# A Multilingual View of Unsupervised Machine Translation

arXiv: https://arxiv.org/abs/2002.02955 · HTML: https://mlsanity.com/paper/2002.02955/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2020.

## Built on by (most cited first, 23 of 23)

- [Pretrained Language Models for Text Generation: A Survey](https://mlsanity.com/paper/2201.05273/) (2022)
- [Neural Machine Translation for Low-Resource Languages: A Survey](https://mlsanity.com/paper/2106.15115/) (2021)
- [The unreasonable effectiveness of few-shot learning for machine translation](https://mlsanity.com/paper/2302.01398/) (2023)
- [Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts](https://mlsanity.com/paper/2306.11372/) (2023)
- [Towards Continual Learning for Multilingual Machine Translation via Vocabulary Substitution](https://mlsanity.com/paper/2103.06799/) (2021)
- [Using natural language prompts for machine translation](https://mlsanity.com/paper/2202.11822/) (2022)
- [Neural Data-to-Text Generation with LM-based Text Augmentation](https://mlsanity.com/paper/2102.03556/) (2021)
- [Machine Translation into Low-resource Language Varieties](https://mlsanity.com/paper/2106.06797/) (2021)
- [Towards the Next 1000 Languages in Multilingual Machine Translation: Exploring the Synergy Between Supervised and Self-Supervised Learning](https://mlsanity.com/paper/2201.03110/) (2022)
- [Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data](https://mlsanity.com/paper/2105.15071/) (2021)
- [UM4: Unified Multilingual Multiple Teacher-Student Model for Zero-Resource Neural Machine Translation](https://mlsanity.com/paper/2207.04900/) (2022)
- [Towards Making the Most of Multilingual Pretraining for Zero-Shot Neural Machine Translation](https://mlsanity.com/paper/2110.08547/) (2021)
- [Towards Universality in Multilingual Text Rewriting](https://mlsanity.com/paper/2107.14749/) (2021)
- [Neural Machine Translation for the Indigenous Languages of the Americas: An Introduction](https://mlsanity.com/paper/2306.06804/) (2023)
- [Unsupervised Machine Translation On Dravidian Languages](https://mlsanity.com/paper/2103.15877/) (2021)
- [Multilingual Bidirectional Unsupervised Translation Through Multilingual Finetuning and Back-Translation](https://mlsanity.com/paper/2209.02821/) (2022)
- [Refining Low-Resource Unsupervised Translation by Language Disentanglement of Multilingual Model](https://mlsanity.com/paper/2205.15544/) (2022)
- [From Native Memes to Global Moderation: Cross-Cultural Evaluation of Vision-Language Models for Hateful Meme Detection](https://mlsanity.com/paper/2602.07497/) (2026)
- [Paraphrase Generation as Unsupervised Machine Translation](https://mlsanity.com/paper/2109.02950/) (2021)
- [Boosting Unsupervised Machine Translation with Pseudo-Parallel Data](https://mlsanity.com/paper/2310.14262/) (2023)
- [Relevance-guided Neural Machine Translation](https://mlsanity.com/paper/2312.00214/) (2023)
- [An Empirical study of Unsupervised Neural Machine Translation: analyzing NMT output, model's behavior and sentences' contribution](https://mlsanity.com/paper/2312.12588/) (2023)
- [Ensemble Self-Training for Unsupervised Machine Translation](https://mlsanity.com/paper/2603.17087/) (2026)

## Abstract (arXiv)

We present a probabilistic framework for multilingual neural machine translation that encompasses supervised and unsupervised setups, focusing on unsupervised translation. In addition to studying the vanilla case where there is only monolingual data available, we propose a novel setup where one language in the (source, target) pair is not associated with any parallel data, but there may exist auxiliary parallel data that contains the other. This auxiliary data can naturally be utilized in our probabilistic framework via a novel cross-translation loss term. Empirically, we show that our approach results in higher BLEU scores over state-of-the-art unsupervised models on the WMT'14 English-French, WMT'16 English-German, and WMT'16 English-Romanian datasets in most directions. In particular, we obtain a +1.65 BLEU advantage over the best-performing unsupervised model in the Romanian-English direction.