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Natural language understanding and dialogue policy learning are bothessential in conversational systems that predict the next system actions inresponse to a current user utterance. Conventional approaches aggregateseparate models of natural language understanding (NLU) and system actionprediction (SAP) as a pipeline that is sensitive to noisy outputs oferror-prone NLU. To address the issues, we propose an end-to-end deep recurrentneural network with limited contextual dialogue memory by jointly training NLUand SAP on DSTC4 multi-domain human-human dialogues. Experiments show that ourproposed model significantly outperforms the state-of-the-art pipeline modelsfor both NLU and SAP, which indicates that our joint model is capable ofmitigating the affects of noisy NLU outputs, and NLU model can be refined byerror flows backpropagating from the extra supervised signals of systemactions.

End-to-End Joint Learning of Natural Language Understanding and Dialogue Manager