An Empirical Study of Multitask Learning to Improve Open Domain Dialogue Systems

• URL: http://arxiv.org/abs/2304.08115v1
• Date: Mon, 17 Apr 2023 09:44:56 GMT
• Title: An Empirical Study of Multitask Learning to Improve Open Domain Dialogue Systems
• Authors: Mehrdad Farahani, Richard Johansson
• Abstract summary: This paper describes an investigation where four different auxiliary tasks are added to small and medium-sized GPT-2 models. The results show that the introduction of the new auxiliary tasks leads to small but consistent improvement in evaluations of the investigated models.
• Score: 0.13706331473063876
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autoregressive models used to generate responses in open-domain dialogue systems often struggle to take long-term context into account and to maintain consistency over a dialogue. Previous research in open-domain dialogue generation has shown that the use of \emph{auxiliary tasks} can introduce inductive biases that encourage the model to improve these qualities. However, most previous research has focused on encoder-only or encoder/decoder models, while the use of auxiliary tasks in \emph{decoder-only} autoregressive models is under-explored. This paper describes an investigation where four different auxiliary tasks are added to small and medium-sized GPT-2 models fine-tuned on the PersonaChat and DailyDialog datasets. The results show that the introduction of the new auxiliary tasks leads to small but consistent improvement in evaluations of the investigated models.

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