EmpHi: Generating Empathetic Responses with Human-like Intents

• URL: http://arxiv.org/abs/2204.12191v1
• Date: Tue, 26 Apr 2022 09:49:49 GMT
• Title: EmpHi: Generating Empathetic Responses with Human-like Intents
• Authors: Mao Yan Chen, Siheng Li, Yujiu Yang
• Abstract summary: We propose a novel model to generate empathetic responses with human-consistent empathetic intents, EmpHi. EmHi 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.
• Score: 15.209172627484246
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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