去掉提问语气，只保留核心信息，能提升问答系统检索准确率。
Improving RAG Retrieval via Propositional Content Extraction: a Speech Act Theory Approach
- 从用户问题中提取核心命题，剥离问句等语言标记
- 在巴西电信新闻数据上，关键文档检索准确率显著提升
- 适合需要精准检索的对话系统与知识库应用
用户提问时常包含疑问句式或礼貌用语等语用标记，这些虽体现意图（如提问、请求），但不构成核心信息。本文基于言语行为理论，提出一种自动将查询转化为其命题等价物的方法，在嵌入前剥离意图标记。在包含63个用户查询的巴西电信新闻语料上进行实验，使用预计算的语义嵌入评估。结果表明，去除言语行为标记后的查询嵌入与文档嵌入在前几项排名中的语义相似度明显提高，验证了该方法能更有效地检索相关文本内容。
原文摘要 · Abstract (English)
When users formulate queries, they often include not only the information they seek, but also pragmatic markers such as interrogative phrasing or polite requests. Although these speech act indicators communicate the user\textquotesingle s intent -- whether it is asking a question, making a request, or stating a fact -- they do not necessarily add to the core informational content of the query itself. This paper investigates whether extracting the underlying propositional content from user utterances -- essentially stripping away the linguistic markers of intent -- can improve retrieval quality in Retrieval-Augmented Generation (RAG) systems. Drawing upon foundational insights from speech act theory, we propose a practical method for automatically transforming queries into their propositional equivalents before embedding. To assess the efficacy of this approach, we conducted an experimental study involving 63 user queries related to a Brazilian telecommunications news corpus with precomputed semantic embeddings. Results demonstrate clear improvements in semantic similarity between query embeddings and document embeddings at top ranks, confirming that queries stripped of speech act indicators more effectively retrieve relevant content.
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