  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. In addition, the toolkit has implemented two
schools of representative deep text matching models, namely
representation-focused models and interaction-focused models. Finally, users
can easily modify existing models, create and share their own models for text
matching in MatchZoo.
