# Weakly-Supervised Concept-based Adversarial Learning for Cross-lingual Word Embeddings

arXiv: https://arxiv.org/abs/1904.09446 · HTML: https://mlsanity.com/paper/1904.09446/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2019.

## Built on by (most cited first, 1 of 1)

- [Cross-Lingual Word Embedding Refinement by ℓ1 Norm Optimisation](https://mlsanity.com/paper/2104.04916/) (2021)

## Abstract (arXiv)

Distributed representations of words which map each word to a continuous vector have proven useful in capturing important linguistic information not only in a single language but also across different languages. Current unsupervised adversarial approaches show that it is possible to build a mapping matrix that align two sets of monolingual word embeddings together without high quality parallel data such as a dictionary or a sentence-aligned corpus. However, without post refinement, the performance of these methods' preliminary mapping is not good, leading to poor performance for typologically distant languages. In this paper, we propose a weakly-supervised adversarial training method to overcome this limitation, based on the intuition that mapping across languages is better done at the concept level than at the word level. We propose a concept-based adversarial training method which for most languages improves the performance of previous unsupervised adversarial methods, especially for typologically distant language pairs.