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.

翻译：对话情感分类（DSC）与行为识别（DAR）的联合任务旨在同时预测对话中每个话语的情感标签和行为标签。然而，现有方法仅对对话上下文进行单向编码，限制了其对上下文的全面理解能力。此外，这些方法忽略了情感标签与行为标签之间的显式关联，导致难以充分捕获丰富的情感和行为线索，从而阻碍了有效且准确的推理。为解决这些问题，我们提出了一种双向多跳推理模型（BMIM），该模型利用特征选择网络和双向多跳推理网络，以双向方式迭代提取并整合丰富的情感和行为线索。我们还采用对比学习和双重学习，显式建模情感标签与行为标签之间的关联。在两个广泛使用的数据集上的实验表明，BMIM在DAR任务上的F1分数比现有最优基线方法至少高出2.6%，在DSC任务上至少高出1.4%。此外，所提出的模型不仅提升了联合情感与行为预测任务的性能，还增强了其可解释性。