论文标题
听众的社会身份在个性化的响应产生中很重要
Listener's Social Identity Matters in Personalised Response Generation
论文作者
论文摘要
个性化的响应生成可以通过将生成器分配为社会身份来产生类似人类的响应。但是，实用主义理论表明，人类不仅基于他们是谁，而且还与他们交谈的人调整说话方式。换句话说，在建模个性化对话时，如果我们还考虑听众的社会身份，这可能是有利的。为了验证这一想法，我们将性别用作社会变量的典型示例，以研究听众的身份如何影响社交媒体上中文对话中使用的语言。另外，我们构建个性化发电机。实验结果表明，听众的身份在响应的语言使用中确实很重要，并且响应发生器可以捕获这种语言使用差异。更有趣的是，通过对听众的身份进行建模，个性化的响应生成器在自己的身份方面表现更好。
Personalised response generation enables generating human-like responses by means of assigning the generator a social identity. However, pragmatics theory suggests that human beings adjust the way of speaking based on not only who they are but also whom they are talking to. In other words, when modelling personalised dialogues, it might be favourable if we also take the listener's social identity into consideration. To validate this idea, we use gender as a typical example of a social variable to investigate how the listener's identity influences the language used in Chinese dialogues on social media. Also, we build personalised generators. The experiment results demonstrate that the listener's identity indeed matters in the language use of responses and that the response generator can capture such differences in language use. More interestingly, by additionally modelling the listener's identity, the personalised response generator performs better in its own identity.
