# Data Augmentation Methods for Anaphoric Zero Pronouns

arXiv: https://arxiv.org/abs/2109.09825 · HTML: https://mlsanity.com/paper/2109.09825/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 89 references that are in the map.
- First posted on arXiv 20 Sep 2021.

## Builds on (most cited first, 26 of 26)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [Colorless green recurrent networks dream hierarchically](https://mlsanity.com/paper/1803.11138/) (2018)
- [Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations](https://mlsanity.com/paper/1805.06201/) (2018)
- [Not Enough Data? Deep Learning to the Rescue!](https://mlsanity.com/paper/1911.03118/) (2019)
- [Good-Enough Compositional Data Augmentation](https://mlsanity.com/paper/1904.09545/) (2019)
- [An Empirical Survey of Data Augmentation for Limited Data Learning in NLP](https://mlsanity.com/paper/2106.07499/) (2021)
- [SwitchOut: an Efficient Data Augmentation Algorithm for Neural Machine Translation](https://mlsanity.com/paper/1808.07512/) (2018)
- [Sequence-Level Mixed Sample Data Augmentation](https://mlsanity.com/paper/2011.09039/) (2020)
- [Soft Contextual Data Augmentation for Neural Machine Translation](https://mlsanity.com/paper/1905.10523/) (2019)
- [GenAug: Data Augmentation for Finetuning Text Generators](https://mlsanity.com/paper/2010.01794/) (2020)
- [Improving Robustness of Machine Translation with Synthetic Noise](https://mlsanity.com/paper/1902.09508/) (2019)
- [XLDA: Cross-Lingual Data Augmentation for Natural Language Inference and Question Answering](https://mlsanity.com/paper/1905.11471/) (2019)
- [Data Augmentation for BERT Fine-Tuning in Open-Domain Question Answering](https://mlsanity.com/paper/1904.06652/) (2019)
- [Keep Calm and Switch On! Preserving Sentiment and Fluency in Semantic Text Exchange](https://mlsanity.com/paper/1909.00088/) (2019)
- [Sequence-to-sequence Pre-training with Data Augmentation for Sentence Rewriting](https://mlsanity.com/paper/1909.06002/) (2019)
- [Simple is Better! Lightweight Data Augmentation for Low Resource Slot Filling and Intent Classification](https://mlsanity.com/paper/2009.03695/) (2020)
- [Finding Friends and Flipping Frenemies: Automatic Paraphrase Dataset Augmentation Using Graph Theory](https://mlsanity.com/paper/2011.01856/) (2020)
- [Neural Coreference Resolution for Arabic](https://mlsanity.com/paper/2011.00286/) (2020)
- [A Deep Neural Network for Chinese Zero Pronoun Resolution](https://mlsanity.com/paper/1604.05800/) (2016)
- [An Empirical Study of Contextual Data Augmentation for Japanese Zero Anaphora Resolution](https://mlsanity.com/paper/2011.00948/) (2020)
- [Generating and Exploiting Large-scale Pseudo Training Data for Zero Pronoun Resolution](https://mlsanity.com/paper/1606.01603/) (2016)
- [Zero-pronoun Data Augmentation for Japanese-to-English Translation](https://mlsanity.com/paper/2107.00318/) (2021)

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

- [A Survey on Semantic Processing Techniques](https://mlsanity.com/paper/2310.18345/) (2023)
- [Aggregating Crowdsourced and Automatic Judgments to Scale Up a Corpus of Anaphoric Reference for Fiction and Wikipedia Texts](https://mlsanity.com/paper/2210.05581/) (2022)
- [Investigating Multilingual Coreference Resolution by Universal Annotations](https://mlsanity.com/paper/2310.17734/) (2023)

## Abstract (arXiv)

In pro-drop language like Arabic, Chinese, Italian, Japanese, Spanish, and many others, unrealized (null) arguments in certain syntactic positions can refer to a previously introduced entity, and are thus called anaphoric zero pronouns. The existing resources for studying anaphoric zero pronoun interpretation are however still limited. In this paper, we use five data augmentation methods to generate and detect anaphoric zero pronouns automatically. We use the augmented data as additional training materials for two anaphoric zero pronoun systems for Arabic. Our experimental results show that data augmentation improves the performance of the two systems, surpassing the state-of-the-art results.