Summary of Dynamic Reward Adjustment in Multi-reward Reinforcement Learning For Counselor Reflection Generation, by Do June Min et al.
Dynamic Reward Adjustment in Multi-Reward Reinforcement Learning for Counselor Reflection Generation
by Do June Min, Veronica Perez-Rosas, Kenneth Resnicow, Rada Mihalcea
First submitted to arxiv on: 20 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 This paper proposes novel methods for multi-reward reinforcement learning in natural language generation. The researchers focus on counselor reflection generation, optimizing generators to improve fluency, coherence, and reflection quality simultaneously. They introduce DynaOpt and C-DynaOpt, bandit methods that combine rewards into a single value and optimize them concurrently. Using non-contextual and contextual multi-arm bandits, they dynamically adjust reward weights during training. Experimental results demonstrate the outperformance of their proposed techniques over existing baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better talk to people who need help. We want computer programs that can have conversations like a counselor would. To do this, we need to teach these computers how to generate good responses. The problem is that “good” is not just one thing – it’s about being clear, easy to understand, and making sense. The researchers came up with two new ways to help the computer learn all at once. They tested their ideas and found they work better than older methods. This could be important for creating more helpful conversations between people and computers. |
Keywords
* Artificial intelligence * Reinforcement learning
