Learning Coupled Policies for Simultaneous Machine Translation
Table 1: The effect of scheduled sampling (SS) in training coupled interpreter/programmer policies with imitation learning. The results are further put in the context of the models trained using the wait-k method. Here we set μy=20 and μa=6 for all language pairs, except CS where we set μy=30.
['[EMPTY]', 'DE→EN BLEU', 'DE→EN AL', 'DE→EN AP', 'CS→EN BLEU', 'CS→EN AL', 'CS→EN AP', 'AR→EN BLEU', 'AR→EN AL', 'AR→EN AP', 'BG → EN BLEU', 'BG → EN AL', 'BG → EN AP', 'RO → EN BLEU', 'RO → EN AL', 'RO → EN AP']
We are now interested into the effect of scheduled sampling (SS) in learning coupled policies in NPI-SiMT. For this purpose, we train four versions of our models where apply SS to both the programmer and the interpreter, only programmer, only interpreter, or none of them. the results when translating from German/Czech/Arabic/Bulgarian/Romanian into English. We further put the results in the context of the policies trained using the wait-k method where k∈{1,2,3,4,5,7,∞}. The NPI-SiMT system that is trained using our proposed oracle (no-SS) was able to learn from the low delay oracle as their natural delay (AL) and (AP) is generally as low as the delay of the oracle during training. The schedule sampling on programmer (ss-agent) during training ameliorate this as it increased up to 10 points of BLEU score in case of Romanian, 8 points in case of Bulgarian and, 5 points in case of German. Additionally applying scheduled sampling on the interpreter (ss-both) further increase the accuracy while also decrease a delay by a little in DE, CS, and RO language pairs.