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.

Dense Semantic Matching Network for Multiturn Conversation Mining the semantic information in the text to model the multiturn conversation has attracted great interests. Previous models ignore the capturing and utilizing of the matching patterns at different levels, which causes the loss of the valuable information for the calculation of the matching degree. To address the problem, we propose a dense semantic matching network (DMN). Given a contextresponse pair, DMN first constructs semantic representations for the response candidate and each utterance in the context. Then, DMN models the interaction between the response candidate and each utterance in the context to generate interactive matrices. By processing the interactive matrices with dense convolutional blocks, the hierarchical matching patterns are generated for each utteranceresponse pair. The matching patterns of all the utteranceresponse pairs are finally accumulated in chronological order with bidirectional long shortterm memory network. The final matching score of the response candidate and the multiturn context is finally calculated. We evaluate the performance of our network on the benchmark dataset. The results show that our network yields a significant performance gain compared with other methods.