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September 24, 2025Open Access

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

Key Points

The hierarchical learning approach enhances multi-lingual implicit discourse relation recognition accuracy.
RoBERTa-HArch achieves the best results in English, while XLM-RoBERTa-HArch excels in multi-lingual scenarios.
Fine-tuning task-specific models consistently outperforms few-shot prompting with large language models.
State-of-the-art results were reported on both DiscoGeM 2.0 and DiscoGeM 1.0 corpora, validating the model's efficacy.

Abstract

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.

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