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Title: DLVGen: a dual latent variable approach to personalized dialogue generation
Authors: Lee, Jing Yang
Lee, Kong Aik
Gan, Woon-Seng
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Lee, J. Y., Lee, K. A. & Gan, W. (2022). DLVGen: a dual latent variable approach to personalized dialogue generation. 14th International Conference on Agents and Artificial Intelligence (ICAART 2022), 2, 193-202.
metadata.dc.contributor.conference: 14th International Conference on Agents and Artificial Intelligence (ICAART 2022)
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.
ISBN: 978-989-758-547-0
ISSN: 2184-433X
DOI: 10.5220/0010812500003116
Schools: School of Electrical and Electronic Engineering 
Rights: © 2022 SCITEPRESS – Science and Technology Publications, Lda. All rights reserved. This paper was published in Proceedings of 14th International Conference on Agents and Artificial Intelligence (ICAART 2022) and is made available with permission of SCITEPRESS.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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