# Is Robustness Transferable across Languages in Multilingual Neural Machine Translation?

arXiv: https://arxiv.org/abs/2310.20162 · HTML: https://mlsanity.com/paper/2310.20162/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 29 references that are in the map.
- First posted on arXiv 31 Oct 2023.

## Builds on (most cited first, 21 of 21)

- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [Beyond English-Centric Multilingual Machine Translation](https://mlsanity.com/paper/2010.11125/) (2020)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [Generating Natural Language Adversarial Examples](https://mlsanity.com/paper/1804.07998/) (2018)
- [Synthetic and Natural Noise Both Break Neural Machine Translation](https://mlsanity.com/paper/1711.02173/) (2017)
- [Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation](https://mlsanity.com/paper/2004.11867/) (2020)
- [Massively Multilingual Neural Machine Translation](https://mlsanity.com/paper/1903.00089/) (2019)
- [When and Why are Pre-trained Word Embeddings Useful for Neural Machine Translation?](https://mlsanity.com/paper/1804.06323/) (2018)
- [Multi-Way, Multilingual Neural Machine Translation with a Shared Attention Mechanism](https://mlsanity.com/paper/1601.01073/) (2016)
- [Robust Neural Machine Translation with Doubly Adversarial Inputs](https://mlsanity.com/paper/1906.02443/) (2019)
- [Toward Multilingual Neural Machine Translation with Universal Encoder and Decoder](https://mlsanity.com/paper/1611.04798/) (2016)
- [Domain Robustness in Neural Machine Translation](https://mlsanity.com/paper/1911.03109/) (2019)
- [Training on Synthetic Noise Improves Robustness to Natural Noise in Machine Translation](https://mlsanity.com/paper/1902.01509/) (2019)
- [Evaluating Robustness to Input Perturbations for Neural Machine Translation](https://mlsanity.com/paper/2005.00580/) (2020)
- [Supervised and Unsupervised Transfer Learning for Question Answering](https://mlsanity.com/paper/1711.05345/) (2017)
- [Transfer Learning in Multilingual Neural Machine Translation with Dynamic Vocabulary](https://mlsanity.com/paper/1811.01137/) (2018)
- [Distributionally Robust Multilingual Machine Translation](https://mlsanity.com/paper/2109.04020/) (2021)
- [Transfer Learning for Named-Entity Recognition with Neural Networks](https://mlsanity.com/paper/1705.06273/) (2017)
- [Robust Domain Adaptation for Pre-trained Multilingual Neural Machine Translation Models](https://mlsanity.com/paper/2210.14979/) (2022)

## Abstract (arXiv)

Robustness, the ability of models to maintain performance in the face of perturbations, is critical for developing reliable NLP systems. Recent studies have shown promising results in improving the robustness of models through adversarial training and data augmentation. However, in machine translation, most of these studies have focused on bilingual machine translation with a single translation direction. In this paper, we investigate the transferability of robustness across different languages in multilingual neural machine translation. We propose a robustness transfer analysis protocol and conduct a series of experiments. In particular, we use character-, word-, and multi-level noises to attack the specific translation direction of the multilingual neural machine translation model and evaluate the robustness of other translation directions. Our findings demonstrate that the robustness gained in one translation direction can indeed transfer to other translation directions. Additionally, we empirically find scenarios where robustness to character-level noise and word-level noise is more likely to transfer.