# Multi-Domain Adaptation in Neural Machine Translation Through Multidimensional Tagging

arXiv: https://arxiv.org/abs/2102.10160 · HTML: https://mlsanity.com/paper/2102.10160/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 35 references that are in the map.
- First posted on arXiv 19 Feb 2021.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [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)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [Convolutional Sequence to Sequence Learning](https://mlsanity.com/paper/1705.03122/) (2017)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [OpenNMT: Open-source Toolkit for Neural Machine Translation](https://mlsanity.com/paper/1709.03815/) (2017)
- [CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB](https://mlsanity.com/paper/1911.04944/) (2019)
- [Tagged Back-Translation](https://mlsanity.com/paper/1906.06442/) (2019)
- [A Survey of Domain Adaptation for Neural Machine Translation](https://mlsanity.com/paper/1806.00258/) (2018)
- [Domain Control for Neural Machine Translation](https://mlsanity.com/paper/1612.06140/) (2016)
- [SYSTRAN's Pure Neural Machine Translation Systems](https://mlsanity.com/paper/1610.05540/) (2016)
- [Gender Aware Spoken Language Translation Applied to English-Arabic](https://mlsanity.com/paper/1802.09287/) (2018)
- [Machine Translation at Booking.com: Journey and Lessons Learned](https://mlsanity.com/paper/1707.07911/) (2017)
- [Domain Adaptation for Statistical Machine Translation](https://mlsanity.com/paper/1804.01760/) (2018)
- [Toward a full-scale neural machine translation in production: the Booking.com use case](https://mlsanity.com/paper/1709.05820/) (2017)

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

- [Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey](https://mlsanity.com/paper/2104.06951/) (2021)
- [Multilingual Domain Adaptation for NMT: Decoupling Language and Domain Information with Adapters](https://mlsanity.com/paper/2110.09574/) (2021)
- [Domain-Specific Text Generation for Machine Translation](https://mlsanity.com/paper/2208.05909/) (2022)
- [Selecting Parallel In-domain Sentences for Neural Machine Translation Using Monolingual Texts](https://mlsanity.com/paper/2112.06096/) (2021)
- [Language Modelling Approaches to Adaptive Machine Translation](https://mlsanity.com/paper/2401.14559/) (2024)
- [Treasure Hunt: Real-time Targeting of the Long Tail using Training-Time Markers](https://mlsanity.com/paper/2506.14702/) (2025)
- [From Priest to Doctor: Domain Adaptation for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/2412.00966/) (2024)
- [In-Domain African Languages Translation Using LLMs and Multi-armed Bandits](https://mlsanity.com/paper/2505.15069/) (2025)

## Abstract (arXiv)

While NMT has achieved remarkable results in the last 5 years, production systems come with strict quality requirements in arbitrarily niche domains that are not always adequately covered by readily available parallel corpora. This is typically addressed by training domain specific models, using fine-tuning methods and some variation of back-translation on top of in-domain monolingual corpora. However, industrial practitioners can rarely afford to focus on a single domain. A far more typical scenario includes a set of closely related, yet succinctly different sub-domains. At Booking.com, we need to translate property descriptions, user reviews, as well as messages, (for example those sent between a customer and an agent or property manager). An editor might need to translate articles across a set of different topics. An e-commerce platform would typically need to translate both the description of each item and the user generated content related to them. To this end, we propose MDT: a novel method to simultaneously fine-tune on several sub-domains by passing multidimensional sentence-level information to the model during training and inference. We show that MDT achieves results competitive to N specialist models each fine-tuned on a single constituent domain, while effectively serving all N sub-domains, therefore cutting development and maintenance costs by the same factor. Besides BLEU (industry standard automatic evaluation metric known to only weakly correlate with human judgement) we also report rigorous human evaluation results for all models and sub-domains as well as specific examples that better contextualise the performance of each model in terms of adequacy and fluency. To facilitate further research, we plan to make the code available upon acceptance.