# Word Embedding Transformation for Robust Unsupervised Bilingual Lexicon Induction

arXiv: https://arxiv.org/abs/2105.12297 · HTML: https://mlsanity.com/paper/2105.12297/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 19 references that are in the map.
- First posted on arXiv 26 May 2021.

## Builds on (most cited first, 13 of 13)

- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Word Translation Without Parallel Data](https://mlsanity.com/paper/1710.04087/) (2017)
- [Unsupervised Neural Machine Translation](https://mlsanity.com/paper/1710.11041/) (2017)
- [A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings](https://mlsanity.com/paper/1805.06297/) (2018)
- [Phrase-Based & Neural Unsupervised Machine Translation](https://mlsanity.com/paper/1804.07755/) (2018)
- [Gromov-Wasserstein Alignment of Word Embedding Spaces](https://mlsanity.com/paper/1809.00013/) (2018)
- [On the Limitations of Unsupervised Bilingual Dictionary Induction](https://mlsanity.com/paper/1805.03620/) (2018)
- [Bilingual Lexicon Induction with Semi-supervision in Non-Isometric Embedding Spaces](https://mlsanity.com/paper/1908.06625/) (2019)
- [Do We Really Need Fully Unsupervised Cross-Lingual Embeddings?](https://mlsanity.com/paper/1909.01638/) (2019)
- [Non-Adversarial Unsupervised Word Translation](https://mlsanity.com/paper/1801.06126/) (2018)
- [Unsupervised Cross-lingual Transfer of Word Embedding Spaces](https://mlsanity.com/paper/1809.03633/) (2018)
- [Density Matching for Bilingual Word Embedding](https://mlsanity.com/paper/1904.02343/) (2019)
- [A Simple and Effective Approach to Robust Unsupervised Bilingual Dictionary Induction](https://mlsanity.com/paper/2011.14874/) (2020)

## Built on by (most cited first, 2 of 2)

- [A survey of neural-network-based methods utilising comparable data for finding translation equivalents](https://mlsanity.com/paper/2410.15144/) (2024)
- [Unsupervised Bilingual Lexicon Induction for Low Resource Languages](https://mlsanity.com/paper/2412.16894/) (2024)

## Abstract (arXiv)

Great progress has been made in unsupervised bilingual lexicon induction (UBLI) by aligning the source and target word embeddings independently trained on monolingual corpora. The common assumption of most UBLI models is that the embedding spaces of two languages are approximately isomorphic. Therefore the performance is bound by the degree of isomorphism, especially on etymologically and typologically distant languages. To address this problem, we propose a transformation-based method to increase the isomorphism. Embeddings of two languages are made to match with each other by rotating and scaling. The method does not require any form of supervision and can be applied to any language pair. On a benchmark data set of bilingual lexicon induction, our approach can achieve competitive or superior performance compared to state-of-the-art methods, with particularly strong results being found on distant languages.