Machine Translation for Machines:the Sentiment Classification Use Case
Table 2: Classification results (F1) obtained with: i) automatic English translations by three models (Generic, Reinforce, MO-Reinforce), and ii) gold-standard English (English) and untranslated German/Italian (Original) tweets.
['[EMPTY]', '[BOLD] De - En  [ITALIC] 5%', '[BOLD] De - En  [ITALIC] 100%', '[BOLD] It - En  [ITALIC] 5%', '[BOLD] It - En  [ITALIC] 100%']
When NMT is trained on 100% of the parallel data, for both languages Reinforce produces translations that lead to classification improvements over those produced by the Generic model ( +0.5 De-En, +0.8 It-En). Although the scores are considerably better than those obtained by the Original classifiers (+9.3 De-En, +7.2 It-En), the gap with respect to the English classifier is still quite large (-1.4 De-En and -2.3 It-En).