# Improving Robustness of Machine Translation with Synthetic Noise

arXiv: https://arxiv.org/abs/1902.09508 · HTML: https://mlsanity.com/paper/1902.09508/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2019.

## Built on by (most cited first, 30 of 36)

- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [Measure and Improve Robustness in NLP Models: A Survey](https://mlsanity.com/paper/2112.08313/) (2021)
- [Robust Open-Vocabulary Translation from Visual Text Representations](https://mlsanity.com/paper/2104.08211/) (2021)
- [Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey](https://mlsanity.com/paper/2104.06951/) (2021)
- [Improving Zero-shot Cross-lingual Transfer between Closely Related Languages by injecting Character-level Noise](https://mlsanity.com/paper/2109.06772/) (2021)
- [Make Every Example Count: On the Stability and Utility of Self-Influence for Learning from Noisy NLP Datasets](https://mlsanity.com/paper/2302.13959/) (2023)
- [Text Generation: A Systematic Literature Review of Tasks, Evaluation, and Challenges](https://mlsanity.com/paper/2405.15604/) (2024)
- [To Augment or Not to Augment? A Comparative Study on Text Augmentation Techniques for Low-Resource NLP](https://mlsanity.com/paper/2111.09618/) (2021)
- [A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunities](https://mlsanity.com/paper/2210.08906/) (2022)
- [Did Translation Models Get More Robust Without Anyone Even Noticing?](https://mlsanity.com/paper/2403.03923/) (2024)
- [Autocorrect in the Process of Translation -- Multi-task Learning Improves Dialogue Machine Translation](https://mlsanity.com/paper/2103.16189/) (2021)
- [Addressing the Vulnerability of NMT in Input Perturbations](https://mlsanity.com/paper/2104.09810/) (2021)
- [BERTwich: Extending BERT's Capabilities to Model Dialectal and Noisy Text](https://mlsanity.com/paper/2311.00116/) (2023)
- [Noisy UGC Translation at the Character Level: Revisiting Open-Vocabulary Capabilities and Robustness of Char-Based Models](https://mlsanity.com/paper/2110.12552/) (2021)
- [Language and Speech Technology for Central Kurdish Varieties](https://mlsanity.com/paper/2403.01983/) (2024)
- [Synthetic Data Generation and Joint Learning for Robust Code-Mixed Translation](https://mlsanity.com/paper/2403.16771/) (2024)
- [Improving the Robustness of Summarization Models by Detecting and Removing Input Noise](https://mlsanity.com/paper/2212.09928/) (2022)
- [Sometimes We Want Translationese](https://mlsanity.com/paper/2104.07623/) (2021)
- [Data Augmentation Methods for Anaphoric Zero Pronouns](https://mlsanity.com/paper/2109.09825/) (2021)
- [vec2text with Round-Trip Translations](https://mlsanity.com/paper/2209.06792/) (2022)
- [Checks and Strategies for Enabling Code-Switched Machine Translation](https://mlsanity.com/paper/2210.05096/) (2022)
- [ViPE: Visualise Pretty-much Everything](https://mlsanity.com/paper/2310.10543/) (2023)
- [Misspellings in Natural Language Processing: A survey](https://mlsanity.com/paper/2501.16836/) (2025)
- [Visual Cues and Error Correction for Translation Robustness](https://mlsanity.com/paper/2103.07352/) (2021)
- [Secoco: Self-Correcting Encoding for Neural Machine Translation](https://mlsanity.com/paper/2108.12137/) (2021)
- [PAEG: Phrase-level Adversarial Example Generation for Neural Machine Translation](https://mlsanity.com/paper/2201.02009/) (2022)
- [CRAT: A Multi-Agent Framework for Causality-Enhanced Reflective and Retrieval-Augmented Translation with Large Language Models](https://mlsanity.com/paper/2410.21067/) (2024)
- [Fine-Tuning on Noisy Instructions: Effects on Generalization and Performance](https://mlsanity.com/paper/2510.03528/) (2025)
- [CharSpan: Utilizing Lexical Similarity to Enable Zero-Shot Machine Translation for Extremely Low-resource Languages](https://mlsanity.com/paper/2305.05214/) (2023)
- [From Priest to Doctor: Domain Adaptation for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/2412.00966/) (2024)

## Abstract (arXiv)

Modern Machine Translation (MT) systems perform consistently well on clean, in-domain text. However most human generated text, particularly in the realm of social media, is full of typos, slang, dialect, idiolect and other noise which can have a disastrous impact on the accuracy of output translation. In this paper we leverage the Machine Translation of Noisy Text (MTNT) dataset to enhance the robustness of MT systems by emulating naturally occurring noise in otherwise clean data. Synthesizing noise in this manner we are ultimately able to make a vanilla MT system resilient to naturally occurring noise and partially mitigate loss in accuracy resulting therefrom.