# Generating and Exploiting Large-scale Pseudo Training Data for Zero Pronoun Resolution

arXiv: https://arxiv.org/abs/1606.01603 · HTML: https://mlsanity.com/paper/1606.01603/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2016.

## Built on by (most cited first, 3 of 3)

- [Pseudo Zero Pronoun Resolution Improves Zero Anaphora Resolution](https://mlsanity.com/paper/2104.07425/) (2021)
- [Data Augmentation Methods for Anaphoric Zero Pronouns](https://mlsanity.com/paper/2109.09825/) (2021)
- [Joint Coreference Resolution for Zeros and non-Zeros in Arabic](https://mlsanity.com/paper/2210.12169/) (2022)

## Abstract (arXiv)

Most existing approaches for zero pronoun resolution are heavily relying on annotated data, which is often released by shared task organizers. Therefore, the lack of annotated data becomes a major obstacle in the progress of zero pronoun resolution task. Also, it is expensive to spend manpower on labeling the data for better performance. To alleviate the problem above, in this paper, we propose a simple but novel approach to automatically generate large-scale pseudo training data for zero pronoun resolution. Furthermore, we successfully transfer the cloze-style reading comprehension neural network model into zero pronoun resolution task and propose a two-step training mechanism to overcome the gap between the pseudo training data and the real one. Experimental results show that the proposed approach significantly outperforms the state-of-the-art systems with an absolute improvements of 3.1% F-score on OntoNotes 5.0 data.