SEACrowd/xquadr

XQuAD-R是XQuAD数据集的检索版本，属于LAReQA基准测试的一部分。与XQuAD类似，XQuAD-R是一个11种语言的并行数据集，每个问题（约1200个）在11种语言中都有对应的答案，并且问题可以与不同语言的答案匹配。数据集将XQuAD中的跨度标记任务转换为检索任务，通过将每个上下文段落分解为句子，并将每个句子视为可能的答案。每种语言大约有1000个候选答案。

XQuAD-R is a retrieval version of the XQuAD dataset (a cross-lingual extractive QA dataset) that is a part of the LAReQA benchmark. Like XQuAD, XQUAD-R is an 11-way parallel dataset, where each question (out of around 1200) appears in 11 different languages and has 11 parallel correct answers across the languages. It is designed so as to include parallel QA pairs across languages, allowing questions to be matched with answers from different languages. The span-tagging task in XQuAD is converted into a retrieval task by breaking up each contextual paragraph into sentences, and treating each sentence as a possible target answer. There are around 1000 candidate answers in each language.

原始信息汇总

XQuAD-R 数据集概述

基本信息

• 名称: XQuAD-R
• 许可证: Creative Commons Attribution Share Alike 4.0 (cc-by-sa-4.0)
• 语言:
  • 泰语 (tha)
  • 越南语 (vie)
• 任务类别: 问答检索 (Question Answering Retrieval)

数据集描述

• 来源: XQuAD-R 是 XQuAD 数据集的检索版本，属于 LAReQA 基准测试的一部分。
• 特点:
  • 11 种语言的平行数据集，每个问题在 11 种语言中都有对应的正确答案。
  • 每个问题约有 1200 个，每种语言约有 1000 个候选答案。
  • 将 XQuAD 的跨度标记任务转换为检索任务，将每个上下文段落分解为句子，并将每个句子视为可能的目标答案。

使用方法

使用 datasets 库

python from datasets import load_dataset dset = datasets.load_dataset("SEACrowd/xquadr", 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("xquadr", schema="seacrowd")

检查数据集的所有可用子集（配置名称）

print(sc.available_config_names("xquadr"))

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: 1.1.0
• SEACrowd 版本: 2024.06.20

引用

bibtex @article{,@inproceedings{roy-etal-2020-lareqa, title = "{LAR}e{QA}: Language-Agnostic Answer Retrieval from a Multilingual Pool", author = "Roy, Uma and Constant, Noah and Al-Rfou, Rami and Barua, Aditya and Phillips, Aaron and Yang, Yinfei", editor = "Webber, Bonnie and Cohn, Trevor and He, Yulan and Liu, Yang", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.emnlp-main.477", doi = "10.18653/v1/2020.emnlp-main.477", pages = "5919--5930", }

@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} }