用问答对捕捉名词语义，让句子分解更细更准。
QA-Noun: Representing Nominal Semantics via Natural Language Question-Answer Pairs
- 设计9种问题模板，从语法和上下文两方面挖掘名词角色。
- 覆盖AMR中90%以上名词论元，比现有方法多出130%细粒度事实。
- 适合需要精准语义解析的NLP任务，如信息抽取与跨文本对齐。
将句子分解为细粒度语义单元正成为建模语义对齐的重要手段。尽管基于问答（QA）的方法在谓词-论元关系表示上表现良好，但对名词中心语义的处理仍属空白。本文提出QA-Noun，一种基于问答的名词语义表示框架。该框架定义了九种问题模板，涵盖名词的显式句法角色和隐含上下文角色，生成可解释的问答对，补充现有的口语化问答语义角色标注（QA-SRL）。我们发布了详细的标注指南、超过2,000个标注名词实例的数据集，以及一个集成到QA-SRL中的训练模型，实现句子意义的统一分解为高度细粒度的事实单元。评估表明，QA-Noun实现了对AMR中名词论元的近乎完整覆盖，并揭示了额外的语境推断关系；结合QA-Noun与QA-SRL后，细粒度程度比FactScore和DecompScore等近期基于事实的分解方法高出130%以上。因此，QA-Noun补全了基于问答的语义框架，形成一套全面且可扩展的细粒度语义分解方法，适用于跨文本对齐任务。
原文摘要 · Abstract (English)
Decomposing sentences into fine-grained meaning units is increasingly used to model semantic alignment. While QA-based semantic approaches have shown effectiveness for representing predicate-argument relations, they have so far left noun-centered semantics largely unaddressed. We introduce QA-Noun, a QA-based framework for capturing noun-centered semantic relations. QA-Noun defines nine question templates that cover both explicit syntactical and implicit contextual roles for nouns, producing interpretable QA pairs that complement verbal QA-SRL. We release detailed guidelines, a dataset of over 2,000 annotated noun mentions, and a trained model integrated with QA-SRL to yield a unified decomposition of sentence meaning into individual, highly fine-grained, facts. Evaluation shows that QA-Noun achieves near-complete coverage of AMR's noun arguments while surfacing additional contextually implied relations, and that combining QA-Noun with QA-SRL yields over 130\% higher granularity than recent fact-based decomposition methods such as FactScore and DecompScore. QA-Noun thus complements the broader QA-based semantic framework, forming a comprehensive and scalable approach to fine-grained semantic decomposition for cross-text alignment.
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