# Bilingual Lexicon Induction through Unsupervised Machine Translation

arXiv: https://arxiv.org/abs/1907.10761 · HTML: https://mlsanity.com/paper/1907.10761/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2019.

## Built on by (most cited first, 16 of 16)

- [Bilingual Lexicon Induction via Unsupervised Bitext Construction and Word Alignment](https://mlsanity.com/paper/2101.00148/) (2021)
- [Exploiting Language Relatedness for Low Web-Resource Language Model Adaptation: An Indic Languages Study](https://mlsanity.com/paper/2106.03958/) (2021)
- [Tokenization Matters: Navigating Data-Scarce Tokenization for Gender Inclusive Language Technologies](https://mlsanity.com/paper/2312.11779/) (2023)
- [Combining Static Word Embeddings and Contextual Representations for Bilingual Lexicon Induction](https://mlsanity.com/paper/2106.03084/) (2021)
- [Bilingual Lexicon Induction for Low-Resource Languages using Graph Matching via Optimal Transport](https://mlsanity.com/paper/2210.14378/) (2022)
- [Majority Voting with Bidirectional Pre-translation For Bitext Retrieval](https://mlsanity.com/paper/2103.06369/) (2021)
- [When your Cousin has the Right Connections: Unsupervised Bilingual Lexicon Induction for Related Data-Imbalanced Languages](https://mlsanity.com/paper/2305.14012/) (2023)
- [Can Synthetic Translations Improve Bitext Quality?](https://mlsanity.com/paper/2203.07643/) (2022)
- [Multilingual Word Embeddings for Low-Resource Languages using Anchors and a Chain of Related Languages](https://mlsanity.com/paper/2311.12489/) (2023)
- [TokAlign++: Advancing Vocabulary Adaptation via Better Token Alignment](https://mlsanity.com/paper/2605.13429/) (2026)
- [A Aelf-supervised Tibetan-chinese Vocabulary Alignment Method Based On Adversarial Learning](https://mlsanity.com/paper/2110.01258/) (2021)
- [From meaning to perception -- exploring the space between word and odor perception embeddings](https://mlsanity.com/paper/2203.10294/) (2022)
- [Massively Multilingual Lexical Specialization of Multilingual Transformers](https://mlsanity.com/paper/2208.01018/) (2022)
- [A survey of neural-network-based methods utilising comparable data for finding translation equivalents](https://mlsanity.com/paper/2410.15144/) (2024)
- [Cross-Domain Bilingual Lexicon Induction via Pretrained Language Models](https://mlsanity.com/paper/2505.23146/) (2025)
- [Edeflip: Supervised Word Translation between English and Yoruba](https://mlsanity.com/paper/2506.13020/) (2025)

## Abstract (arXiv)

A recent research line has obtained strong results on bilingual lexicon induction by aligning independently trained word embeddings in two languages and using the resulting cross-lingual embeddings to induce word translation pairs through nearest neighbor or related retrieval methods. In this paper, we propose an alternative approach to this problem that builds on the recent work on unsupervised machine translation. This way, instead of directly inducing a bilingual lexicon from cross-lingual embeddings, we use them to build a phrase-table, combine it with a language model, and use the resulting machine translation system to generate a synthetic parallel corpus, from which we extract the bilingual lexicon using statistical word alignment techniques. As such, our method can work with any word embedding and cross-lingual mapping technique, and it does not require any additional resource besides the monolingual corpus used to train the embeddings. When evaluated on the exact same cross-lingual embeddings, our proposed method obtains an average improvement of 6 accuracy points over nearest neighbor and 4 points over CSLS retrieval, establishing a new state-of-the-art in the standard MUSE dataset.