We present DREAM, the first dialogue-based multiple-choice reading
comprehension dataset. Collected from English-as-a-foreign-language
examinations designed by human experts to evaluate the comprehension level of
Chinese learners of English, our dataset contains 10,197 multiple-choice
questions for 6,444 dialogues. In contrast to existing reading comprehension
datasets, 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 dataset show the
effectiveness of dialogue structure and general world knowledge. DREAM will be
available at this https URL

DREAM 是第一个基于会话的多项选择阅读理解数据集，包含 10197 个问题，针对 6444 个对话，84％的答案是非提取式的，85％的问题需要超出单个句子的推理，34％的问题涉及常识知识。Neural networks 的应用对数据集的阅读理解性能有很大改善。