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.

本文重现了 Artetxe 等人（2018b）的实验，探讨了全无监督交叉语言词向量映射的鲁棒自学习方法，并通过引入 4 种与原始文献所提出的语言不太相似的语言，进一步研究了其模型的可靠性。通过对合理超参数的网格搜索，评估了其模型的稳定性，并提出了对实现完全可重复性研究的重要建议。