# Non-Adversarial Unsupervised Word Translation

arXiv: https://arxiv.org/abs/1801.06126 · HTML: https://mlsanity.com/paper/1801.06126/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jan 2018.

## Built on by (most cited first, 19 of 19)

- [Black Box Few-Shot Adaptation for Vision-Language models](https://mlsanity.com/paper/2304.01752/) (2023)
- [Do Vision and Language Models Share Concepts? A Vector Space Alignment Study](https://mlsanity.com/paper/2302.06555/) (2023)
- [Bilingual Lexicon Induction via Unsupervised Bitext Construction and Word Alignment](https://mlsanity.com/paper/2101.00148/) (2021)
- [Improving Word Translation via Two-Stage Contrastive Learning](https://mlsanity.com/paper/2203.08307/) (2022)
- [Transferring Knowledge Distillation for Multilingual Social Event Detection](https://mlsanity.com/paper/2108.03084/) (2021)
- [Cross-Lingual Word Embedding Refinement by ℓ1 Norm Optimisation](https://mlsanity.com/paper/2104.04916/) (2021)
- [mini-vec2vec: Scaling Universal Geometry Alignment with Linear Transformations](https://mlsanity.com/paper/2510.02348/) (2025)
- [Aligning Embeddings and Geometric Random Graphs: Informational Results and Computational Approaches for the Procrustes-Wasserstein Problem](https://mlsanity.com/paper/2405.14532/) (2024)
- [Word Embedding Transformation for Robust Unsupervised Bilingual Lexicon Induction](https://mlsanity.com/paper/2105.12297/) (2021)
- [Towards Unsupervised Speech Recognition at the Syllable-Level](https://mlsanity.com/paper/2510.03639/) (2025)
- [Don't Forget Cheap Training Signals Before Building Unsupervised Bilingual Word Embeddings](https://mlsanity.com/paper/2205.15713/) (2022)
- [Robust Unsupervised Cross-Lingual Word Embedding using Domain Flow Interpolation](https://mlsanity.com/paper/2210.03319/) (2022)
- [Domain Mismatch Doesn't Always Prevent Cross-Lingual Transfer Learning](https://mlsanity.com/paper/2211.16671/) (2022)
- [TokAlign++: Advancing Vocabulary Adaptation via Better Token Alignment](https://mlsanity.com/paper/2605.13429/) (2026)
- [Cross-language Information Retrieval](https://mlsanity.com/paper/2111.05988/) (2021)
- [Utilizing Language-Image Pretraining for Efficient and Robust Bilingual Word Alignment](https://mlsanity.com/paper/2205.11616/) (2022)
- [Symmetrized Robust Procrustes: Constant-Factor Approximation and Exact Recovery](https://mlsanity.com/paper/2207.08592/) (2022)
- [Accessing Higher Dimensions for Unsupervised Word Translation](https://mlsanity.com/paper/2305.14200/) (2023)
- [Unsupervised Bilingual Lexicon Induction for Low Resource Languages](https://mlsanity.com/paper/2412.16894/) (2024)

## Abstract (arXiv)

Unsupervised word translation from non-parallel inter-lingual corpora has attracted much research interest. Very recently, neural network methods trained with adversarial loss functions achieved high accuracy on this task. Despite the impressive success of the recent techniques, they suffer from the typical drawbacks of generative adversarial models: sensitivity to hyper-parameters, long training time and lack of interpretability. In this paper, we make the observation that two sufficiently similar distributions can be aligned correctly with iterative matching methods. We present a novel method that first aligns the second moment of the word distributions of the two languages and then iteratively refines the alignment. Extensive experiments on word translation of European and Non-European languages show that our method achieves better performance than recent state-of-the-art deep adversarial approaches and is competitive with the supervised baseline. It is also efficient, easy to parallelize on CPU and interpretable.