QCRI Machine Translation Systems for IWSLT 16
Table 10: Incremental Progress English-to-Arabic System
['[BOLD] System', '[BOLD] ted-11', '[BOLD] ted-12', '[BOLD] ted-13', '[BOLD] ted-14', '[BOLD] Avg']
We did not do detailed experiments for the English→Arabic direction because of computational limitations, but simply replicated what worked for the Arabic→English direction. The baseline system (ID) was trained on the the TED data and target side of all the permissible parallel data. In the second row, we added all the parallel data except for the UN. In the third row we additionally added the UN data that we selected in the Arabic→English direction. Additional parallel data gives an average improvement of +1.4 BLEU point. Then we added an NNJM model trained on in-domain TED data on top of this system to improve it by +0.8. Adding GigaWord and monolingual OPUS data (another 20M Sentences other than the target-side of the parallel data) gave an improvement of +0.3. Finally we replaced the baseline NNJM with the one trained on OPUS data and fine-tuned with the in-domain data to get our best system.