用语义框架增强表示，让模型更好理解自然语言问句的深层结构。
FRASE: Structured Representations for Generalizable SPARQL Query Generation
- 基于语义框架标签构建问题的结构化表示
- 在未知模板和自然表述测试集上提升生成准确率
- 适合需要泛化能力的开放域知识库问答任务
将自然语言问题转化为SPARQL查询，可实现对知识库的事实性与实时响应。然而，现有数据集多为模板驱动，导致模型仅学习表面映射，缺乏真正泛化能力，难以应对自然表达且无模板的问题。本文提出FRASE（FRAme-based Semantic Enhancement），利用语义框架角色标注（FSRL）改进表示。我们还基于LC-QuAD 2.0构建了新的LC-QuAD 3.0数据集，通过框架检测和框架元素到参数的映射对每个问题进行增强。在多种大语言模型与微调配置下的实验表明，引入基于框架的结构化表示能持续提升SPARQL生成性能，尤其在测试问题包含未见模板（未知模板划分）或全为重新表述的自然句时表现更优。
原文摘要 · Abstract (English)
Translating natural language questions into SPARQL queries enables Knowledge Base querying for factual and up-to-date responses. However, existing datasets for this task are predominantly template-based, leading models to learn superficial mappings between question and query templates rather than developing true generalization capabilities. As a result, models struggle when encountering naturally phrased, template-free questions. This paper introduces FRASE (FRAme-based Semantic Enhancement), a novel approach that leverages Frame Semantic Role Labeling (FSRL) to address this limitation. We also present LC-QuAD 3.0, a new dataset derived from LC-QuAD 2.0, in which each question is enriched using FRASE through frame detection and the mapping of frame-elements to their argument. We evaluate the impact of this approach through extensive experiments on recent large language models (LLMs) under different fine-tuning configurations. Our results demonstrate that integrating frame-based structured representations consistently improves SPARQL generation performance, particularly in challenging generalization scenarios when test questions feature unseen templates (unknown template splits) and when they are all naturally phrased (reformulated questions).
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