Enhancing Multiparty Dialog Discourse Parsing With Dynamic Task-Adaptive Graph Transformer and Difficulty-Aware Task Scheduling

Yaxin Fan, Peifeng Li, Fang Kong, Qiaoming Zhu

Abstract: Multiparty dialog discourse parsing (MDDP) aims to identify the links between pairs of utterances and recognize their discourse relations. Previous research has attempted to address data sparsity in discourse parsing through multitask learning, but these efforts often relied on manually annotated fine-grained information, limiting their practical applicability. In this study, we propose dynamic task-adaptive graph transformer with difficulty-aware task scheduling (DTGT-DTS), an innovative multitask approach that enhances discourse parsing by leveraging neighboring tasks like addressee recognition and speaker identification, without requiring additional annotations. These tasks share common discourse links with discourse parsing but also possess distinct private links. To tackle this, we design a dynamic task-adaptive graph transformer (DTGT) that captures shared links between discourse parsing and its neighboring tasks while distinguishing the private links of neighboring tasks. In addition, we develop a difficulty-aware task scheduling (DTS) strategy that promotes multitask learning by dynamically adjusting training priorities based on the relative difficulty of different tasks. Experimental results on two widely used discourse datasets—Molweni (78 245 links and relations) and STAC (12 691 links and relations)—show that our DTGT-DTS model achieves a 6.07% and 5.31% performance improvement in link identification, respectively, and a 7.27% and 6.02% improvement in relation recognition.

External IDs:dblp:journals/tnn/FanLKZ25