论文标题
生成，删除和重写：改善对话生成角色一致性的三阶段框架
Generate, Delete and Rewrite: A Three-Stage Framework for Improving Persona Consistency of Dialogue Generation
论文作者
论文摘要
在对话中保持一致的性格对于人类来说是很自然的，但对于机器来说仍然是一项不平凡的任务。因此，介绍了基于角色的对话生成任务，以通过将明确的角色文本纳入对话生成模型中来解决人格矛盾的问题。尽管现有基于角色的模型在产生类似人类的响应方面取得了成功，但它们的一个阶段解码框架几乎无法避免产生不一致的角色单词。在这项工作中，我们引入了一个三阶段的框架，该框架采用生成的降落 - 剥离机制来从生成的响应原型中删除不一致的单词，并将其进一步将其重写为与个性相吻合的单词。我们通过人类和自动指标进行评估。角色chat数据集的实验表明，我们的方法取得了良好的性能。
Maintaining a consistent personality in conversations is quite natural for human beings, but is still a non-trivial task for machines. The persona-based dialogue generation task is thus introduced to tackle the personality-inconsistent problem by incorporating explicit persona text into dialogue generation models. Despite the success of existing persona-based models on generating human-like responses, their one-stage decoding framework can hardly avoid the generation of inconsistent persona words. In this work, we introduce a three-stage framework that employs a generate-delete-rewrite mechanism to delete inconsistent words from a generated response prototype and further rewrite it to a personality-consistent one. We carry out evaluations by both human and automatic metrics. Experiments on the Persona-Chat dataset show that our approach achieves good performance.
