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In dialog system, dialog act recognition and sentiment classification are twocorrelative tasks to capture speakers intentions, where dialog act andsentiment can indicate the explicit and the implicit intentions separately.Most of the existing systems either treat them as separate tasks or justjointly model the two tasks by sharing parameters in an implicit way withoutexplicitly modeling mutual interaction and relation. To address this problem,we propose a Deep Co-Interactive Relation Network (DCR-Net) to explicitlyconsider the cross-impact and model the interaction between the two tasks byintroducing a co-interactive relation layer. In addition, the proposed relationlayer can be stacked to gradually capture mutual knowledge with multiple stepsof interaction. Especially, we thoroughly study different relation layers andtheir effects. Experimental results on two public datasets (Mastodon andDailydialog) show that our model outperforms the state-of-the-art joint modelby 4.3% and 3.4% in terms of F1 score on dialog act recognition task, 5.7% and12.4% on sentiment classification respectively. Comprehensive analysisempirically verifies the effectiveness of explicitly modeling the relationbetween the two tasks and the multi-steps interaction mechanism. Finally, weemploy the Bidirectional Encoder Representation from Transformer (BERT) in ourframework, which can further boost our performance in both tasks.

DCR-Net: A Deep Co-Interactive Relation Network for Joint Dialog Act Recognition and Sentiment Classification