A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings
Table 4: Ablation test on the dataset of Dinu et al. (2015) and the extensions of Artetxe et al. (2017, 2018a). 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] EN-IT  [BOLD] best', '[BOLD] EN-IT  [BOLD] avg', '[BOLD] EN-IT  [BOLD] s', '[BOLD] EN-IT  [BOLD] t', '[BOLD] EN-DE  [BOLD] best', '[BOLD] EN-DE  [BOLD] avg', '[BOLD] EN-DE  [BOLD] s', '[BOLD] EN-DE  [BOLD] t', '[BOLD] EN-FI  [BOLD] best', '[BOLD] EN-FI  [BOLD] avg', '[BOLD] EN-FI  [BOLD] s', '[BOLD] EN-FI  [BOLD] t', '[BOLD] EN-ES  [BOLD] best', '[BOLD] EN-ES  [BOLD] avg', '[BOLD] EN-ES  [BOLD] s', '[BOLD] EN-ES  [BOLD] t']
In order to better understand the role of different aspects in the proposed system, we perform an ablation test, where we separately analyze the effect of initialization, the different components of our robust self-learning algorithm, and the final symmetric re-weighting.