# Investigating Backtranslation in Neural Machine Translation

arXiv: https://arxiv.org/abs/1804.06189 · HTML: https://mlsanity.com/paper/1804.06189/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2018.

## Built on by (most cited first, 14 of 14)

- [Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey](https://mlsanity.com/paper/2104.06951/) (2021)
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- [Unsupervised Neural Machine Translation with Generative Language Models Only](https://mlsanity.com/paper/2110.05448/) (2021)
- [A Survey on Low-Resource Neural Machine Translation](https://mlsanity.com/paper/2107.04239/) (2021)
- [Original or Translated? A Causal Analysis of the Impact of Translationese on Machine Translation Performance](https://mlsanity.com/paper/2205.02293/) (2022)
- [Comparison of Grammatical Error Correction Using Back-Translation Models](https://mlsanity.com/paper/2104.07848/) (2021)
- [Attentive fine-tuning of Transformers for Translation of low-resourced languages @LoResMT 2021](https://mlsanity.com/paper/2108.08556/) (2021)
- [Lean-ing on Quality: How High-Quality Data Beats Diverse Multilingual Data in AutoFormalization](https://mlsanity.com/paper/2502.15795/) (2025)
- [Learning from Many Voices: Literary MT Using Multi-Reference Human and Synthetic Data](https://mlsanity.com/paper/2412.18707/) (2024)
- [The Impact of Syntactic and Semantic Proximity on Machine Translation with Back-Translation](https://mlsanity.com/paper/2403.18031/) (2024)
- [Self-Learning for Zero Shot Neural Machine Translation](https://mlsanity.com/paper/2103.05951/) (2021)
- [Domain Adaptation for Arabic Machine Translation: The Case of Financial Texts](https://mlsanity.com/paper/2309.12863/) (2023)
- [Neural machine translation system for Lezgian, Russian and Azerbaijani languages](https://mlsanity.com/paper/2410.05472/) (2024)
- [TransClean: A Benchmark for Detecting and Extracting Clean Translations from Large Language Model Outputs](https://mlsanity.com/paper/2609.11399/) (2026)

## Abstract (arXiv)

A prerequisite for training corpus-based machine translation (MT) systems -- either Statistical MT (SMT) or Neural MT (NMT) -- is the availability of high-quality parallel data. This is arguably more important today than ever before, as NMT has been shown in many studies to outperform SMT, but mostly when large parallel corpora are available; in cases where data is limited, SMT can still outperform NMT. Recently researchers have shown that back-translating monolingual data can be used to create synthetic parallel corpora, which in turn can be used in combination with authentic parallel data to train a high-quality NMT system. Given that large collections of new parallel text become available only quite rarely, backtranslation has become the norm when building state-of-the-art NMT systems, especially in resource-poor scenarios. However, we assert that there are many unknown factors regarding the actual effects of back-translated data on the translation capabilities of an NMT model. Accordingly, in this work we investigate how using back-translated data as a training corpus -- both as a separate standalone dataset as well as combined with human-generated parallel data -- affects the performance of an NMT model. We use incrementally larger amounts of back-translated data to train a range of NMT systems for German-to-English, and analyse the resulting translation performance.