论文标题
改进角色接地对话数据集的双重任务框架
Dual Task Framework for Improving Persona-grounded Dialogue Dataset
论文作者
论文摘要
本文介绍了一种简单但有效的以数据为中心的方法，用于改善角色条件对话代理的任务。以前以模型为中心的方法无疑取决于原始的众包基准数据集，例如角色chat。相比之下，我们旨在修复基准测试中的注释工件，这在任何对话模型上都适用于任何对话模型。具体而言，我们通过利用两个任务的原始偶尔结构来增强相关角色以改善对话数据集/代理，从而预测基于彼此的对话响应和角色。关于角色chat的实验表明，我们的方法在准确性方面优于预先训练的LMS 11.7点增益。
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.
