提出分层双适配器模型，提升多语言跨框架论说关系分类准确率
CLaC at DISRPT 2025: Hierarchical Adapters for Cross-Framework Multi-lingual Discourse Relation Classification
- 设计分层双适配器对比学习架构，降低参数量同时保持性能
- 在16语言39语料上达67.5%准确率，优于全量微调与提示模型
- 适合关注多语言论说分析与高效模型设计的研究者
我们提交了2025年DISRPT任务3（论说关系分类）的成果。该任务在16种语言、39个语料库和六种论说框架下统一了17类论说关系标签，带来显著的多语言与跨形式挑战。我们首先通过微调mBERT、XLM-RoBERTa-Base和XLM-RoBERTa-Large三种多语言模型，并结合两种论点顺序策略与渐进解冻比例建立强基线。随后评估基于提示的大语言模型（Claude Opus 4.0）在零样本与少样本设置下的表现，以理解其对新统一标签的响应。最终提出HiDAC：一种分层双适配器对比学习模型。结果表明，尽管更大规模的Transformer模型准确率更高，但提升有限；解冻顶层75%编码器层即可达到接近全量微调的性能，且训练参数大幅减少。提示模型显著落后于微调的Transformer，而HiDAC在所有方法中取得最高准确率（67.5%），同时保持更高的参数效率。
原文摘要 · Abstract (English)
We present our submission to Task 3 (Discourse Relation Classification) of the DISRPT 2025 shared task. Task 3 introduces a unified set of 17 discourse relation labels across 39 corpora in 16 languages and six discourse frameworks, posing significant multilingual and cross-formalism challenges. We first benchmark the task by fine-tuning multilingual BERT-based models (mBERT, XLM-RoBERTa-Base, and XLM-RoBERTa-Large) with two argument-ordering strategies and progressive unfreezing ratios to establish strong baselines. We then evaluate prompt-based large language models (namely Claude Opus 4.0) in zero-shot and few-shot settings to understand how LLMs respond to the newly proposed unified labels. Finally, we introduce HiDAC, a Hierarchical Dual-Adapter Contrastive learning model. Results show that while larger transformer models achieve higher accuracy, the improvements are modest, and that unfreezing the top 75% of encoder layers yields performance comparable to full fine-tuning while training far fewer parameters. Prompt-based models lag significantly behind fine-tuned transformers, and HiDAC achieves the highest overall accuracy (67.5%) while remaining more parameter-efficient than full fine-tuning.
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