Summary of Enhancing End-to-end Multi-task Dialogue Systems: a Study on Intrinsic Motivation Reinforcement Learning Algorithms For Improved Training and Adaptability, by Navin Kamuni et al.
Enhancing End-to-End Multi-Task Dialogue Systems: A Study on Intrinsic Motivation Reinforcement Learning Algorithms for Improved Training and Adaptability
by Navin Kamuni, Hardik Shah, Sathishkumar Chintala, Naveen Kunchakuri, Sujatha Alla Old Dominion
First submitted to arxiv on: 31 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 research investigates end-to-end multi-task dialogue systems with a focus on reinforcement learning algorithms for intrinsic motivation. The current dialogue systems rely on simplistic rewards, which hinders the agent’s ability to learn and adapt quickly. This study aims to improve the policy module by teaching it an internal incentive system using techniques like random network distillation and curiosity-driven reinforcement learning. The goal is to encourage exploration and measure state visits based on semantic similarity between utterances. Experimental results on MultiWOZ, a heterogeneous dataset, show that intrinsic motivation-based dialogue systems outperform policies relying on extrinsic incentives, achieving an average success rate of 73% compared to the baseline Proximal Policy Optimization (PPO) at 60%. Additionally, performance indicators like booking rates and completion rates improve by 10%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Intrinsic motivation is key in end-to-end dialogue systems. Current systems rely on simple rewards, which slows down learning. This study wants to change that. It’s all about teaching the agent an internal drive to do better, using clever techniques to make it explore more and learn faster. They tested this idea on a big dataset called MultiWOZ, and the results are amazing! The new system did much better than the old one, with an average success rate of 73% compared to just 60%. It also got better at booking things and completing tasks, by a whole 10%! This is super important because it means the system can be used in lots of different areas without getting stuck. |
Keywords
* Artificial intelligence * Distillation * Multi task * Optimization * Reinforcement learning
