可用子集（配置名称）
print(sc.available_config_names("vilexnorm"))
使用特定配置加载数据集
dset = sc.load_dataset_by_config_name(config_name="<config_name>")
数据集版本
- 源版本: 1.0.0
- SEACrowd 版本: 2024.06.20
引用
bibtex @inproceedings{nguyen-etal-2024-vilexnorm, title = "{V}i{L}ex{N}orm: A Lexical Normalization Corpus for {V}ietnamese Social Media Text", author = "Nguyen, Thanh-Nhi and Le, Thanh-Phong and Nguyen, Kiet", editor = "Graham, Yvette and Purver, Matthew", 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", abstract = "Lexical normalization, a fundamental task in Natural Language Processing (NLP), involves the transformation of words into their canonical forms. This process has been proven to benefit various downstream NLP tasks greatly. In this work, we introduce Vietnamese Lexical Normalization (ViLexNorm), the first-ever corpus developed for the Vietnamese lexical normalization task. The corpus comprises over 10,000 pairs of sentences meticulously annotated by human annotators, sourced from public comments on Vietnam{}s most popular social media platforms. Various methods were used to evaluate our corpus, and the best-performing system achieved a result of 57.74% using the Error Reduction Rate (ERR) metric (van der Goot, 2019a) with the Leave-As-Is (LAI) baseline. For extrinsic evaluation, employing the model trained on ViLexNorm demonstrates the positive impact of the Vietnamese lexical normalization task on other NLP tasks. Our corpus is publicly available exclusively for research purposes.", }
@article{lovenia2024seacrowd, title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages}, author={Holy Lovenia and Rahmad Mahendra and Salsabil Maulana Akbar and Lester James V. Miranda and Jennifer Santoso and Elyanah Aco and Akhdan Fadhilah and Jonibek Mansurov and Joseph Marvin Imperial and Onno P. Kampman and Joel Ruben Antony Moniz and Muhammad Ravi Shulthan Habibi and Frederikus Hudi and Railey Montalan and Ryan Ignatius and Joanito Agili Lopo and William Nixon and Börje F. Karlsson and James Jaya and Ryandito Diandaru and Yuze Gao and Patrick Amadeus and Bin Wang and Jan Christian Blaise Cruz and Chenxi Whitehouse and Ivan Halim Parmonangan and Maria Khelli and Wenyu Zhang and Lucky Susanto and Reynard Adha Ryanda and Sonny Lazuardi Hermawan and Dan John Velasco and Muhammad Dehan Al Kautsar and Willy Fitra Hendria and Yasmin Moslem and Noah Flynn and Muhammad Farid Adilazuarda and Haochen Li and Johanes Lee and R. Damanhuri and Shuo Sun and Muhammad Reza Qorib and Amirbek Djanibekov and Wei Qi Leong and Quyet V. Do and Niklas Muennighoff and Tanrada Pansuwan and Ilham Firdausi Putra and Yan Xu and Ngee Chia Tai and Ayu Purwarianti and Sebastian Ruder and William Tjhi and Peerat Limkonchotiwat and Alham Fikri Aji and Sedrick Keh and Genta Indra Winata and Ruochen Zhang and Fajri Koto and Zheng-Xin Yong and Samuel Cahyawijaya}, year={2024}, eprint={2406.10118}, journal={arXiv preprint arXiv: 2406.10118} }
