You Impress Me: Dialogue Generation via Mutual Persona Perception

By Qian Liu, Yihong Chen, Bei Chen, Jian-Guang Lou, Zixuan Chen, Bin Zhou, Dongmei Zhang. 2020

187 citations recorded by the source corpus.

Despite the continuing efforts to improve the engagingness and consistency of chit-chat dialogue systems, the majority of current work simply focus on mimicking human-like responses, leaving understudied the aspects of modeling understanding between interlocutors. The research in cognitive science, instead, suggests that understanding is an essential signal for a high-quality chit-chat conversation. Motivated by this, we propose Pˆ2 Bot, a transmitter-receiver based framework with the aim of explicitly modeling understanding. Specifically, Pˆ2 Bot incorporates mutual persona perception to enhance the quality of personalized dialogue generation. Experiments on a large public dataset, Persona-Chat, demonstrate the effectiveness of our approach, with a considerable boost over the state-of-the-art baselines across both automatic metrics and human evaluations.

Subfields: Artificial Intelligence, Human-Computer Interaction

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Citations in the corpus

Lifelong and Continual Learning Dialogue Systems: Learning during Conversation (2021, methodology): “The ﬁrst part of the deﬁnition is taken from (Chen and Liu 2018) based on the early work in (Thrun 1998; Silver, Yang, and Li 2013; Ruvolo and Eaton 2013; Chen and Liu 2014) and the second part of on-the-job learning is proposed in (Liu 2020).”
Recent advances in deep learning based dialogue systems: a systematic survey (2021, methodology): “Likewise, a commonsense knowledge graph was used by Liu et al. (2020c) to lead the conversation topic and make recommendations.”
PERSONACHATGEN: Generating Personalized Dialogues using GPT-3 (2022, background): “Liu et al. (2020) improved the quality of generated responses by incorporating mutual persona perception.”
SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents (2023, background): “…dialogue systems are designed to have “chit-chat” with users (Kann et al., 2022; Kim et al., 2023), often incorporate personal information to make conversations more engaging (Zhang et al., 2018a; Liu et al., 2020; Baha et al., 2023; Do ˘ gru ¨ oz & Skantze, 2021; Skantze & Do ˘ gru ¨ oz, 2023).”