# Recovering Dropped Pronouns in Chinese Conversations via Modeling Their Referents Source: [Recovering Dropped Pronouns in Chinese Conversations via Modeling Their Referents]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:36:37.771Z Authors: Jingxuan Yang, Jianzhuo Tong, Si Li, Sheng Gao, Jun Guo, Nianwen Xue Published: 2019-05-17T05:13:22.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Dropped Pronoun Recovery on OntoNotes TC 5.0 (CoNLL 2012 Shared Task) (test)](): Dropped Pronoun Recovery; OntoNotes TC 5.0 (CoNLL 2012 Shared Task) (test); Precision: 0.3963. - [Dropped Pronoun Recovery on BaiduZhidao (test)](): Dropped Pronoun Recovery; BaiduZhidao (test); Precision: 41.04. - [Dropped Pronoun Recovery on SPDPR (test)](): Dropped Pronoun Recovery; SPDPR (test); Precision: 49.39. ## Original sources and implementation [Original paper]() [PDF]()