Abstract
Pronouns are often dropped in Chinese sentences, and this happens more
frequently in conversational genres as their referents can be easily understood
from context. Recovering dropped pronouns is essential to applications such as
Information Extraction where the referents of these dropped pronouns need to be
resolved, or Machine Translation when Chinese is the source language. In this
work, we present a novel end-to-end neural network model to recover dropped
pronouns in conversational data. Our model is based on a structured attention
mechanism that models the referents of dropped pronouns utilizing both
sentence-level and word-level information. Results on three different
conversational genres show that our approach achieves a significant improvement
over the current state of the art.