Low-Latency Neural Speech Translation
Table 1: Results for English to Spanish
['System', 'Valid (tst2011) BLEU', 'Test (tst2012) BLEU', 'TEDTest Partial BLEU', 'TEDTest Partial length (tokens)', 'SLT (tst2010) BLEU', 'SLT (tst2010) Word Up', 'SLT (tst2010) Mssg. Up.']
We report results in BLEU on the test and validation set with full sentence translation. Next, we report results on the test data with all possible prefixes, and finally, results on the ASR output. The baseline system is only trained on complete sentences. All other systems use the baseline system, and continue training using different strategies. The system ”Partial” is fine-tuned on all partial sentences. On the other hand, the BLEU score calculated on only partial sentences improves by ∼3 BLEU points. As shown by the number of tokens, the length of translations is reduced by 25%. So, a major problem of the baseline system is that it generates translations that are too long for the partial sentences. When testing on the ASR output in the last two columns, we see that the translation quality of the final hypothesis also drops, but the number of words which are updated is reduced by 45%. Also, the number of messages where at least one word changed is reduced by 20%. The system in the third line uses multi-task learning. In this case, the system is trained to perform both tasks: translation of partial and full sentences. Using this technique, we can combine the advantages of both models and maximize the translation quality of the final hypothesis, while minimizing the number of updates. The system has the same translation quality as the baseline system, with the same reduction in updates as the partial system.