# Zero-pronoun Data Augmentation for Japanese-to-English Translation

arXiv: https://arxiv.org/abs/2107.00318 · HTML: https://mlsanity.com/paper/2107.00318/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 23 references that are in the map.
- First posted on arXiv 1 Jul 2021.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Neural Machine Translation with Extended Context](https://mlsanity.com/paper/1708.05943/) (2017)
- [Evaluating Discourse Phenomena in Neural Machine Translation](https://mlsanity.com/paper/1711.00513/) (2017)
- [Context-Aware Monolingual Repair for Neural Machine Translation](https://mlsanity.com/paper/1909.01383/) (2019)
- [Does Neural Machine Translation Benefit from Larger Context?](https://mlsanity.com/paper/1704.05135/) (2017)
- [Optimizing Transformer for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/2011.02266/) (2020)
- [One Model to Learn Both: Zero Pronoun Prediction and Translation](https://mlsanity.com/paper/1909.00369/) (2019)
- [Designing the Business Conversation Corpus](https://mlsanity.com/paper/2008.01940/) (2020)
- [A Novel Approach to Dropped Pronoun Translation](https://mlsanity.com/paper/1604.06285/) (2016)
- [Using Context in Neural Machine Translation Training Objectives](https://mlsanity.com/paper/2005.01483/) (2020)
- [Document-aligned Japanese-English Conversation Parallel Corpus](https://mlsanity.com/paper/2012.06143/) (2020)

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

- [A Survey on Zero Pronoun Translation](https://mlsanity.com/paper/2305.10196/) (2023)
- [Data Augmentation Methods for Anaphoric Zero Pronouns](https://mlsanity.com/paper/2109.09825/) (2021)
- [Context-aware Neural Machine Translation for English-Japanese Business Scene Dialogues](https://mlsanity.com/paper/2311.11976/) (2023)

## Abstract (arXiv)

For Japanese-to-English translation, zero pronouns in Japanese pose a challenge, since the model needs to infer and produce the corresponding pronoun in the target side of the English sentence. However, although fully resolving zero pronouns often needs discourse context, in some cases, the local context within a sentence gives clues to the inference of the zero pronoun. In this study, we propose a data augmentation method that provides additional training signals for the translation model to learn correlations between local context and zero pronouns. We show that the proposed method significantly improves the accuracy of zero pronoun translation with machine translation experiments in the conversational domain.