Low-Latency Neural Speech Translation
Table 2: Results for English to French
['System', 'tst2010 Final', 'tst2010 Mix', 'SLT(tst2010) BLEU', 'SLT(tst2010) Word Up', 'SLT(tst2010) Mssg. Up.']
S5SS3SSS0Px4 English→French We again have a baseline system trained with cross-entropy, and a baseline system where training is continued with RL. The models were evaluated on the full sentences and the mixed set of complete and partial sentences, as well as on the ASR output. Similar to the other translation directions, we improved translation performance on partial sentences and reduced the number of rewritten tokens for the SLT output by using multi-task learning. Interestingly, using reinforcement learning also helped us improve the performance on partial sentences. The RL criteria evaluates the n-gram precision as well as the recall of the translation, which is punished when the generated output is too long. Both methods can be combined to achieve the overall best performance, which reduced the rewritten tokens by up to 50% without compromising translation performance.