Summary of Empathy Level Alignment Via Reinforcement Learning For Empathetic Response Generation, by Hui Ma et al.
Empathy Level Alignment via Reinforcement Learning for Empathetic Response Generation
by Hui Ma, Bo Zhang, Bo Xu, Jian Wang, Hongfei Lin, Xiao Sun
First submitted to arxiv on: 6 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Empathetic Response Generation Framework (EmpRL) aims to build human-like dialogue systems by generating empathetic responses. Traditional approaches typically employ maximum likelihood estimation as the optimization objective during training, but fail to align the empathy levels between generated and target responses. To address this issue, EmpRL develops an effective empathy reward function and generates empathetic responses using reinforcement learning (RL). The framework utilizes a pre-trained T5 model as the generator and fine-tunes it to initialize the policy. An empathy reward function containing three communication mechanisms – emotional reaction, interpretation, and exploration – is constructed using pre-designed and pre-trained empathy identifiers. During RL training, the proximal policy optimization algorithm is used to fine-tune the policy, enabling the generation of empathetic responses. Automatic and human evaluations demonstrate that EmpRL significantly improves the quality of generated responses, enhances the similarity in empathy levels between generated and target responses, and produces empathetic responses covering both affective and cognitive aspects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Empathetic response generation is important for building human-like dialogue systems. Traditional approaches don’t do a good job of matching the level of empathy between generated and target responses. To fix this, researchers propose a new framework called EmpRL (short for Empathetic Response Generation). It uses a special type of learning called reinforcement learning to generate empathetic responses. This framework starts with a pre-trained model called T5 and fine-tunes it to make better guesses about what to say next. The goal is to create responses that are both empathetic and accurate, covering emotional and cognitive aspects. Tests show that EmpRL does a great job of generating high-quality responses that match the level of empathy in the original response. |
Keywords
» Artificial intelligence » Likelihood » Optimization » Reinforcement learning » T5
