论文标题
将网络搜索查询转换为自然语言问题
Translating Web Search Queries into Natural Language Questions
论文作者
论文摘要
用户通常会牢记搜索引擎的搜索引擎，通常这些查询是关键字或次句片段。例如，如果用户想知道“美国资本是什么”的答案，那么他们很可能会查询“美国资本”或“美国资本”或某些基于关键字的变化。例如，对于用户输入查询“美国之都”的查询，最可能的问题是“美国的首都是什么？”。在本文中，我们提出了一种从给定的基于关键字的查询中产生良好的自然语言问题的方法，该问题具有与查询相同的问题。将基于关键字的Web查询转换为一个形式良好的问题有很多应用程序，其中一些应用程序正在搜索引擎，社区问题答案（CQA）网站和机器人通信。我们发现与标准机器翻译（MT）任务之间的查询问题问题之间的协同作用。我们已经使用统计MT（SMT）和神经MT（NMT）模型来产生查询问题。我们已经观察到MT模型在自动和人类评估方面的表现都很好。
Users often query a search engine with a specific question in mind and often these queries are keywords or sub-sentential fragments. For example, if the users want to know the answer for "What's the capital of USA", they will most probably query "capital of USA" or "USA capital" or some keyword-based variation of this. For example, for the user entered query "capital of USA", the most probable question intent is "What's the capital of USA?". In this paper, we are proposing a method to generate well-formed natural language question from a given keyword-based query, which has the same question intent as the query. Conversion of keyword-based web query into a well-formed question has lots of applications, with some of them being in search engines, Community Question Answering (CQA) website and bots communication. We found a synergy between query-to-question problem with standard machine translation(MT) task. We have used both Statistical MT (SMT) and Neural MT (NMT) models to generate the questions from the query. We have observed that MT models perform well in terms of both automatic and human evaluation.
