# Word and Phrase Translation with word2vec

arXiv: https://arxiv.org/abs/1705.03127 · HTML: https://mlsanity.com/paper/1705.03127/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2017.

## Built on by (most cited first, 4 of 4)

- [fairDMS: Rapid Model Training by Data and Model Reuse](https://mlsanity.com/paper/2204.09805/) (2022)
- [Deep Artificial Intelligence for Fantasy Football Language Understanding](https://mlsanity.com/paper/2111.02874/) (2021)
- [NLP Meets RNA: Unsupervised Embedding Learning for Ribozymes with Word2Vec](https://mlsanity.com/paper/2307.05537/) (2023)
- [Edeflip: Supervised Word Translation between English and Yoruba](https://mlsanity.com/paper/2506.13020/) (2025)

## Abstract (arXiv)

Word and phrase tables are key inputs to machine translations, but costly to produce. New unsupervised learning methods represent words and phrases in a high-dimensional vector space, and these monolingual embeddings have been shown to encode syntactic and semantic relationships between language elements. The information captured by these embeddings can be exploited for bilingual translation by learning a transformation matrix that allows matching relative positions across two monolingual vector spaces. This method aims to identify high-quality candidates for word and phrase translation more cost-effectively from unlabeled data. This paper expands the scope of previous attempts of bilingual translation to four languages (English, German, Spanish, and French). It shows how to process the source data, train a neural network to learn the high-dimensional embeddings for individual languages and expands the framework for testing their quality beyond the English language. Furthermore, it shows how to learn bilingual transformation matrices and obtain candidates for word and phrase translation, and assess their quality.