A Deep Neural Network for Chinese Zero Pronoun Resolution
This work improves natural language processing for Chinese by resolving zero pronouns more accurately, though it is incremental as it builds on prior methods.
The paper tackles the problem of Chinese zero pronoun resolution by addressing the oversight of semantic information and local-only candidate processing in existing approaches, resulting in a method that substantially outperforms the state-of-the-art on the OntoNotes 5.0 corpus.
Existing approaches for Chinese zero pronoun resolution overlook semantic information. This is because zero pronouns have no descriptive information, which results in difficulty in explicitly capturing their semantic similarities with antecedents. Moreover, when dealing with candidate antecedents, traditional systems simply take advantage of the local information of a single candidate antecedent while failing to consider the underlying information provided by the other candidates from a global perspective. To address these weaknesses, we propose a novel zero pronoun-specific neural network, which is capable of representing zero pronouns by utilizing the contextual information at the semantic level. In addition, when dealing with candidate antecedents, a two-level candidate encoder is employed to explicitly capture both the local and global information of candidate antecedents. We conduct experiments on the Chinese portion of the OntoNotes 5.0 corpus. Experimental results show that our approach substantially outperforms the state-of-the-art method in various experimental settings.