Anaphora resolution with pointer networks

Anaphora resolution is a fundamental problem in natural language processing. In this paper, we cast an anaphora resolution problem to a statistical pointing problem. To address the problem, we use the Pointer Networks model for a neural sequential anaphora pointing architecture. The proposed model automatically captures syntactic and semantic features to resolve anaphora by encoding the input tokens and decoding the chain form of the anaphora with attention mechanism. Our approach does not require any handcrafting features or language-specific rules to implement anaphora resolvers. Furthermore, it shows state-of-the-art performance.