首个多语言隐含语篇关系分类模型，提升跨语言文本连贯性理解
Multi-Lingual Implicit Discourse Relation Recognition with Multi-Label Hierarchical Learning
- 构建分层概率模型，利用语篇意义层级关系进行多标签预测
- 在多语言数据上超越GPT-4o和Llama-4-Maverick，SOTA表现
- 适用于多语言语篇分析、自然语言推理等任务的研究者
本文提出首个支持多语言、多标签的隐含语篇关系识别（IDRR）模型HArch。该模型在新发布的DiscoGeM 2.0语料库上评估，利用PDTB 3.0框架中三个语篇意义层级间的层次依赖关系，预测所有层级的概率分布。我们比较了多种预训练编码器，发现RoBERTa-HArch在英文中表现最优，而XLM-RoBERTa-HArch在多语言设置下更优。此外，我们在所有语言配置下将微调模型与GPT-4o和Llama-4-Maverick进行少样本提示对比，结果表明微调模型始终优于大模型，凸显任务特定微调的优势。最后，我们在DiscoGeM 1.0上报告了当前最优结果，进一步验证分层方法的有效性。
原文摘要 · Abstract (English)
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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