# Domain adapted machine translation: What does catastrophic forgetting forget and why?

arXiv: https://arxiv.org/abs/2412.17537 · HTML: https://mlsanity.com/paper/2412.17537/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 38 references that are in the map.
- First posted on arXiv 23 Dec 2024.

## Builds on (most cited first, 16 of 16)

- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [Unsupervised Domain Clusters in Pretrained Language Models](https://mlsanity.com/paper/2004.02105/) (2020)
- [Tensor2Tensor for Neural Machine Translation](https://mlsanity.com/paper/1803.07416/) (2018)
- [On the Impact of Various Types of Noise on Neural Machine Translation](https://mlsanity.com/paper/1805.12282/) (2018)
- [Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey](https://mlsanity.com/paper/2104.06951/) (2021)
- [Sequence Length is a Domain: Length-based Overfitting in Transformer Models](https://mlsanity.com/paper/2109.07276/) (2021)
- [Regularization techniques for fine-tuning in neural machine translation](https://mlsanity.com/paper/1707.09920/) (2017)
- [Meta-Learning for Few-Shot NMT Adaptation](https://mlsanity.com/paper/2004.02745/) (2020)
- [Overcoming Catastrophic Forgetting beyond Continual Learning: Balanced Training for Neural Machine Translation](https://mlsanity.com/paper/2203.03910/) (2022)
- [Investigating Catastrophic Forgetting During Continual Training for Neural Machine Translation](https://mlsanity.com/paper/2011.00678/) (2020)
- [JParaCrawl v3.0: A Large-scale English-Japanese Parallel Corpus](https://mlsanity.com/paper/2202.12607/) (2022)
- [Designing the Business Conversation Corpus](https://mlsanity.com/paper/2008.01940/) (2020)
- [Addressing Exposure Bias With Document Minimum Risk Training: Cambridge at the WMT20 Biomedical Translation Task](https://mlsanity.com/paper/2010.05333/) (2020)

## Built on by (most cited first, 2 of 2)

- [Conditions for Catastrophic Forgetting in Multilingual Translation](https://mlsanity.com/paper/2510.19546/) (2025)
- [BYOL: Bring Your Own Language Into LLMs](https://mlsanity.com/paper/2601.10804/) (2026)

## Abstract (arXiv)

Neural Machine Translation (NMT) models can be specialized by domain adaptation, often involving fine-tuning on a dataset of interest. This process risks catastrophic forgetting: rapid loss of generic translation quality. Forgetting has been widely observed, with many mitigation methods proposed. However, the causes of forgetting and the relationship between forgetting and adaptation data are under-explored. This paper takes a novel approach to understanding catastrophic forgetting during NMT adaptation by investigating the impact of the data. We provide a first investigation of what is forgotten, and why. We examine the relationship between forgetting and the in-domain data, and show that the amount and type of forgetting is linked to that data's target vocabulary coverage. Our findings pave the way toward better informed NMT domain adaptation.