用大模型生成跨语言混用文本的句法标注，解决低资源语言标注难题。
Parsing the Switch: LLM-Based UD Annotation for Complex Code-Switched and Low-Resource Languages
- 基于提示工程与专家审核，构建跨语言混合文本的句法标注流程。
- 在西语-瓜拉尼语数据上达到95.29%的LAS，显著优于现有模型。
- 首次发布西语-瓜拉尼通用依存树库，适合多语言与低资源研究者使用。
语言混用给句法分析带来复杂挑战，尤其在低资源语言环境下标注数据稀缺。尽管近期研究探索了大语言模型（LLMs）在序列标注中的应用，但少有工作系统研究其在混用语境中捕捉句法结构的能力。现有基于单语树库训练的解析器难以泛化至多语言或混合语言输入。为此，我们提出BiLingua Parser，一种基于大模型的标注流水线，用于生成代码混用文本的通用依存（UD）标注。首先，我们构建了针对西班牙语-英语和西班牙语-瓜拉尼语的数据提示框架，结合少量示例提示与专家评审。其次，我们发布了两个标注数据集，包括首个西班牙语-瓜拉尼语的UD标注语料库。第三，我们对不同语言对和交际场景下的切换点进行了详细句法分析。实验结果表明，经过专家修订后，BiLingua Parser的LAS达到95.29%，显著优于先前基线和多语言解析器。结果表明，经精心引导的大模型可成为低资源、混用语言环境中句法资源构建的实用工具。数据与源码已公开于https://github.com/N3mika/ParsingProject。
原文摘要 · Abstract (English)
Code-switching presents a complex challenge for syntactic analysis, especially in low-resource language settings where annotated data is scarce. While recent work has explored the use of large language models (LLMs) for sequence-level tagging, few approaches systematically investigate how well these models capture syntactic structure in code-switched contexts. Moreover, existing parsers trained on monolingual treebanks often fail to generalize to multilingual and mixed-language input. To address this gap, we introduce the BiLingua Parser, an LLM-based annotation pipeline designed to produce Universal Dependencies (UD) annotations for code-switched text. First, we develop a prompt-based framework for Spanish-English and Spanish-Guaraní data, combining few-shot LLM prompting with expert review. Second, we release two annotated datasets, including the first Spanish-Guaraní UD-parsed corpus. Third, we conduct a detailed syntactic analysis of switch points across language pairs and communicative contexts. Experimental results show that BiLingua Parser achieves up to 95.29% LAS after expert revision, significantly outperforming prior baselines and multilingual parsers. These results show that LLMs, when carefully guided, can serve as practical tools for bootstrapping syntactic resources in under-resourced, code-switched environments. Data and source code are available at https://github.com/N3mika/ParsingProject
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