Online Learning for Effort Reduction in Interactive Neural Machine Translation
Table 5: Translation results in terms of TER [%] and BLEU [%], of an adaptive NMT system (OL-NMT), compared to static NMT. ∇ and Δ represent absolute decrements and increments in terms of percentage for the corresponding metric of the online NMT system with respect the static one. Results that are statistically significantly better for each task and metric are boldfaced.
['[EMPTY]', '[EMPTY]', 'TER [%] NMT', 'TER [%] OL-NMT', 'TER [%] ∇', 'BLEU [%] NMT', 'BLEU [%] OL-NMT', 'BLEU [%] Δ']
In almost every case, the effort required, measured in terms of TER, is improved, yielding significant reductions in all tasks but EU. BLEU is consequently improved, following the TER trend. On the other hand, in the EU task we obtained almost no differences between static and adaptive systems. This lack of improvement was a common pattern in the UE along all the experimentation conducted in this work. OL was unable to be exploited to the full in this corpus.