One Chatbot Per Person: Creating Personalized Chatbots based on Implicit User Profiles

• URL: http://arxiv.org/abs/2108.09355v1
• Date: Fri, 20 Aug 2021 20:33:12 GMT
• Title: One Chatbot Per Person: Creating Personalized Chatbots based on Implicit User Profiles
• Authors: Zhengyi Ma, Zhicheng Dou, Yutao Zhu, Hanxun Zhong, Ji-Rong Wen
• Abstract summary: Existing personalized approaches tried to incorporate several text descriptions as explicit user profiles. We train a personalized language model to construct a general user profile from the user's historical responses. We design a personalized decoder to fuse two decoding strategies, including generating a word from the generic vocabulary and copying one word from the user's personalized vocabulary.
• Score: 31.432585994256375
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Personalized chatbots focus on endowing chatbots with a consistent personality to behave like real users, give more informative responses, and further act as personal assistants. Existing personalized approaches tried to incorporate several text descriptions as explicit user profiles. However, the acquisition of such explicit profiles is expensive and time-consuming, thus being impractical for large-scale real-world applications. Moreover, the restricted predefined profile neglects the language behavior of a real user and cannot be automatically updated together with the change of user interests. In this paper, we propose to learn implicit user profiles automatically from large-scale user dialogue history for building personalized chatbots. Specifically, leveraging the benefits of Transformer on language understanding, we train a personalized language model to construct a general user profile from the user's historical responses. To highlight the relevant historical responses to the input post, we further establish a key-value memory network of historical post-response pairs, and build a dynamic post-aware user profile. The dynamic profile mainly describes what and how the user has responded to similar posts in history. To explicitly utilize users' frequently used words, we design a personalized decoder to fuse two decoding strategies, including generating a word from the generic vocabulary and copying one word from the user's personalized vocabulary. Experiments on two real-world datasets show the significant improvement of our model compared with existing methods.

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