Translating Web Search Queries into Natural Language Questions Users often query a search engine with a specific question in mind and often these queries are keywords or subsentential fragments. For example, if the users want to know the answer for Whatu0027s the capital of USA, they will most probably query capital of USA or USA capital or some keywordbased variation of this. For example, for the user entered query capital of USA, the most probable question intent is Whatu0027s the capital of USA?. In this paper, we are proposing a method to generate wellformed natural language question from a given keywordbased query, which has the same question intent as the query. Conversion of keywordbased web query into a wellformed 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 querytoquestion 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.

Machine ComprehensionIncorporated Relevance Matching In current web search engines, the relevance between a query and web pages (i.e. documents) is measured by Text Matching (TM) models. The documents retrieved mainly focus on matching text queries themselves but fail to find target information towards the user intent (e.g. the direct answer to questionstyle queries). Thus the document containing target information that users want may not be ranked at the top 1 in the search result or even not be recalled. Besides, as voice search and voicepowered assistants are entering our life, queries tend to be long tail ones, which needs a search engine evolved into a higher level of semantic relevance matching. Therefore, it is necessary to build an intenttarget relevance matching model in modern search scenarios. This paper proposes a unified model of Machine Comprehensionincorporated Relevance Matching (MCRM). Totally, MCRM models how web users choose the relevant documents to read by observing the titles or summaries, and further look for target information from them. To accomplish that, we first formulate two tasks as Text Matching and Target Extracting. For learning each task, a Contextaugmented Matching network (ContMatch) and a Matchingfused machine Comprehension network (MatComprehend) are proposed. Then, they are integrated into an endtoend framework that can not only measure the semantic relevance but also extract the intentrelated target, by deeply comprehending the semantics hidden in queries and exploiting intenttarget relations between queries and documents. In MCRM, the two tasks are jointly learned by a multitask learning approach, where the semantic relevance measured by ContMatch, and the intenttarget relevance captured by MatComprehend, are combined and enhanced mutually for boosting the final performance. We conduct extensive experiments on realworld data. The experimental results demonstrate the superiority of MCRM against the stateoftheart relevance models.