论文标题
EMP-RFT：通过识别发音之间的特征过渡来产生同情响应
Emp-RFT: Empathetic Response Generation via Recognizing Feature Transitions between Utterances
论文作者
论文摘要
多转向善解对话中的每一个话语都具有情感，关键字和话语级别的含义之类的特征。话语之间的特征过渡自然发生。但是，现有方法无法感知过渡，因为它们在粗粒级别提取上下文的特征。为了解决上述问题，我们提出了一种新颖的方法，可以识别话语之间的特征过渡，这有助于理解对话流并更好地掌握需要注意的话语的特征。此外，我们引入了一种响应生成策略，以帮助专注于与适当特征有关的情绪和关键字，并在产生响应时。实验结果表明，我们的方法的表现优于基准，尤其是在多转对话方面取得了重大改进。
Each utterance in multi-turn empathetic dialogues has features such as emotion, keywords, and utterance-level meaning. Feature transitions between utterances occur naturally. However, existing approaches fail to perceive the transitions because they extract features for the context at the coarse-grained level. To solve the above issue, we propose a novel approach of recognizing feature transitions between utterances, which helps understand the dialogue flow and better grasp the features of utterance that needs attention. Also, we introduce a response generation strategy to help focus on emotion and keywords related to appropriate features when generating responses. Experimental results show that our approach outperforms baselines and especially, achieves significant improvements on multi-turn dialogues.
