A Robust Self-Learning Method for Fully Unsupervised Cross-Lingual Mappings of Word Embeddings: Making the Method Robustly Reproducible as Well
Published in Proceedings of the 12th Language Resources and Evaluation Conference, 2020
Recommended citation: Garneau, N., Godbout, M., Beauchemin, D., Durand, A., & Lamontagne, L. (2020). A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings: Making the method robustly reproducible as well. Proceedings of the 12th Language Resources and Evaluation Conference. https://www.aclweb.org/anthology/2020.lrec-1.681/
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 that apply to any research project in order to deliver fully reproducible research.