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arXiv · 2202.05435

Dual Task Framework for Improving Persona-grounded Dialogue Dataset

Minju Kim·Beong-woo Kwak·Youngwook Kim·Hong-in Lee·Seung-won Hwang·Jinyoung Yeo
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Abstract

This paper introduces a simple yet effective data-centric approach for the task of improving persona-conditioned dialogue agents. Prior model-centric approaches unquestioningly depend on the raw crowdsourced benchmark datasets such as Persona-Chat. In contrast, we aim to fix annotation artifacts in benchmarking, which is orthogonally applicable to any dialogue model. Specifically, we augment relevant personas to improve dialogue dataset/agent, by leveraging the primal-dual structure of the two tasks, predicting dialogue responses and personas based on each other. Experiments on Persona-Chat show that our approach outperforms pre-trained LMs by an 11.7 point gain in terms of accuracy.

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Minju Kim, Beong-woo Kwak, Youngwook Kim, Hong-in Lee, Seung-won Hwang, Jinyoung Yeo. 2022-02-11. Dual Task Framework for Improving Persona-grounded Dialogue Dataset. https://arxiv.org/abs/2202.05435

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