Aligning Vector-spaces with Noisy Supervised Lexicons
Table 1: Bilingual Experiment P@1. Numbers are based on 10 runs of each method. The En→De, En→Fi and En→Es improvements are significant at p<0.05 according to ANOVA on the different runs.
['Method', 'En→It best', 'En→It avg', 'En→It iters', 'En→De best', 'En→De avg', 'En→De iters', 'En→Fi best', 'En→Fi avg', 'En→Fi iters', 'En→Es best', 'En→Es avg', 'En→Es iters']
In Table 1 we report the best and average precision@1 scores and the average number of iterations among 10 experiments, for different language translations. Our model improves the results in the translation tasks. In most setups our average case is better than the former best case. In addition, the noise-aware model is more stable and therefore requires fewer iterations to converge. The accuracy improvements are small but consistent, and we note that we consider them as a lower-bound on the actual improvements as the current test set comes from the same distribution of the training set, and also contains similarly noisy pairs.