  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.
