# Application of Low-resource Machine Translation Techniques to Russian-Tatar Language Pair

arXiv: https://arxiv.org/abs/1910.00368 · HTML: https://mlsanity.com/paper/1910.00368/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2019.

## Built on by (most cited first, 7 of 7)

- [A Large-Scale Study of Machine Translation in the Turkic Languages](https://mlsanity.com/paper/2109.04593/) (2021)
- [Evaluating Multiway Multilingual NMT in the Turkic Languages](https://mlsanity.com/paper/2109.06262/) (2021)
- [Recent Advancements and Challenges of Turkic Central Asian Language Processing](https://mlsanity.com/paper/2407.05006/) (2024)
- [Domain Generalization using Ensemble Learning](https://mlsanity.com/paper/2103.10257/) (2021)
- [Deep Learning Models in Software Requirements Engineering](https://mlsanity.com/paper/2105.07771/) (2021)
- [Class-incremental Learning using a Sequence of Partial Implicitly Regularized Classifiers](https://mlsanity.com/paper/2104.01577/) (2021)
- [Analyzing the effectiveness of image augmentations for face recognition from limited data](https://mlsanity.com/paper/2105.08796/) (2021)

## Abstract (arXiv)

Neural machine translation is the current state-of-the-art in machine translation. Although it is successful in a resource-rich setting, its applicability for low-resource language pairs is still debatable. In this paper, we explore the effect of different techniques to improve machine translation quality when a parallel corpus is as small as 324 000 sentences, taking as an example previously unexplored Russian-Tatar language pair. We apply such techniques as transfer learning and semi-supervised learning to the base Transformer model, and empirically show that the resulting models improve Russian to Tatar and Tatar to Russian translation quality by +2.57 and +3.66 BLEU, respectively.