Deep Reinforcement Learning for Multi-Domain Dialogue Systems
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Abstract (arXiv)
Standard deep reinforcement learning methods such as Deep Q-Networks (DQN) for multiple tasks (domains) face scalability problems. We propose a method for multi-domain dialogue policy learning---termed NDQN, and apply it to an information-seeking spoken dialogue system in the domains of restaurants and hotels. Experimental results comparing DQN (baseline) versus NDQN (proposed) using simulations report that our proposed method exhibits better scalability and is promising for optimising the behaviour of multi-domain dialogue systems.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Nov 2016.
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Citing papers per year: 2021: 1; 2022: 2.
3 papers.
- 2022A Survey on Recent Advances and Challenges in Reinforcement Learning Methods for Task-Oriented Dialogue Policy Learningcited by 30
- 2021A Survey on Deep Reinforcement Learning for Audio-Based Applicationscited by 17
- 2022Reinforcement Learning of Multi-Domain Dialog Policies Via Action Embeddingscited by 0
- 2022Reinforcement Learning of Multi-Domain Dialog Policies Via Action Embeddings1 Jul 2022
- 2022A Survey on Recent Advances and Challenges in Reinforcement Learning Methods for Task-Oriented Dialogue Policy Learning28 Feb 2022
- 2021A Survey on Deep Reinforcement Learning for Audio-Based Applications1 Jan 2021