A Discourse Structure- and Interlocutor-Guided Network for Dialogue Act Recognition and Sentiment Classification

Dialogue act recognition and sentiment classification (DAR and DSC) are closely related tasks in dialogue systems, both benefiting from joint modeling of their interdependencies. Recent advancements have improved performance on these tasks by integrating them, yet many approaches oversimplify dialogues by treating them as monologues and assuming uniform influence across all utterances. This neglects the inherent structural and interactive nature of dialogues. To address these issues, we propose a novel <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b>iscourse <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b>tructure- and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</b>nterlocutor-<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b>uided (DSIG) network that fuses dialogue act recognition and sentiment classification. Our network synergizes structural dialogue relationships with interlocutor identity information, enabling effective modeling of utterance flow and cross-task interactions. Specifically, we utilize a shared encoder that functions as a dialogue discourse parser to dynamically construct utterance connections, thereby integrating dialogue structural relationships. In addition, we embed interlocutor affiliations into the fusion process during encoding, semantic modeling, and decoding, enhancing dialogue understanding and dual-task reasoning. The core component is a collaborative updating graph interaction layer, which filters redundant connections and introduces interlocutor nodes for exclusive information exchange. Experimental results on two benchmark datasets demonstrate DSIG achieves state-of-the-art performance by effectively fusing dialogue structure and interlocutor information, improving F1 scores by 6.9% and 2.1% for DSC and DAR, respectively, on Mastodon, and by 13.7% and 4.7% on DailyDialog. Model variants trained independently show promise for extension to other dialogue-related tasks.