# Machine Translation into Low-resource Language Varieties

arXiv: https://arxiv.org/abs/2106.06797 · HTML: https://mlsanity.com/paper/2106.06797/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 55 references that are in the map.
- First posted on arXiv 12 Jun 2021.

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [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)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [On the Variance of the Adaptive Learning Rate and Beyond](https://mlsanity.com/paper/1908.03265/) (2019)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [Word Translation Without Parallel Data](https://mlsanity.com/paper/1710.04087/) (2017)
- [Unsupervised Machine Translation Using Monolingual Corpora Only](https://mlsanity.com/paper/1711.00043/) (2017)
- [OpenNMT: Open-source Toolkit for Neural Machine Translation](https://mlsanity.com/paper/1709.03815/) (2017)
- [WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia](https://mlsanity.com/paper/1907.05791/) (2019)
- [Tangled up in BLEU: Reevaluating the Evaluation of Automatic Machine Translation Evaluation Metrics](https://mlsanity.com/paper/2006.06264/) (2020)
- [Unsupervised Neural Machine Translation](https://mlsanity.com/paper/1710.11041/) (2017)
- [CCAligned: A Massive Collection of Cross-Lingual Web-Document Pairs](https://mlsanity.com/paper/1911.06154/) (2019)
- [A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages](https://mlsanity.com/paper/2006.06202/) (2020)
- [A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality Indices](https://mlsanity.com/paper/1807.00787/) (2018)
- [Unsupervised Text Style Transfer using Language Models as Discriminators](https://mlsanity.com/paper/1805.11749/) (2018)
- [A Probabilistic Formulation of Unsupervised Text Style Transfer](https://mlsanity.com/paper/2002.03912/) (2020)
- [When Does Unsupervised Machine Translation Work?](https://mlsanity.com/paper/2004.05516/) (2020)
- [Domain Adaptation of Neural Machine Translation by Lexicon Induction](https://mlsanity.com/paper/1906.00376/) (2019)
- [Von Mises-Fisher Loss for Training Sequence to Sequence Models with Continuous Outputs](https://mlsanity.com/paper/1812.04616/) (2018)
- [A Multilingual View of Unsupervised Machine Translation](https://mlsanity.com/paper/2002.02955/) (2020)
- [Harnessing Multilinguality in Unsupervised Machine Translation for Rare Languages](https://mlsanity.com/paper/2009.11201/) (2020)
- [Adapting Word Embeddings to New Languages with Morphological and Phonological Subword Representations](https://mlsanity.com/paper/1808.09500/) (2018)
- [Reusing a Pretrained Language Model on Languages with Limited Corpora for Unsupervised NMT](https://mlsanity.com/paper/2009.07610/) (2020)
- [Neural Machine Translation into Language Varieties](https://mlsanity.com/paper/1811.01064/) (2018)

## Built on by (most cited first, 19 of 19)

- [Survey of Low-Resource Machine Translation](https://mlsanity.com/paper/2109.00486/) (2021)
- [The unreasonable effectiveness of few-shot learning for machine translation](https://mlsanity.com/paper/2302.01398/) (2023)
- [Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey](https://mlsanity.com/paper/2210.07700/) (2022)
- [GIVT: Generative Infinite-Vocabulary Transformers](https://mlsanity.com/paper/2312.02116/) (2023)
- [A Survey of Race, Racism, and Anti-Racism in NLP](https://mlsanity.com/paper/2106.11410/) (2021)
- [Natural Language Processing for Dialects of a Language: A Survey](https://mlsanity.com/paper/2401.05632/) (2024)
- [Efficient Test Time Adapter Ensembling for Low-resource Language Varieties](https://mlsanity.com/paper/2109.04877/) (2021)
- [FRMT: A Benchmark for Few-Shot Region-Aware Machine Translation](https://mlsanity.com/paper/2210.00193/) (2022)
- [CODET: A Benchmark for Contrastive Dialectal Evaluation of Machine Translation](https://mlsanity.com/paper/2305.17267/) (2023)
- [Towards Universality in Multilingual Text Rewriting](https://mlsanity.com/paper/2107.14749/) (2021)
- [Dim Wihl Gat Tun: The Case for Linguistic Expertise in NLP for Underdocumented Languages](https://mlsanity.com/paper/2203.09632/) (2022)
- [KpopMT: Translation Dataset with Terminology for Kpop Fandom](https://mlsanity.com/paper/2407.07413/) (2024)
- [High-Resource Methodological Bias in Low-Resource Investigations](https://mlsanity.com/paper/2211.07534/) (2022)
- [Dialectal and Low-Resource Machine Translation for Aromanian](https://mlsanity.com/paper/2410.17728/) (2024)
- [Birdie: Advancing State Space Models with Reward-Driven Objectives and Curricula](https://mlsanity.com/paper/2411.01030/) (2024)
- [Datasets for Multilingual Answer Sentence Selection](https://mlsanity.com/paper/2406.10172/) (2024)
- [Using Contextually Aligned Online Reviews to Measure LLMs' Performance Disparities Across Language Varieties](https://mlsanity.com/paper/2502.07058/) (2025)
- [A Komi-Yazva--Russian Parallel Corpus and Evaluation Protocol for Zero- and Few-Shot LLM Translation](https://mlsanity.com/paper/2606.06420/) (2026)
- [Machine Translation between English and Syriac (East Syriac Dialect) using Statistical Machine Learning](https://mlsanity.com/paper/2609.18529/) (2026)

## Abstract (arXiv)

State-of-the-art machine translation (MT) systems are typically trained to generate the "standard" target language; however, many languages have multiple varieties (regional varieties, dialects, sociolects, non-native varieties) that are different from the standard language. Such varieties are often low-resource, and hence do not benefit from contemporary NLP solutions, MT included. We propose a general framework to rapidly adapt MT systems to generate language varieties that are close to, but different from, the standard target language, using no parallel (source--variety) data. This also includes adaptation of MT systems to low-resource typologically-related target languages. We experiment with adapting an English--Russian MT system to generate Ukrainian and Belarusian, an English--Norwegian Bokmaal system to generate Nynorsk, and an English--Arabic system to generate four Arabic dialects, obtaining significant improvements over competitive baselines.