Dual Task Framework for Debiasing Persona-grounded Dialogue Dataset
- URL: http://arxiv.org/abs/2202.05435v1
- Date: Fri, 11 Feb 2022 04:08:46 GMT
- Title: Dual Task Framework for Debiasing Persona-grounded Dialogue Dataset
- Authors: Minju Kim, Beong-woo Kwak, Youngwook Kim, Hong-in Lee, Seung-won
Hwang, Jinyoung Yeo
- Abstract summary: We introduce a data-centric approach for the task of improving persona-conditioned dialogue agents.
Specifically, we augment relevant personas to improve dialogue dataset/agent, by leveraging the primal-dual structure of the two tasks.
Experiments on Persona-Chat show that our approach outperforms pre-trained LMs by an 11.7 point gain in terms of accuracy.
- Score: 17.403065663306567
- License: http://creativecommons.org/licenses/by/4.0/
- 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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