论文标题
推论：推断说话者的同情对话生成的意图
InferEM: Inferring the Speaker's Intention for Empathetic Dialogue Generation
论文作者
论文摘要
当前的移情响应生成方法通常直接编码整个对话记录，并将输出放入解码器中以产生友好的反馈。这些方法着重于建模上下文信息，但忽略了捕获说话者的直接意图。我们认为，对话中的最后一句话从经验上传达了演讲者的意图。因此，我们提出了一个新型模型，名为“ Sealtem”，以产生同理心反应产生。我们分别编码了最后的话语，并通过基于多头注意的意图融合模块将其与整个对话融合在一起，以捕获说话者的意图。此外，我们利用以前的话语来预测最后的话语，这模拟了人类的心理学，以猜测对话者可能会说什么。为了平衡话语预测和响应产生的优化速率，为Sealem设计了多任务学习策略。实验结果证明了Sexem在改善插节表达方面的合理性和有效性。
Current approaches to empathetic response generation typically encode the entire dialogue history directly and put the output into a decoder to generate friendly feedback. These methods focus on modelling contextual information but neglect capturing the direct intention of the speaker. We argue that the last utterance in the dialogue empirically conveys the intention of the speaker. Consequently, we propose a novel model named InferEM for empathetic response generation. We separately encode the last utterance and fuse it with the entire dialogue through the multi-head attention based intention fusion module to capture the speaker's intention. Besides, we utilize previous utterances to predict the last utterance, which simulates human's psychology to guess what the interlocutor may speak in advance. To balance the optimizing rates of the utterance prediction and response generation, a multi-task learning strategy is designed for InferEM. Experimental results demonstrate the plausibility and validity of InferEM in improving empathetic expression.
