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arXiv · 2006.02588

Meta Dialogue Policy Learning

Yumo Xu·Chenguang Zhu·Baolin Peng·Michael Zeng
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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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Yumo Xu, Chenguang Zhu, Baolin Peng, Michael Zeng. 2020-06-03. Meta Dialogue Policy Learning. https://arxiv.org/abs/2006.02588

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