Translations as Additional Contexts for Sentence Classification
Table 3: Accuracies of the worst CNN+translation classifiers when N=1. Accuracies less than CNN accuracies are highlighted in red.
['[BOLD] Model', '[BOLD] MR', '[BOLD] SUBJ', '[BOLD] CR', '[BOLD] TREC']
We compare our model (CNN+MCFA) and the baselines discussed above (CNN+B1, CNN+B2). On all settings, our model outperforms the baselines. When N=10, the performance of our model increases over the performance when N=1, however the performance of CNN+B1 decreases when compared to the performance when N=1. On all data sets except SUBJ, the accuracy of CNN+B1 decreases from the base CNN accuracy, while the accuracy of our model always improves from the base CNN accuracy. This is resolved by CNN+B2 by applying L2 regularization, however the increase in performance is marginal.