QCRI Machine Translation Systems for IWSLT 16
Table 6: Neural Network Joint Model + Different Adaptation Methods
['[BOLD] System', '[BOLD] ted-11', '[BOLD] ted-12', '[BOLD] ted-13', '[BOLD] ted-14', '[BOLD] Avg']
We also explored the award winning Neural Network Joint Model (NNJM) in our pipeline and tried to adapt it towards the in-domain data. it by running for 25 more epochs on the in-domain data. Because the data is huge, the entire training took 1.5 months of wall-clock time. The NNJM model gave significant improvement (+0.6) on top of baseline which does not include it. We found fine-tuning method to give slight gains (+0.2) when the baseline model was trained on the Opus data. On the contrary, fine-tuning did not help when the model trained was on UN.