  In a 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. The
dialog context information (contextual information) and the mutual interaction
information are two key factors that contribute to the two related tasks.
Unfortunately, none of the existing approaches consider the two important
sources of information simultaneously. In this paper, we propose a
Co-Interactive Graph Attention Network (Co-GAT) to jointly perform the two
tasks. The core module is a proposed co-interactive graph interaction layer
where a cross-utterances connection and a cross-tasks connection are
constructed and iteratively updated with each other, achieving to consider the
two types of information simultaneously. Experimental results on two public
datasets show that our model successfully captures the two sources of
information and achieve the state-of-the-art performance.
  In addition, we find that the contributions from the contextual and mutual
interaction information do not fully overlap with contextualized word
representations (BERT, Roberta, XLNet).
