Long-term Control for Dialogue Generation: Methods and Evaluation

Ramakrishnan, Ramya; Narangodage, Hashan Buddhika; Schilman, Mauro; Weinberger, Kilian Q.; McDonald, Ryan

doi:10.48550/arxiv.2205.07352

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“…Many works (Chen et al, 2022;Zhou et al, 2017) focus on generating relevant and diverse responses given a dialogue context. Controlled response generation (Wu et al, 2021;Ramakrishnan et al, 2022;Gupta et al, 2020) goes further to control responses towards defined styles or lexical constraints like inclusion of specific words or phrases. These prior works have used similar seq2seq models for response generation, which we apply to the related problem of multi-step action prediction.…”

Section: Related Workmentioning

confidence: 99%

Long-term Control for Dialogue Generation: Methods and Evaluation

Ramakrishnan¹,

Narangodage²,

Schilman³

et al. 2022

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Current approaches for controlling dialogue response generation are primarily focused on high-level attributes like style, sentiment, or topic. In this work, we focus on constrained long-term dialogue generation, which involves more fine-grained control and requires a given set of control words to appear in generated responses. This setting requires a model to not only consider the generation of these control words in the immediate context, but also produce utterances that will encourage the generation of the words at some time in the (possibly distant) future. We define the problem of constrained long-term control for dialogue generation, identify gaps in current methods for evaluation, and propose new metrics that better measure long-term control. We also propose a retrieval-augmented method that improves performance of long-term controlled generation via logit modification techniques. We show through experiments on three task-oriented dialogue datasets that our metrics better assess dialogue control relative to current alternatives and that our method outperforms state-of-theart constrained generation baselines. 1

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