Model-Based Simulation for Optimising Smart Reply

• URL: http://arxiv.org/abs/2305.16852v1
• Date: Fri, 26 May 2023 12:04:33 GMT
• Title: Model-Based Simulation for Optimising Smart Reply
• Authors: Benjamin Towle and Ke Zhou
• Abstract summary: Smart Reply (SR) systems present a user with a set of replies, of which one can be selected in place of having to type out a response. Previous work has focused largely on post-hoc diversification, rather than explicitly learning to predict sets of responses. We present a novel method SimSR, that employs model-based simulation to discover high-value response sets.
• Score: 3.615981646205045
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Smart Reply (SR) systems present a user with a set of replies, of which one can be selected in place of having to type out a response. To perform well at this task, a system should be able to effectively present the user with a diverse set of options, to maximise the chance that at least one of them conveys the user's desired response. This is a significant challenge, due to the lack of datasets containing sets of responses to learn from. Resultantly, previous work has focused largely on post-hoc diversification, rather than explicitly learning to predict sets of responses. Motivated by this problem, we present a novel method SimSR, that employs model-based simulation to discover high-value response sets, through simulating possible user responses with a learned world model. Unlike previous approaches, this allows our method to directly optimise the end-goal of SR--maximising the relevance of at least one of the predicted replies. Empirically on two public datasets, when compared to SoTA baselines, our method achieves up to 21% and 18% improvement in ROUGE score and Self-ROUGE score respectively.

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