A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings
Table 1: Results of unsupervised methods on the dataset of Zhang et al. (2017a). We perform 10 runs for each method and report the best and average accuracies (%), the number of successful runs (those with >5% accuracy) and the average runtime (minutes).
['[EMPTY]', '[BOLD] ES-EN  [BOLD] best', '[BOLD] ES-EN  [BOLD] avg', '[BOLD] ES-EN  [BOLD] s', '[BOLD] ES-EN  [BOLD] t', '[BOLD] IT-EN  [BOLD] best', '[BOLD] IT-EN  [BOLD] avg', '[BOLD] IT-EN  [BOLD] s', '[BOLD] IT-EN  [BOLD] t', '[BOLD] TR-EN  [BOLD] best', '[BOLD] TR-EN  [BOLD] avg', '[BOLD] TR-EN  [BOLD] s', '[BOLD] TR-EN  [BOLD] t']
We report the results in the dataset of Zhang et al. As it can be seen, the proposed method performs at par with that of Conneau et al. both in Spanish-English and Italian-English, but gets substantially better results in the more challenging Turkish-English pair. While we are able to reproduce the results reported by Zhang et al. Another disadvantage of that model is that different language pairs require different hyperparameters: λ=1 works substantially better for Spanish-English and Italian-English, but only λ=10 works for Turkish-English.