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.

本文介绍了一种简单而有效的数据中心方法，用于改善个性化对话代理。通过利用针对两个任务的原始 - 对偶结构（预测对话响应和个人资料之间的联系），我们增强了相关人物角色，以改进对话数据集 / 代理，并修复了基准数据集的注释问题，其巨大地增加了模型的精度，体现在 Persona-Chat 上的实验中，我们的方法在准确性上比预训练 LM 高出 11.7 个百分点。