# The Source-Target Domain Mismatch Problem in Machine Translation

arXiv: https://arxiv.org/abs/1909.13151 · HTML: https://mlsanity.com/paper/1909.13151/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2019.

## Built on by (most cited first, 8 of 8)

- [Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP](https://mlsanity.com/paper/2110.03618/) (2021)
- [On the Language Coverage Bias for Neural Machine Translation](https://mlsanity.com/paper/2106.03297/) (2021)
- [MTet: Multi-domain Translation for English and Vietnamese](https://mlsanity.com/paper/2210.05610/) (2022)
- [Rule-Based, Neural and LLM Back-Translation: Comparative Insights from a Variant of Ladin](https://mlsanity.com/paper/2407.08819/) (2024)
- [Cross-lingual Human-Preference Alignment for Neural Machine Translation with Direct Quality Optimization](https://mlsanity.com/paper/2409.17673/) (2024)
- [Enriching Biomedical Knowledge for Low-resource Language Through Large-Scale Translation](https://mlsanity.com/paper/2210.05598/) (2022)
- [Domain Mismatch Doesn't Always Prevent Cross-Lingual Transfer Learning](https://mlsanity.com/paper/2211.16671/) (2022)
- [BYOL: Bring Your Own Language Into LLMs](https://mlsanity.com/paper/2601.10804/) (2026)

## Abstract (arXiv)

While we live in an increasingly interconnected world, different places still exhibit strikingly different cultures and many events we experience in our every day life pertain only to the specific place we live in. As a result, people often talk about different things in different parts of the world. In this work we study the effect of local context in machine translation and postulate that particularly in low resource settings this causes the domains of the source and target language to greatly mismatch, as the two languages are often spoken in further apart regions of the world with more distinctive cultural traits and unrelated local events. We first formalize the concept of source-target domain mismatch, propose a metric to quantify it, and provide empirical evidence corroborating our intuition that organic text produced by people speaking very different languages exhibits the most dramatic differences. We conclude with an empirical study of how source-target domain mismatch affects training of machine translation systems for low resource language pairs. In particular, we find that it severely affects back-translation, but the degradation can be alleviated by combining back-translation with self-training and by increasing the relative amount of target side monolingual data.