用大模型将句子转为语法结构，提升西班牙语句法分析效果
Fine-tuning of Large Language Models for Constituency Parsing Using a Sequence to Sequence Approach
- 用序列到序列方法微调大模型，实现句子到语法树的转换
- 在AnCora-ES数据集上获得高F1分数，验证了方法有效性
- 适用于语言教学工具开发，尤其适合西班牙语语法分析
大型神经模型在自然语言处理中的最新进展为基于机器学习的句法分析带来了新可能。本文探索了一种新颖的短语结构分析方法：通过微调大语言模型（LLMs），将输入句子翻译成对应的语法结构。主要目标是扩展MiSintaxis这一用于教授西班牙语语法的教学工具的功能。使用Hugging Face仓库中的多个模型，基于AnCora-ES语料库生成的训练数据进行微调，并以F1分数评估性能。结果表明，该方法在短语结构分析中表现出高准确性，凸显了此方法的潜力。
原文摘要 · Abstract (English)
Recent advances in natural language processing with large neural models have opened new possibilities for syntactic analysis based on machine learning. This work explores a novel approach to phrase-structure analysis by fine-tuning large language models (LLMs) to translate an input sentence into its corresponding syntactic structure. The main objective is to extend the capabilities of MiSintaxis, a tool designed for teaching Spanish syntax. Several models from the Hugging Face repository were fine-tuned using training data generated from the AnCora-ES corpus, and their performance was evaluated using the F1 score. The results demonstrate high accuracy in phrase-structure analysis and highlight the potential of this methodology.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。