# A Large-Scale Study of Machine Translation in the Turkic Languages

arXiv: https://arxiv.org/abs/2109.04593 · HTML: https://mlsanity.com/paper/2109.04593/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 81 references that are in the map.
- First posted on arXiv 9 Sep 2021.

## Builds on (most cited first, 20 of 20)

- [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)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [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)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages](https://mlsanity.com/paper/2010.02353/) (2020)
- [The Tatoeba Translation Challenge -- Realistic Data Sets for Low Resource and Multilingual MT](https://mlsanity.com/paper/2010.06354/) (2020)
- [When and Why are Pre-trained Word Embeddings Useful for Neural Machine Translation?](https://mlsanity.com/paper/1804.06323/) (2018)
- [The FLoRes Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-English](https://mlsanity.com/paper/1902.01382/) (2019)
- [Joey NMT: A Minimalist NMT Toolkit for Novices](https://mlsanity.com/paper/1907.12484/) (2019)
- [Pushing the Limits of Low-Resource Morphological Inflection](https://mlsanity.com/paper/1908.05838/) (2019)
- [From SPMRL to NMRL: What Did We Learn (and Unlearn) in a Decade of Parsing Morphologically-Rich Languages (MRLs)?](https://mlsanity.com/paper/2005.01330/) (2020)
- [It's Easier to Translate out of English than into it: Measuring Neural Translation Difficulty by Cross-Mutual Information](https://mlsanity.com/paper/2005.02354/) (2020)
- [Linguistically Motivated Vocabulary Reduction for Neural Machine Translation from Turkish to English](https://mlsanity.com/paper/1707.09879/) (2017)
- [Unsung Challenges of Building and Deploying Language Technologies for Low Resource Language Communities](https://mlsanity.com/paper/1912.03457/) (2019)
- [On Romanization for Model Transfer Between Scripts in Neural Machine Translation](https://mlsanity.com/paper/2009.14824/) (2020)
- [Application of Low-resource Machine Translation Techniques to Russian-Tatar Language Pair](https://mlsanity.com/paper/1910.00368/) (2019)

## Built on by (most cited first, 21 of 21)

- [Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model](https://mlsanity.com/paper/2402.07827/) (2024)
- [Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages](https://mlsanity.com/paper/2305.12182/) (2023)
- [Building Machine Translation Systems for the Next Thousand Languages](https://mlsanity.com/paper/2205.03983/) (2022)
- [An Open Dataset and Model for Language Identification](https://mlsanity.com/paper/2305.13820/) (2023)
- [DIALECTBENCH: A NLP Benchmark for Dialects, Varieties, and Closely-Related Languages](https://mlsanity.com/paper/2403.11009/) (2024)
- [GlotLID: Language Identification for Low-Resource Languages](https://mlsanity.com/paper/2310.16248/) (2023)
- [Goldfish: Monolingual Language Models for 350 Languages](https://mlsanity.com/paper/2408.10441/) (2024)
- [What a Creole Wants, What a Creole Needs](https://mlsanity.com/paper/2206.00437/) (2022)
- [Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval](https://mlsanity.com/paper/2204.02292/) (2022)
- [EMMA-500: Enhancing Massively Multilingual Adaptation of Large Language Models](https://mlsanity.com/paper/2409.17892/) (2024)
- [SMOL: Professionally translated parallel data for 115 under-represented languages](https://mlsanity.com/paper/2502.12301/) (2025)
- [Evaluating Multiway Multilingual NMT in the Turkic Languages](https://mlsanity.com/paper/2109.06262/) (2021)
- [Fixing MoE Over-Fitting on Low-Resource Languages in Multilingual Machine Translation](https://mlsanity.com/paper/2212.07571/) (2022)
- [Multilingual Arbitrage: Optimizing Data Pools to Accelerate Multilingual Progress](https://mlsanity.com/paper/2408.14960/) (2024)
- [Recent Advancements and Challenges of Turkic Central Asian Language Processing](https://mlsanity.com/paper/2407.05006/) (2024)
- [OpenLID-v3: Improving the Precision of Closely Related Language Identification -- An Experience Report](https://mlsanity.com/paper/2602.13139/) (2026)
- [KyrgyzNLP: Challenges, Progress, and Future](https://mlsanity.com/paper/2411.05503/) (2024)
- [Benchmarking Multilabel Topic Classification in the Kyrgyz Language](https://mlsanity.com/paper/2308.15952/) (2023)
- [TurkicNLP: An NLP Toolkit for Turkic Languages](https://mlsanity.com/paper/2602.19174/) (2026)
- [Vavanagi: a Community-run Platform for Documentation of the Hula Language in Papua New Guinea](https://mlsanity.com/paper/2603.14210/) (2026)
- [Cross-Lingual Transfer for Machine Translation in Turkic Languages](https://mlsanity.com/paper/2607.29355/) (2026)

## Abstract (arXiv)

Recent advances in neural machine translation (NMT) have pushed the quality of machine translation systems to the point where they are becoming widely adopted to build competitive systems. However, there is still a large number of languages that are yet to reap the benefits of NMT. In this paper, we provide the first large-scale case study of the practical application of MT in the Turkic language family in order to realize the gains of NMT for Turkic languages under high-resource to extremely low-resource scenarios. In addition to presenting an extensive analysis that identifies the bottlenecks towards building competitive systems to ameliorate data scarcity, our study has several key contributions, including, i) a large parallel corpus covering 22 Turkic languages consisting of common public datasets in combination with new datasets of approximately 2 million parallel sentences, ii) bilingual baselines for 26 language pairs, iii) novel high-quality test sets in three different translation domains and iv) human evaluation scores. All models, scripts, and data will be released to the public.