# Can Domains Be Transferred Across Languages in Multi-Domain Multilingual Neural Machine Translation?

arXiv: https://arxiv.org/abs/2210.11628 · HTML: https://mlsanity.com/paper/2210.11628/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 34 references that are in the map.
- First posted on arXiv 20 Oct 2022.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [Six Challenges for Neural Machine Translation](https://mlsanity.com/paper/1706.03872/) (2017)
- [Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges](https://mlsanity.com/paper/1907.05019/) (2019)
- [Simple, Scalable Adaptation for Neural Machine Translation](https://mlsanity.com/paper/1909.08478/) (2019)
- [Transfer Learning for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1604.02201/) (2016)
- [Multi-Way, Multilingual Neural Machine Translation with a Shared Attention Mechanism](https://mlsanity.com/paper/1601.01073/) (2016)
- [Beyond Distillation: Task-level Mixture-of-Experts for Efficient Inference](https://mlsanity.com/paper/2110.03742/) (2021)
- [Toward Multilingual Neural Machine Translation with Universal Encoder and Decoder](https://mlsanity.com/paper/1611.04798/) (2016)
- [Analyzing the Source and Target Contributions to Predictions in Neural Machine Translation](https://mlsanity.com/paper/2010.10907/) (2020)
- [Facebook AI WMT21 News Translation Task Submission](https://mlsanity.com/paper/2108.03265/) (2021)
- [Domain Control for Neural Machine Translation](https://mlsanity.com/paper/1612.06140/) (2016)
- [Fast Domain Adaptation for Neural Machine Translation](https://mlsanity.com/paper/1612.06897/) (2016)
- [Multilingual Domain Adaptation for NMT: Decoupling Language and Domain Information with Adapters](https://mlsanity.com/paper/2110.09574/) (2021)
- [Go From the General to the Particular: Multi-Domain Translation with Domain Transformation Networks](https://mlsanity.com/paper/1911.09912/) (2019)
- [Improving Domain Adaptation Translation with Domain Invariant and Specific Information](https://mlsanity.com/paper/1904.03879/) (2019)
- [A Multilingual Neural Machine Translation Model for Biomedical Data](https://mlsanity.com/paper/2008.02878/) (2020)

## Built on by (most cited first, 1 of 1)

- [How Transferable are Attribute Controllers on Pretrained Multilingual Translation Models?](https://mlsanity.com/paper/2309.08565/) (2023)

## Abstract (arXiv)

Previous works mostly focus on either multilingual or multi-domain aspects of neural machine translation (NMT). This paper investigates whether the domain information can be transferred across languages on the composition of multi-domain and multilingual NMT, particularly for the incomplete data condition where in-domain bitext is missing for some language pairs. Our results in the curated leave-one-domain-out experiments show that multi-domain multilingual (MDML) NMT can boost zero-shot translation performance up to +10 gains on BLEU, as well as aid the generalisation of multi-domain NMT to the missing domain. We also explore strategies for effective integration of multilingual and multi-domain NMT, including language and domain tag combination and auxiliary task training. We find that learning domain-aware representations and adding target-language tags to the encoder leads to effective MDML-NMT.