Dropped Pronoun Recovery in Chinese Conversations with Knowledge-Enriched Neural Network

Abstract

Dropped pronoun recovery, which aims to detect the type of pronoun dropped before each token, plays a vital role in many applications such as Machine Translation and Information Extraction. Recently, deep neural networks have been applied to this task. Though promising improvements have been observed, these methods recover dropped pronouns from the limited context in a small-size window and lack common sense to connect the referred entity to a proper pronoun. In this paper, we propose a knowledge-enriched neural attention framework for Chinese dropped pronoun (DP) recovery. A structured attention mechanism is used to capture the semantics of DP referents from the wider context. External knowledge, which consists of a knowledge base and a hierarchical pronoun-category assumption, is also incorporated in our model to provide pronoun classification information of referred entity and contextual dependency degree. Results on three different conversational genres show that our approach achieves a convincing improvement over the current state of the art.