Meta Dialogue Policy Learning

• URL: http://arxiv.org/abs/2006.02588v1
• Date: Wed, 3 Jun 2020 23:53:06 GMT
• Title: Meta Dialogue Policy Learning
• Authors: Yumo Xu, Chenguang Zhu, Baolin Peng and Michael Zeng
• Abstract summary: We propose Deep Transferable Q-Network (DTQN) to utilize shareable low-level signals between domains. We decompose the state and action representation space into feature subspaces corresponding to these low-level components. In experiments, our model outperforms baseline models in terms of both success rate and dialogue efficiency.
• Score: 58.045067703675095
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dialog policy determines the next-step actions for agents and hence is central to a dialogue system. However, when migrated to novel domains with little data, a policy model can fail to adapt due to insufficient interactions with the new environment. We propose Deep Transferable Q-Network (DTQN) to utilize shareable low-level signals between domains, such as dialogue acts and slots. We decompose the state and action representation space into feature subspaces corresponding to these low-level components to facilitate cross-domain knowledge transfer. Furthermore, we embed DTQN in a meta-learning framework and introduce Meta-DTQN with a dual-replay mechanism to enable effective off-policy training and adaptation. In experiments, our model outperforms baseline models in terms of both success rate and dialogue efficiency on the multi-domain dialogue dataset MultiWOZ 2.0.

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