Semantic matching is critical in many text applications, including paraphrase identi cation, information retrieval, and question answering. A variety of machine learning techniques have been developed for various semantic matching tasks, referred to as \learning to match". Recently, deep learning approaches have shown their e ectiveness in this area, and a number of methods have been proposed. In this talk, I will discuss the deep solutions to semantic matching from the aspects of the word and the sentence. At the word-level matching, I will discuss the distributed word representations that bridge the semantic gap between di erent words. At the sentence-level matching, I will discuss the matching methods that capture the proximity and text matching patterns. Potential applications and future directions of semantic text matching will also be discussed.
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