# A Robust Self-Learning Method for Fully Unsupervised Cross-Lingual Mappings of Word Embeddings: Making the Method Robustly Reproducible as Well

arXiv: https://arxiv.org/abs/1912.01706 · HTML: https://mlsanity.com/paper/1912.01706/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2019.

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

- [A Systematic Review of Reproducibility Research in Natural Language Processing](https://mlsanity.com/paper/2103.07929/) (2021)
- [On Cross-Lingual Retrieval with Multilingual Text Encoders](https://mlsanity.com/paper/2112.11031/) (2021)
- [MIPE: A Metric Independent Pipeline for Effective Code-Mixed NLG Evaluation](https://mlsanity.com/paper/2107.11534/) (2021)
- [Zero-Resource Multi-Dialectal Arabic Natural Language Understanding](https://mlsanity.com/paper/2104.06591/) (2021)

## Abstract (arXiv)

In this paper, we reproduce the experiments of Artetxe et al. (2018b) regarding the robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings. We show that the reproduction of their method is indeed feasible with some minor assumptions. We further investigate the robustness of their model by introducing four new languages that are less similar to English than the ones proposed by the original paper. In order to assess the stability of their model, we also conduct a grid search over sensible hyperparameters. We then propose key recommendations applicable to any research project in order to deliver fully reproducible research.