MatchZoo: A Toolkit for Deep Text Matching

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. Speci€cally, the toolkit provides a uni€ed data preparation module for di‚erent text matching problems, a ƒexible 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 interactionfocused models. Finally, users can easily modify existing models, create and share their own models for text matching in MatchZoo.