# Deep Reinforcement Learning for Chinese Zero pronoun Resolution

arXiv: https://arxiv.org/abs/1806.03711 · HTML: https://mlsanity.com/paper/1806.03711/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2018.

## Built on by (most cited first, 5 of 5)

- [An Empirical Comparison on Imitation Learning and Reinforcement Learning for Paraphrase Generation](https://mlsanity.com/paper/1908.10835/) (2019)
- [A Survey on Semantic Processing Techniques](https://mlsanity.com/paper/2310.18345/) (2023)
- [A Joint Model for Dropped Pronoun Recovery and Conversational Discourse Parsing in Chinese Conversational Speech](https://mlsanity.com/paper/2106.03345/) (2021)
- [RLAP: A Reinforcement Learning Enhanced Adaptive Planning Framework for Multi-step NLP Task Solving](https://mlsanity.com/paper/2505.11893/) (2025)
- [How Much Do LLMs Know About Chinese Zero Pronouns?](https://mlsanity.com/paper/2605.31056/) (2026)

## Abstract (arXiv)

Deep neural network models for Chinese zero pronoun resolution learn semantic information for zero pronoun and candidate antecedents, but tend to be short-sighted---they often make local decisions. They typically predict coreference chains between the zero pronoun and one single candidate antecedent one link at a time, while overlooking their long-term influence on future decisions. Ideally, modeling useful information of preceding potential antecedents is critical when later predicting zero pronoun-candidate antecedent pairs. In this study, we show how to integrate local and global decision-making by exploiting deep reinforcement learning models. With the help of the reinforcement learning agent, our model learns the policy of selecting antecedents in a sequential manner, where useful information provided by earlier predicted antecedents could be utilized for making later coreference decisions. Experimental results on OntoNotes 5.0 dataset show that our technique surpasses the state-of-the-art models.