论文标题
CDL：课程双重学习，可控制情绪的响应生成
CDL: Curriculum Dual Learning for Emotion-Controllable Response Generation
论文作者
论文摘要
可控制的响应产生是一项有吸引力且有价值的任务，旨在使开放域对话更加善解人意和引人入胜。现有方法主要通过将正则化术语添加到标准的跨透明拷贝损失，从而影响训练过程来增强情绪表达。但是，由于缺乏进一步考虑内容一致性，因此加强了响应生成任务的常见问题，即安全响应。此外，在以前的模型中简单地忽略了可以帮助建模查询与响应之间关系的查询情绪，这将进一步损害连贯性。为了减轻这些问题，我们提出了一个名为“课程双学习”（CDL）的新颖框架，该框架将可控制的响应生成扩展到双重任务，以产生情感响应和情感查询。 CDL利用两个重点关注情感和内容的奖励来提高双重性。此外，它应用了课程学习，以根据表达各种情绪的困难逐渐产生高质量的反应。实验结果表明，CDL在连贯性，多样性和与情绪因素的关系方面显着优于基准。
Emotion-controllable response generation is an attractive and valuable task that aims to make open-domain conversations more empathetic and engaging. Existing methods mainly enhance the emotion expression by adding regularization terms to standard cross-entropy loss and thus influence the training process. However, due to the lack of further consideration of content consistency, the common problem of response generation tasks, safe response, is intensified. Besides, query emotions that can help model the relationship between query and response are simply ignored in previous models, which would further hurt the coherence. To alleviate these problems, we propose a novel framework named Curriculum Dual Learning (CDL) which extends the emotion-controllable response generation to a dual task to generate emotional responses and emotional queries alternatively. CDL utilizes two rewards focusing on emotion and content to improve the duality. Additionally, it applies curriculum learning to gradually generate high-quality responses based on the difficulties of expressing various emotions. Experimental results show that CDL significantly outperforms the baselines in terms of coherence, diversity, and relation to emotion factors.
