Domain Information Enhanced Dependency Parser

Dependency parsing has been an important task in the natural language processing (NLP) community. Supervised methods have achieved great success these years. However, these models can suffer significant performance loss when test domain differs from the training domain. In this paper, we adopt the Bi-Affine parser as our baseline. To explore domain-specific information and domain-independent information for cross-domain dependency parsing, we apply an ensemble-style self-training and adversarial learning, respectively. We finally combine the two strategies to enhance our baseline model and our final system was ranked the first of at NLPCC2019 shared task on cross-domain dependency parsing.