用分层语义原型替代人工描述符，提升隐含语篇关系识别准确率。
Leveraging Hierarchical Prototypes as the Verbalizer for Implicit Discourse Relation Recognition
- 用类级别语义原型和层级标签结构作为自动化的描述符
- 在多个数据集上超越现有基线模型，提升识别精度
- 支持零样本跨语言迁移，适用于低资源语言
隐含语篇关系识别旨在确定未通过显式连接词关联的文本片段之间的关系。近年来，预训练、提示生成与预测范式为该任务提供了有前景的解决方案。然而，以往工作仅依赖人工设计的描述符，存在歧义甚至错误的问题。为此，本文利用捕捉特定类别语义特征的原型以及不同类别间的层次化标签结构作为描述符。实验表明，该方法在多个基准数据集上优于当前先进模型。此外，所提方法可扩展至零样本跨语言学习，有助于在资源匮乏的语言中实现语篇关系识别。这些进展验证了该方法在多语言环境下应对隐含语篇关系识别问题的实用性与通用性。
原文摘要 · Abstract (English)
Implicit discourse relation recognition involves determining relationships that hold between spans of text that are not linked by an explicit discourse connective. In recent years, the pre-train, prompt, and predict paradigm has emerged as a promising approach for tackling this task. However, previous work solely relied on manual verbalizers for implicit discourse relation recognition, which suffer from issues of ambiguity and even incorrectness. To overcome these limitations, we leverage the prototypes that capture certain class-level semantic features and the hierarchical label structure for different classes as the verbalizer. We show that our method improves on competitive baselines. Besides, our proposed approach can be extended to enable zero-shot cross-lingual learning, facilitating the recognition of discourse relations in languages with scarce resources. These advancement validate the practicality and versatility of our approach in addressing the issues of implicit discourse relation recognition across different languages.
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