# Cross-Lingual Word Embeddings for Turkic Languages

arXiv: https://arxiv.org/abs/2005.08340 · HTML: https://mlsanity.com/paper/2005.08340/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2020.

## Built on by (most cited first, 12 of 12)

- [A machine transliteration tool between Uzbek alphabets](https://mlsanity.com/paper/2205.09578/) (2022)
- [Resources for Turkish Natural Language Processing: A critical survey](https://mlsanity.com/paper/2204.05042/) (2022)
- [UzBERT: pretraining a BERT model for Uzbek](https://mlsanity.com/paper/2108.09814/) (2021)
- [Development of a rule-based lemmatization algorithm through Finite State Machine for Uzbek language](https://mlsanity.com/paper/2210.16006/) (2022)
- [Uzbek Sentiment Analysis based on local Restaurant Reviews](https://mlsanity.com/paper/2205.15930/) (2022)
- [Accuracy of the Uzbek stop words detection: a case study on "School corpus"](https://mlsanity.com/paper/2209.07053/) (2022)
- [Text classification dataset and analysis for Uzbek language](https://mlsanity.com/paper/2302.14494/) (2023)
- [When your Cousin has the Right Connections: Unsupervised Bilingual Lexicon Induction for Related Data-Imbalanced Languages](https://mlsanity.com/paper/2305.14012/) (2023)
- [Linear Transformations for Cross-lingual Sentiment Analysis](https://mlsanity.com/paper/2209.07244/) (2022)
- [Creating a morphological and syntactic tagged corpus for the Uzbek language](https://mlsanity.com/paper/2210.15234/) (2022)
- [MultimodalHugs: Enabling Sign Language Processing in Hugging Face](https://mlsanity.com/paper/2509.09729/) (2025)
- [TurkicNLP: An NLP Toolkit for Turkic Languages](https://mlsanity.com/paper/2602.19174/) (2026)

## Abstract (arXiv)

There has been an increasing interest in learning cross-lingual word embeddings to transfer knowledge obtained from a resource-rich language, such as English, to lower-resource languages for which annotated data is scarce, such as Turkish, Russian, and many others. In this paper, we present the first viability study of established techniques to align monolingual embedding spaces for Turkish, Uzbek, Azeri, Kazakh and Kyrgyz, members of the Turkic family which is heavily affected by the low-resource constraint. Those techniques are known to require little explicit supervision, mainly in the form of bilingual dictionaries, hence being easily adaptable to different domains, including low-resource ones. We obtain new bilingual dictionaries and new word embeddings for these languages and show the steps for obtaining cross-lingual word embeddings using state-of-the-art techniques. Then, we evaluate the results using the bilingual dictionary induction task. Our experiments confirm that the obtained bilingual dictionaries outperform previously-available ones, and that word embeddings from a low-resource language can benefit from resource-rich closely-related languages when they are aligned together. Furthermore, evaluation on an extrinsic task (Sentiment analysis on Uzbek) proves that monolingual word embeddings can, although slightly, benefit from cross-lingual alignments.