MatchZoo: A Toolkit for Deep Text Matching

23 Jul 2017Yixing Fan • Liang Pang • JianPeng Hou • Jiafeng Guo • Yanyan Lan • Xueqi Cheng

In recent years, deep neural models have been widely adopted for text matching tasks, such as question answering and information retrieval, showing improved performance as compared with previous methods. In this paper, we introduce the MatchZoo toolkit that aims to facilitate the designing, comparing and sharing of deep text matching models. Specifically, the toolkit provides a unified data preparation module for different text matching problems, a flexible layer-based model construction process, and a variety of training objectives and evaluation metrics.

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