用结构化提示模板让预训练模型直接做依存句法分析，无需额外网络。
Dependency Parsing with the Structuralized Prompt Template
- 用文本到文本训练方式，仅靠编码器+结构化提示完成解析
- 在多个语言和模型上表现优于传统方法，精度接近或超过基准
- 适配性强，易集成新任务特征，适合多语言场景
依存句法分析是自然语言处理中的基础任务，旨在识别句子的语法依赖关系并构建语法树。传统依存句法分析模型通常需要构建嵌入表示并添加额外预测层。本文提出一种新型依存句法分析方法，仅依赖一个编码器模型，并采用文本到文本的训练范式。为实现这一目标，我们引入了一种结构化提示模板，有效捕捉依存树的结构信息。实验结果表明，该方法虽仅使用预训练模型，但性能显著优于传统模型。此外，该方法对不同目标语言和训练环境下的多种预训练模型均表现出高度适应性，便于集成任务特定特征。
原文摘要 · Abstract (English)
Dependency parsing is a fundamental task in natural language processing (NLP), aiming to identify syntactic dependencies and construct a syntactic tree for a given sentence. Traditional dependency parsing models typically construct embeddings and utilize additional layers for prediction. We propose a novel dependency parsing method that relies solely on an encoder model with a text-to-text training approach. To facilitate this, we introduce a structured prompt template that effectively captures the structural information of dependency trees. Our experimental results demonstrate that the proposed method achieves outstanding performance compared to traditional models, despite relying solely on a pre-trained model. Furthermore, this method is highly adaptable to various pre-trained models across different target languages and training environments, allowing easy integration of task-specific features.
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