# Low-resource Machine Translation for Code-switched Kazakh-Russian Language Pair

arXiv: https://arxiv.org/abs/2503.20007 · HTML: https://mlsanity.com/paper/2503.20007/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 53 references that are in the map.
- First posted on arXiv 25 Mar 2025.

## Builds on (most cited first, 27 of 27)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020)
- [Beyond English-Centric Multilingual Machine Translation](https://mlsanity.com/paper/2010.11125/) (2020)
- [PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models](https://mlsanity.com/paper/2404.02948/) (2024)
- [SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings](https://mlsanity.com/paper/2004.08728/) (2020)
- [GLUECoS : An Evaluation Benchmark for Code-Switched NLP](https://mlsanity.com/paper/2004.12376/) (2020)
- [LinCE: A Centralized Benchmark for Linguistic Code-switching Evaluation](https://mlsanity.com/paper/2005.04322/) (2020)
- [Training Neural Machine Translation To Apply Terminology Constraints](https://mlsanity.com/paper/1906.01105/) (2019)
- [SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets](https://mlsanity.com/paper/2008.04277/) (2020)
- [Are Multilingual Models Effective in Code-Switching?](https://mlsanity.com/paper/2103.13309/) (2021)
- [Code-Switched Language Models Using Neural Based Synthetic Data from Parallel Sentences](https://mlsanity.com/paper/1909.08582/) (2019)
- [Code-Switching for Enhancing NMT with Pre-Specified Translation](https://mlsanity.com/paper/1904.09107/) (2019)
- [Code-switching pre-training for neural machine translation](https://mlsanity.com/paper/2009.08088/) (2020)
- [Overview for the Second Shared Task on Language Identification in Code-Switched Data](https://mlsanity.com/paper/1909.13016/) (2019)
- [Many-to-English Machine Translation Tools, Data, and Pretrained Models](https://mlsanity.com/paper/2104.00290/) (2021)
- [End-to-End Speech Translation for Code Switched Speech](https://mlsanity.com/paper/2204.05076/) (2022)
- [Named Entity Recognition on Code-Switched Data: Overview of the CALCS 2018 Shared Task](https://mlsanity.com/paper/1906.04138/) (2019)
- [Can You Traducir This? Machine Translation for Code-Switched Input](https://mlsanity.com/paper/2105.04846/) (2021)
- [From English to Code-Switching: Transfer Learning with Strong Morphological Clues](https://mlsanity.com/paper/1909.05158/) (2019)
- [Code-switched Language Models Using Dual RNNs and Same-Source Pretraining](https://mlsanity.com/paper/1809.01962/) (2018)
- [Language Modeling for Code-Switching: Evaluation, Integration of Monolingual Data, and Discriminative Training](https://mlsanity.com/paper/1810.11895/) (2018)
- [The Effect of Alignment Objectives on Code-Switching Translation](https://mlsanity.com/paper/2309.05044/) (2023)

## Built on by (most cited first, 3 of 3)

- [Beyond Monolingual Assumptions: A Survey of Code-Switched NLP in the Era of Large Language Models across Modalities](https://mlsanity.com/paper/2510.07037/) (2025)
- [VietMix: A Naturally-Occurring Parallel Corpus and Augmentation Framework for Vietnamese-English Code-Mixed Machine Translation](https://mlsanity.com/paper/2505.24472/) (2025)
- [Towards Fine-Grained Code-Switch Speech Translation with Semantic Space Alignment](https://mlsanity.com/paper/2511.10670/) (2025)

## Abstract (arXiv)

Machine translation for low resource language pairs is a challenging task. This task could become extremely difficult once a speaker uses code switching. We propose a method to build a machine translation model for code-switched Kazakh-Russian language pair with no labeled data. Our method is basing on generation of synthetic data. Additionally, we present the first codeswitching Kazakh-Russian parallel corpus and the evaluation results, which include a model achieving 16.48 BLEU almost reaching an existing commercial system and beating it by human evaluation.