mlsanityData to 7 Oct 2026

A Morphologically-Aware Dictionary-based Data Augmentation Technique for Machine Translation of Under-Represented Languages

arXiv 2402.01939 ↗First posted 2 Feb 2024cs.CLcited by 2

Not on the map. Why?

Abstract (arXiv)

The availability of parallel texts is crucial to the performance of machine translation models. However, most of the world's languages face the predominant challenge of data scarcity. In this paper, we propose strategies to synthesize parallel data relying on morpho-syntactic information and using bilingual lexicons along with a small amount of seed parallel data. Our methodology adheres to a realistic scenario backed by the small parallel seed data. It is linguistically informed, as it aims to create augmented data that is more likely to be grammatically correct. We analyze how our synthetic data can be combined with raw parallel data and demonstrate a consistent improvement in performance in our experiments on 14 languages (28 English <-> X pairs) ranging from well- to very low-resource ones. Our method leads to improvements even when using only five seed sentences and a bilingual lexicon.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 20 of its 34 references that are in the map. First posted on arXiv 2 Feb 2024.

Builds on

20 of 34 references.

References · 20

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,898
  3. 2022No Language Left Behind: Scaling Human-Centered Machine Translationcited by 1,290
  4. 2015Improving Neural Machine Translation Models with Monolingual Datacited by 784
  5. 2020Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translationcited by 277
  6. 2017Unsupervised Machine Translation Using Monolingual Corpora Onlycited by 272
  7. 2019Simple, Scalable Adaptation for Neural Machine Translationcited by 239
  8. 2013Exploiting Similarities among Languages for Machine Translationcited by 187
  9. 2022Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuningcited by 149
  10. 2017Unsupervised Neural Machine Translationcited by 148
  11. 2017Data Augmentation for Low-Resource Neural Machine Translationcited by 139
  12. 2018Phrase-Based & Neural Unsupervised Machine Translationcited by 138
8 more
  1. 2021DeltaLM: Encoder-Decoder Pre-training for Language Generation and Translation by Augmenting Pretrained Multilingual Encoderscited by 50
  2. 2022Expanding Pretrained Models to Thousands More Languages via Lexicon-based Adaptationcited by 44
  3. 2016Incorporating Discrete Translation Lexicons into Neural Machine Translationcited by 33
  4. 2020Bilingual Dictionary Based Neural Machine Translation without Using Parallel Sentencescited by 19
  5. 2016Bridging Neural Machine Translation and Bilingual Dictionariescited by 18
  6. 2020Dictionary-based Data Augmentation for Cross-Domain Neural Machine Translationcited by 13
  7. 2021Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approachcited by 8
  8. 2018Incorporating Dictionaries into Deep Neural Networks for the Chinese Clinical Named Entity Recognitioncited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026AFRILANGTUTOR: Advancing Language Tutoring and Culture Education in Low-Resource Languages with Large Language Modelscited by 1
  2. 2026A Factorial Study of Synthetic Data Generation for Low-Resource Machine Translation using Grammar Bookscited by 0