MuTual: A Dataset for Multi-Turn Dialogue Reasoning

• URL: http://arxiv.org/abs/2004.04494v1
• Date: Thu, 9 Apr 2020 11:42:33 GMT
• Title: MuTual: A Dataset for Multi-Turn Dialogue Reasoning
• Authors: Leyang Cui, Yu Wu, Shujie Liu, Yue Zhang, Ming Zhou
• Abstract summary: MuTual is a novel dataset for Multi-Turn dialogue Reasoning. It consists of 8,860 manually annotated dialogues based on Chinese student English listening comprehension exams. We show that state-of-the-art methods only reach 71%, which is far behind the human performance of 94%.
• Score: 53.10434937685962
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Non-task oriented dialogue systems have achieved great success in recent years due to largely accessible conversation data and the development of deep learning techniques. Given a context, current systems are able to yield a relevant and fluent response, but sometimes make logical mistakes because of weak reasoning capabilities. To facilitate the conversation reasoning research, we introduce MuTual, a novel dataset for Multi-Turn dialogue Reasoning, consisting of 8,860 manually annotated dialogues based on Chinese student English listening comprehension exams. Compared to previous benchmarks for non-task oriented dialogue systems, MuTual is much more challenging since it requires a model that can handle various reasoning problems. Empirical results show that state-of-the-art methods only reach 71%, which is far behind the human performance of 94%, indicating that there is ample room for improving reasoning ability. MuTual is available at https://github.com/Nealcly/MuTual.

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