基于栈溢出查询映射的高效查询自动重构
Automated Query Reformulation for Efficient Search based on Query Logs From Stack Overflow
摘要：
作为一个流行的编程问答网站，Stack Overflow是开发者的宝藏。然而，Stack Overflow上的大量问题和答案使得开发人员很难有效地找到他们正在寻找的信息。有两个差距导致搜索结果不佳：用户的意图和文本查询之间的差距，以及查询和帖子内容之间的语义差距。因此，开发人员必须不断地重新制定他们的查询，纠正拼写错误的单词，增加限制某些程序设计语言或平台等查询重新制定是繁琐的开发人员，特别是新手，我们提出了一个自动化的软件特定的查询重新制定方法的基础上深度学习。利用Stack Overflow提供的查询日志，我们构建了一个大规模的查询重构语料库，包括原始查询和相应的重构查询。我们的方法训练了一个Transformer模型，当给定用户的原始查询时，该模型可以自动生成候选重构查询。评估结果表明，我们的方法优于五个国家的最先进的基线，并实现了5.6%至33.5%的提升方面的$\mathit{ExactMatch}$和4.8%至14.4%的提升方面的$\mathit{GLEU}$。
英文摘要：
As a popular Q&A site for programming, Stack Overflow is a treasure for developers. However, the amount of questions and answers on Stack Overflow make it difficult for developers to efficiently locate the information they are looking for. There are two gaps leading to poor search results: the gap between the user's intention and the textual query, and the semantic gap between the query and the post content. Therefore, developers have to constantly reformulate their queries by correcting misspelled words, adding limitations to certain programming languages or platforms, etc. As query reformulation is tedious for developers, especially for novices, we propose an automated software-specific query reformulation approach based on deep learning. With query logs provided by Stack Overflow, we construct a large-scale query reformulation corpus, including the original queries and corresponding reformulated ones. Our approach trains a Transformer model that can automatically generate candidate reformulated queries when given the user's original query. The evaluation results show that our approach outperforms five state-of-the-art baselines, and achieves a 5.6% to 33.5% boost in terms of $\mathit{ExactMatch}$ and a 4.8% to 14.4% boost in terms of $\mathit{GLEU}$.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
