Multi-Lingual Implicit Discourse Relation Recognition with Multi-Label Hierarchical Learning

This paper introduces the first multi-lingual and multi-label classification model for implicit discourse relation recognition (IDRR). Our model, HArch, is evaluated on the recently released DiscoGeM 2.0 corpus and leverages hierarchical dependencies between discourse senses to predict probability distributions across all three sense levels in the PDTB 3.0 framework. We compare several pre-trained encoder backbones and find that RoBERTa-HArch achieves the best performance in English, while XLM-RoBERTa-HArch performs best in the multi-lingual setting. In addition, we compare our fine-tuned models against GPT-4o and Llama-4-Maverick using few-shot prompting across all language configurations. Our results show that our fine-tuned models consistently outperform these LLMs, highlighting the advantages of task-specific fine-tuning over prompting in IDRR. Finally, we report SOTA results on the DiscoGeM 1.0 corpus, further validating the effectiveness of our hierarchical approach.

A Multi-Task and Multi-Label Classification Model for Implicit Discourse Relation Recognition

In this work, we address the inherent ambiguity in Implicit Discourse Relation Recognition (IDRR) by introducing a novel multi-task classification model capable of learning both multi-label and single-label representations of discourse relations. Leveraging the DiscoGeM corpus, we train and evaluate our model on both multi-label and traditional single-label classification tasks. To the best of our knowledge, our work presents the first truly multi-label classifier in IDRR, establishing a benchmark for multi-label classification and achieving SOTA results in single-label classification on DiscoGeM. Additionally, we evaluate our model on the PDTB 3.0 corpus for single-label classification without any prior exposure to its data. While the performance is below the current SOTA, our model demonstrates promising results indicating potential for effective transfer learning across both corpora.