论文标题
Emphi：以人类的意图产生善解人意的反应
EmpHi: Generating Empathetic Responses with Human-like Intents
论文作者
论文摘要
在同理心对话中，人类表达了同情心的同情心。然而，大多数现有的同理心对话方法都缺乏善解人意的意图，从而导致单调同理心。为了解决善解人意对话模型和人类之间同情意图分布的偏见，我们提出了一种新型模型，以产生善解人意的反应，并以人为一致的同情意图（简称为emphi）。确切地说，Emphi通过离散的潜在变量了解了潜在的同情意图的分布，然后结合了隐式和明确的意图表示，以与各种移情意图产生响应。实验表明，在自动和人类评估上，Emphi在同理心，相关性和多样性方面都优于最先进的模型。此外，案例研究表明了我们模型的高解释性和出色表现。
In empathetic conversations, humans express their empathy to others with empathetic intents. However, most existing empathetic conversational methods suffer from a lack of empathetic intents, which leads to monotonous empathy. To address the bias of the empathetic intents distribution between empathetic dialogue models and humans, we propose a novel model to generate empathetic responses with human-consistent empathetic intents, EmpHi for short. Precisely, EmpHi learns the distribution of potential empathetic intents with a discrete latent variable, then combines both implicit and explicit intent representation to generate responses with various empathetic intents. Experiments show that EmpHi outperforms state-of-the-art models in terms of empathy, relevance, and diversity on both automatic and human evaluation. Moreover, the case studies demonstrate the high interpretability and outstanding performance of our model.
