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Structured Deep Learning for Dialogue Management

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In this talk, I will first introduce the works on structured deep learning for speech and language processing at the SpeechLab of Shanghai Jiao Tong University. Then, I will focus on a recent work on structured deep reinforcement learning. When dialogue domain changes dynamically, e.g. a new previously unseen concept (or slot) which can be then used as a database search constraint is added, or the policy for one domain is transferred to another domain, the dialogue state space and action sets both will change. Therefore, the model structures for different domains have to be different. This makes dialogue policy adaptation/transfer challenging. Here a multi-agent dialogue policy (MADP) is proposed to tackle these problems. MADP consists of some slot-dependent agents (S-Agents) and a slot-independent agent (G-Agent). S-Agents have shared parameters in addition to private parameters for each one. During policy transfer, the shared parameters in S-Agents and the parameters in G-Agent can be directly transferred to the agents in extended/new domain. Simulation experiments showed that MADP can significantly speed up the policy learning and facilitate policy adaptation.

This talk is part of the CUED Speech Group Seminars series.

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