1. Paper title

MuTual: A Dataset for Multi-Turn Dialogue Reasoning

2. link

https://www.aclweb.org/anthology/2020.acl-main.130.pdf

3. 摘要

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.

4. 要解决什么问题

非任务型对话系统由于缺乏推断能力能经常出现逻辑错误。

5. 作者的主要贡献

MuTual：一个用于训练推断能力的数据库。
使用英文听力考虑的题目加上人工标注。

6. 得到了什么结果

STOA在MuTal上正确率远低于人类得分，因此在推断方面有提升空间。

7. 关键字

数据集