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        <title>DREAM: A Challenge Data Set and Models for Dialogue-Based Reading Comprehension</title>
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        <namePart type="given">Kai</namePart>
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    <abstract>We present DREAM, the first dialogue-based multiple-choice reading comprehension data set. Collected from English as a Foreign Language examinations designed by human experts to evaluate the comprehension level of Chinese learners of English, our data set contains 10,197 multiple-choice questions for 6,444 dialogues. In contrast to existing reading comprehension data sets, DREAM is the first to focus on in-depth multi-turn multi-party dialogue understanding. DREAM is likely to present significant challenges for existing reading comprehension systems: 84% of answers are non-extractive, 85% of questions require reasoning beyond a single sentence, and 34% of questions also involve commonsense knowledge. We apply several popular neural reading comprehension models that primarily exploit surface information within the text and find them to, at best, just barely outperform a rule-based approach. We next investigate the effects of incorporating dialogue structure and different kinds of general world knowledge into both rule-based and (neural and non-neural) machine learning-based reading comprehension models. Experimental results on the DREAM data set show the effectiveness of dialogue structure and general world knowledge. DREAM is available at https://dataset.org/dream/.</abstract>
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%0 Journal Article
%T DREAM: A Challenge Data Set and Models for Dialogue-Based Reading Comprehension
%A Sun, Kai
%A Yu, Dian
%A Chen, Jianshu
%A Yu, Dong
%A Choi, Yejin
%A Cardie, Claire
%J Transactions of the Association for Computational Linguistics
%D 2019
%V 7
%I MIT Press
%C Cambridge, MA
%F sun-etal-2019-dream
%X We present DREAM, the first dialogue-based multiple-choice reading comprehension data set. Collected from English as a Foreign Language examinations designed by human experts to evaluate the comprehension level of Chinese learners of English, our data set contains 10,197 multiple-choice questions for 6,444 dialogues. In contrast to existing reading comprehension data sets, DREAM is the first to focus on in-depth multi-turn multi-party dialogue understanding. DREAM is likely to present significant challenges for existing reading comprehension systems: 84% of answers are non-extractive, 85% of questions require reasoning beyond a single sentence, and 34% of questions also involve commonsense knowledge. We apply several popular neural reading comprehension models that primarily exploit surface information within the text and find them to, at best, just barely outperform a rule-based approach. We next investigate the effects of incorporating dialogue structure and different kinds of general world knowledge into both rule-based and (neural and non-neural) machine learning-based reading comprehension models. Experimental results on the DREAM data set show the effectiveness of dialogue structure and general world knowledge. DREAM is available at https://dataset.org/dream/.
%R 10.1162/tacl_a_00264
%U https://aclanthology.org/Q19-1014
%U https://doi.org/10.1162/tacl_a_00264
%P 217-231

Markdown (Informal)

[DREAM: A Challenge Data Set and Models for Dialogue-Based Reading Comprehension](https://aclanthology.org/Q19-1014) (Sun et al., TACL 2019)

• DREAM: A Challenge Data Set and Models for Dialogue-Based Reading Comprehension (Sun et al., TACL 2019)

ACL

• Kai Sun, Dian Yu, Jianshu Chen, Dong Yu, Yejin Choi, and Claire Cardie. 2019. DREAM: A Challenge Data Set and Models for Dialogue-Based Reading Comprehension. Transactions of the Association for Computational Linguistics, 7:217–231.