In this paper, we propose an adversarial multicriteria learning for CWS by integrating shared knowledge from multiple segmentation criteria.
we regard each segmentation criterion as a single task and propose three different shared-private models under the framework of multi-task learning (Caruana, 1997;Ben-David and Schuller, 2003), where a shared layer is used to extract the criteria-invariant features, and a private layer is used to extract the criteria-specific features.