# Neural Recovery Machine for Chinese Dropped Pronoun Source: [Neural Recovery Machine for Chinese Dropped Pronoun]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:58:21.378Z Authors: Wei-Nan Zhang, Ting Liu, Qingyu Yin, Yu Zhang Published: 2016-05-07T02:41:54.000Z ## Abstract Dropped pronouns (DPs) are ubiquitous in pro-drop languages like Chinese, Japanese etc. Previous work mainly focused on painstakingly exploring the empirical features for DPs recovery. In this paper, we propose a neural recovery machine (NRM) to model and recover DPs in Chinese, so that to avoid the non-trivial feature engineering process. The experimental results show that the proposed NRM significantly outperforms the state-of-the-art approaches on both two heterogeneous datasets. Further experiment results of Chinese zero pronoun (ZP) resolution show that the performance of ZP resolution can also be improved by recovering the ZPs to DPs. ## 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: 0.2312. - [Dropped Pronoun Recovery on BaiduZhidao (test)](): Dropped Pronoun Recovery; BaiduZhidao (test); Precision: 26.87. - [Dropped Pronoun Recovery on SPDPR (test)](): Dropped Pronoun Recovery; SPDPR (test); Precision: 37.11. ## Original sources and implementation [Original paper]() [PDF]()