  The joint task of Dialog Sentiment Classification (DSC) and Act Recognition
(DAR) aims to predict the sentiment label and act label for each utterance in a
dialog simultaneously. However, current methods encode the dialog context in
only one direction, which limits their ability to thoroughly comprehend the
context. Moreover, these methods overlook the explicit correlations between
sentiment and act labels, which leads to an insufficient ability to capture
rich sentiment and act clues and hinders effective and accurate reasoning. To
address these issues, we propose a Bi-directional Multi-hop Inference Model
(BMIM) that leverages a feature selection network and a bi-directional
multi-hop inference network to iteratively extract and integrate rich sentiment
and act clues in a bi-directional manner. We also employ contrastive learning
and dual learning to explicitly model the correlations of sentiment and act
labels. Our experiments on two widely-used datasets show that BMIM outperforms
state-of-the-art baselines by at least 2.6% on F1 score in DAR and 1.4% on F1
score in DSC. Additionally, Our proposed model not only improves the
performance but also enhances the interpretability of the joint sentiment and
act prediction task.
