QuAC是一个18年发表的数据集,全称是Question Answering in Context. 中文翻译过来就是:结合了语境的问答数据集。实验的baseline 和实验结果的排行榜可以在quac.ai上找到。 这篇论文与以往的数据集最大的不同是: 1. 提问者没有预先看passage,只知道主题,然后跟着主题自由提问。这么做,可以避免提问者受文本影响,...
QuAC（即Question Answering in Context）是用于问答的数据集，包含14,000个众包对话框，涉及学生和教师，旨在探讨和理解隐藏的Wikipedia文本。该数据集中的对话包含两位参与者：一名学生提出了一系列自由格式问题，旨在尽可能多地了解隐藏的Wikipedia文本；一名教师通过提供文本的简短摘录来回答问题。问题通常在...
QuAC (Question Answering in Context)是一个用于评估问答系统的指标。该指标主要关注在给定上下文的情况下,系统是否能够正确回答问题。QuAC使用了一种开放领域的数据集,其中包含问题、上下文和参考答案。系统需要在给定的上下文中理解问题,并生成准确的回答。 QuAC指标主要通过计算系统生成的回答与参考答案之间的相似度来评...
【机器阅读理解数据集】QuAC : Question Answering in Context #开源论文# 本文提出了一个基于上下文的机器阅读理解数据集QuAC,该数据集存在两类人群:Student和Teacher。Student依次提出一系列自由式的问题,而...
Question Answering In Context Question Answering (QA) has long been a promising yet challenging task, and a large number of work has been done in this area. However, simple QA tasks such as extracting answer spans from a given text have been criticized as "shallow pattern recognition", which...
[1] Choi E, He H, Iyyer M, et al. Quac: Question answering in context[J]. arXiv preprint arXiv:1808.07036, 2018. [2] Rajpurkar P, Zhang J, Lopyrev K, et al. Squad: 100,000+ questions for machine comprehension of text[J]. arXiv preprint arXiv:1606.05250, 2016. [3] Reddy...
[1] Choi E, He H, Iyyer M, et al. Quac: Question answering in context[J]. arXiv preprint arXiv:1808.07036, 2018.[2] Rajpurkar P, Zhang J, Lopyrev K, et al. Squad: 100,000+ questions for machine comprehension of text[J]. arXiv preprint arXiv:1606.05250, 2016.[3] Reddy S...
Question Answering in Context is a large-scale dataset that consists of around 14K crowdsourced Question Answering dialogs with 98K question-answer pairs in total. Data instances consist of an interactive dialog between two crowd workers: (1) a student w
[1] Choi E, He H, Iyyer M, et al. Quac: Question answering in context[J]. arXiv preprint arXiv:1808.07036, 2018. [2] Rajpurkar P, Zhang J, Lopyrev K, et al. Squad: 100,000+ questions for machine comprehension of text[J]. arXiv preprint arXiv:1606.05250, 2016. ...
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