Chinese zero pronoun resolution with deep neural networks

Abstract

While unsupervised anaphoric zero pronoun (AZP) resolvers have recently been shown to rival their supervised counterparts in performance, it is relatively difficult to scale them up to reach the next level of performance due to the large amount of feature engineering efforts involved and their ineffectiveness in exploiting lexical features. To address these weaknesses, we propose a supervised approach to AZP resolution based on deep neural networks, taking advantage of their ability to learn useful task-specific representations and effectively exploit lexical features via word embeddings. Our approach achieves stateof-the-art performance when resolving the Chinese AZPs in the OntoNotes corpus.