hsseinmz/arcd

阿拉伯语阅读理解数据集（ARCD）由1,395个问题组成，这些问题是由众包工作者在维基百科文章上提出的。数据集的任务类别是问答（question-answering），具体任务为抽取式问答（extractive-qa）。数据集的语言为阿拉伯语（ar），并且是单语种（monolingual）。数据集的大小在1K到10K之间，分为训练集和验证集，分别包含693和702个样本。数据集的字段包括id、title、context、question和answers，其中answers包含text和answer_start两个子字段。

原始信息汇总

数据集概述

名称: ARCD (Arabic Reading Comprehension Dataset)

语言: 阿拉伯语 (ar-SA)

许可证: MIT

多语言性: 单语种

大小: 1K<n<10K

源数据: 原始数据

任务类别: 问答 (extractive-qa)

数据集结构

数据实例

• 字段:
  • id: 字符串
  • title: 字符串
  • context: 字符串
  • question: 字符串
  • answers: 字典，包含
    • text: 字符串
    • answer_start: 整数

数据分割

名称	训练	验证
plain_text	693	702

数据集创建

注释者

• 注释创建者: 众包
• 语言创建者: 众包

许可证信息

• 许可证: MIT

引用信息

@inproceedings{mozannar-etal-2019-neural, title = "Neural {A}rabic Question Answering", author = "Mozannar, Hussein and Maamary, Elie and El Hajal, Karl and Hajj, Hazem", booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop", month = aug, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/W19-4612", doi = "10.18653/v1/W19-4612", pages = "108--118", abstract = "This paper tackles the problem of open domain factual Arabic question answering (QA) using Wikipedia as our knowledge source. This constrains the answer of any question to be a span of text in Wikipedia. Open domain QA for Arabic entails three challenges: annotated QA datasets in Arabic, large scale efficient information retrieval and machine reading comprehension. To deal with the lack of Arabic QA datasets we present the Arabic Reading Comprehension Dataset (ARCD) composed of 1,395 questions posed by crowdworkers on Wikipedia articles, and a machine translation of the Stanford Question Answering Dataset (Arabic-SQuAD). Our system for open domain question answering in Arabic (SOQAL) is based on two components: (1) a document retriever using a hierarchical TF-IDF approach and (2) a neural reading comprehension model using the pre-trained bi-directional transformer BERT. Our experiments on ARCD indicate the effectiveness of our approach with our BERT-based reader achieving a 61.3 F1 score, and our open domain system SOQAL achieving a 27.6 F1 score.", }