Meta Dialogue Policy Learning
This addresses the challenge of data scarcity in dialogue systems for AI assistants, though it is incremental as it builds on existing transfer and meta-learning methods.
The paper tackles the problem of dialogue policy adaptation to novel domains with limited data by proposing a meta-learning framework with a dual-replay mechanism, achieving higher success rates and dialogue efficiency on the MultiWOZ 2.0 dataset compared to baselines.
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.