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First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. Summary: The paper presents two novel convolutional neural network architectures for modeling sentences in natural languages. These networks are trained specifically for the problem of matching a pair of sentences. The first architecture is a minor modification to the standard way of using a convolutional network over natural language sentences. After a convolution operation on the word embeddings, instead of doing a pooling operation across time (full sequence of words in a sentence) to select a single feature (or k features), the proposed model applies pooling to features associated with consecutive pairs of words.