Global and Local Hierarchy-aware Contrastive Framework for Implicit Discourse Relation Recognition

TL;DR

This paper introduces GOLF, a novel framework that leverages both global and local hierarchical sense information through multi-task and contrastive learning to improve implicit discourse relation recognition, outperforming existing models.

Contribution

It proposes a new hierarchical contrastive learning framework that effectively models global and local sense hierarchies for better discourse relation representations.

Findings

Significant performance improvements on PDTB datasets.

Outperforms state-of-the-art models at all hierarchical levels.

Code is publicly available for reproducibility.

Abstract

Due to the absence of explicit connectives, implicit discourse relation recognition (IDRR) remains a challenging task in discourse analysis. The critical step for IDRR is to learn high-quality discourse relation representations between two arguments. Recent methods tend to integrate the whole hierarchical information of senses into discourse relation representations for multi-level sense recognition. Nevertheless, they insufficiently incorporate the static hierarchical structure containing all senses (defined as global hierarchy), and ignore the hierarchical sense label sequence corresponding to each instance (defined as local hierarchy). For the purpose of sufficiently exploiting global and local hierarchies of senses to learn better discourse relation representations, we propose a novel GlObal and Local Hierarchy-aware Contrastive Framework (GOLF), to model two kinds of hierarchies with the aid of multi-task learning and contrastive learning. Experimental results on PDTB 2.0 and PDTB 3.0 datasets demonstrate that our method remarkably outperforms current state-of-the-art models at all hierarchical levels. Our code is publicly available at https://github.com/YJiangcm/GOLF_for_IDRR