# Data Augmentation for Machine Translation via Dependency Subtree Swapping

arXiv: https://arxiv.org/abs/2307.07025 · HTML: https://mlsanity.com/paper/2307.07025/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 37 references that are in the map.
- First posted on arXiv 13 Jul 2023.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [FastText.zip: Compressing text classification models](https://mlsanity.com/paper/1612.03651/) (2016)
- [Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection](https://mlsanity.com/paper/2004.10643/) (2020)
- [OpenNMT: Open-source Toolkit for Neural Machine Translation](https://mlsanity.com/paper/1709.03815/) (2017)
- [Data Augmentation for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1705.00440/) (2017)
- [An Analysis of Simple Data Augmentation for Named Entity Recognition](https://mlsanity.com/paper/2010.11683/) (2020)
- [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)
- [Trivial Transfer Learning for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1809.00357/) (2018)
- [Improved Relation Classification by Deep Recurrent Neural Networks with Data Augmentation](https://mlsanity.com/paper/1601.03651/) (2016)
- [Substructure Substitution: Structured Data Augmentation for NLP](https://mlsanity.com/paper/2101.00411/) (2021)
- [Meta-Learning for Few-Shot NMT Adaptation](https://mlsanity.com/paper/2004.02745/) (2020)
- [On the Role of Supervision in Unsupervised Constituency Parsing](https://mlsanity.com/paper/2010.02423/) (2020)
- [Uncertainty-Aware Semantic Augmentation for Neural Machine Translation](https://mlsanity.com/paper/2010.04411/) (2020)
- [Learning to Generalize to More: Continuous Semantic Augmentation for Neural Machine Translation](https://mlsanity.com/paper/2204.06812/) (2022)
- [Syntax-aware Data Augmentation for Neural Machine Translation](https://mlsanity.com/paper/2004.14200/) (2020)
- [Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach](https://mlsanity.com/paper/2109.03645/) (2021)
- [HuSpaCy: an industrial-strength Hungarian natural language processing toolkit](https://mlsanity.com/paper/2201.01956/) (2022)
- [Augmenting Neural Machine Translation with Knowledge Graphs](https://mlsanity.com/paper/1902.08816/) (2019)
- [Syntax-based data augmentation for Hungarian-English machine translation](https://mlsanity.com/paper/2201.06876/) (2022)

## Built on by (most cited first, 1 of 1)

- [TreeSwap: Data Augmentation for Machine Translation via Dependency Subtree Swapping](https://mlsanity.com/paper/2311.02355/) (2023)

## Abstract (arXiv)

We present a generic framework for data augmentation via dependency subtree swapping that is applicable to machine translation. We extract corresponding subtrees from the dependency parse trees of the source and target sentences and swap these across bisentences to create augmented samples. We perform thorough filtering based on graphbased similarities of the dependency trees and additional heuristics to ensure that extracted subtrees correspond to the same meaning. We conduct resource-constrained experiments on 4 language pairs in both directions using the IWSLT text translation datasets and the Hunglish2 corpus. The results demonstrate consistent improvements in BLEU score over our baseline models in 3 out of 4 language pairs. Our code is available on GitHub.