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Matching natural language sentences is central for many applications such asinformation retrieval and question answering. Existing deep models rely on asingle sentence representation or multiple granularity representations formatching. However, such methods cannot well capture the contextualized localinformation in the matching process. To tackle this problem, we present a newdeep architecture to match two sentences with multiple positional sentencerepresentations. Specifically, each positional sentence representation is asentence representation at this position, generated by a bidirectional longshort term memory (Bi-LSTM). The matching score is finally produced byaggregating interactions between these different positional sentencerepresentations, through $k$-Max pooling and a multi-layer perceptron. Ourmodel has several advantages: (1) By using Bi-LSTM, rich context of the wholesentence is leveraged to capture the contextualized local information in eachpositional sentence representation; (2) By matching with multiple positionalsentence representations, it is flexible to aggregate different importantcontextualized local information in a sentence to support the matching; (3)Experiments on different tasks such as question answering and sentencecompletion demonstrate the superiority of our model.

A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations