mlsanityData to 7 Oct 2026

Data Augmentation for Machine Translation via Dependency Subtree Swapping

arXiv 2307.07025 ↗First posted 13 Jul 2023cs.CLcited by 1

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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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 25 of its 37 references that are in the map. First posted on arXiv 13 Jul 2023.

Builds on

25 of 37 references.

References · 25

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Improving Neural Machine Translation Models with Monolingual Datacited by 784
  3. 2020Stanza: A Python Natural Language Processing Toolkit for Many Human Languagescited by 719
  4. 2016Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translationcited by 467
  5. 2021A Survey of Data Augmentation Approaches for NLPcited by 408
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  7. 2020Universal Dependencies v2: An Evergrowing Multilingual Treebank Collectioncited by 269
  8. 2017OpenNMT: Open-source Toolkit for Neural Machine Translationcited by 251
  9. 2017Data Augmentation for Low-Resource Neural Machine Translationcited by 139
  10. 2020An Analysis of Simple Data Augmentation for Named Entity Recognitioncited by 75
  11. 2018SwitchOut: an Efficient Data Augmentation Algorithm for Neural Machine Translationcited by 72
  12. 2020Sequence-Level Mixed Sample Data Augmentationcited by 71
13 more
  1. 2019Soft Contextual Data Augmentation for Neural Machine Translationcited by 49
  2. 2018Trivial Transfer Learning for Low-Resource Neural Machine Translationcited by 47
  3. 2016Improved Relation Classification by Deep Recurrent Neural Networks with Data Augmentationcited by 26
  4. 2021Substructure Substitution: Structured Data Augmentation for NLPcited by 25
  5. 2020Meta-Learning for Few-Shot NMT Adaptationcited by 20
  6. 2020On the Role of Supervision in Unsupervised Constituency Parsingcited by 18
  7. 2020Uncertainty-Aware Semantic Augmentation for Neural Machine Translationcited by 13
  8. 2022Learning to Generalize to More: Continuous Semantic Augmentation for Neural Machine Translationcited by 13
  9. 2020Syntax-aware Data Augmentation for Neural Machine Translationcited by 10
  10. 2021Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approachcited by 8
  11. 2022HuSpaCy: an industrial-strength Hungarian natural language processing toolkitcited by 8
  12. 2019Augmenting Neural Machine Translation with Knowledge Graphscited by 7
  13. 2022Syntax-based data augmentation for Hungarian-English machine translationcited by 1

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Citing papers per year: 2023: 1.

1 paper.

  1. 2023TreeSwap: Data Augmentation for Machine Translation via Dependency Subtree Swappingcited by 2