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The short text matching task employs a model to determine whether two shorttexts have the same semantic meaning or intent. Existing short text matchingmodels usually rely on the content of short texts which are lack information ormissing some key clues. Therefore, the short texts need external knowledge tocomplete their semantic meaning. To address this issue, we propose a new shorttext matching framework for introducing external knowledge to enhance the shorttext contextual representation. In detail, we apply a self-attention mechanismto enrich short text representation with external contexts. Experiments on twoChinese datasets and one English dataset demonstrate that our frameworkoutperforms the state-of-the-art short text matching models.

Context Enhanced Short Text Matching using Clickthrough Data