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In end-to-end dialogue modeling and agent learning, it is important to (1)effectively learn knowledge from data, and (2) fully utilize heterogeneousinformation, e.g., dialogue act flow and utterances. However, the majority ofexisting methods cannot simultaneously satisfy the two conditions. For example,rule definition and data labeling during system design take too much manualwork, and sequence-to-sequence methods only model one-side utteranceinformation. In this paper, we propose a novel joint end-to-end model bymulti-task representation learning, which can capture the knowledge fromheterogeneous information through automatically learning knowledgeablelow-dimensional embeddings from data, named with DialogAct2Vec. The modelrequires little manual work for intervention in system design and we find thatthe multi-task learning can greatly improve the effectiveness of representationlearning. Extensive experiments on a public dataset for restaurant reservationshow that the proposed method leads to significant improvements against thestate-of-the-art baselines on both the act prediction task and utteranceprediction task.

DialogAct2Vec: Towards End-to-End Dialogue Agent by Multi-Task Representation Learning