  In dialog system, dialog act recognition and sentiment classification are two
correlative tasks to capture speakers intentions, where dialog act and
sentiment can indicate the explicit and the implicit intentions separately.
Most of the existing systems either treat them as separate tasks or just
jointly model the two tasks by sharing parameters in an implicit way without
explicitly modeling mutual interaction and relation. To address this problem,
we propose a Deep Co-Interactive Relation Network (DCR-Net) to explicitly
consider the cross-impact and model the interaction between the two tasks by
introducing a co-interactive relation layer. In addition, the proposed relation
layer can be stacked to gradually capture mutual knowledge with multiple steps
of interaction. Especially, we thoroughly study different relation layers and
their effects. Experimental results on two public datasets (Mastodon and
Dailydialog) show that our model outperforms the state-of-the-art joint model
by 4.3% and 3.4% in terms of F1 score on dialog act recognition task, 5.7% and
12.4% on sentiment classification respectively. Comprehensive analysis
empirically verifies the effectiveness of explicitly modeling the relation
between the two tasks and the multi-steps interaction mechanism. Finally, we
employ the Bidirectional Encoder Representation from Transformer (BERT) in our
framework, which can further boost our performance in both tasks.
