DLVGen: A Dual Latent Variable Approach to Personalized Dialogue Generation

• URL: http://arxiv.org/abs/2111.11363v1
• Date: Mon, 22 Nov 2021 17:21:21 GMT
• Title: DLVGen: A Dual Latent Variable Approach to Personalized Dialogue Generation
• Authors: Jing Yang Lee, Kong Aik Lee, Woon Seng Gan
• Abstract summary: We propose a Dual Latent Variable Generator (DLVGen) capable of generating personalized dialogue. Unlike prior work, DLVGen models the latent distribution over potential responses as well as the latent distribution over the agent's potential persona. Empirical results show that DLVGen is capable of generating diverse responses which accurately incorporate the agent's persona.
• Score: 28.721411816698563
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The generation of personalized dialogue is vital to natural and human-like conversation. Typically, personalized dialogue generation models involve conditioning the generated response on the dialogue history and a representation of the persona/personality of the interlocutor. As it is impractical to obtain the persona/personality representations for every interlocutor, recent works have explored the possibility of generating personalized dialogue by finetuning the model with dialogue examples corresponding to a given persona instead. However, in real-world implementations, a sufficient number of corresponding dialogue examples are also rarely available. Hence, in this paper, we propose a Dual Latent Variable Generator (DLVGen) capable of generating personalized dialogue in the absence of any persona/personality information or any corresponding dialogue examples. Unlike prior work, DLVGen models the latent distribution over potential responses as well as the latent distribution over the agent's potential persona. During inference, latent variables are sampled from both distributions and fed into the decoder. Empirical results show that DLVGen is capable of generating diverse responses which accurately incorporate the agent's persona.

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