Model-Based Simulation for Optimising Smart Reply
Benjamin Towle Ke Zhou
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.