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Simultaneous translation is vastly different from full-sentence translation, in the sense that it starts translation before the source sentence ends, with only a few words delay. However, due to the lack of large scale and publicly available simultaneous translation datasets, most simultaneous translation systems still train with ordinary full-sentence parallel corpora which are not suitable for the simultaneous scenario due to the existence of unnecessary long-distance reorderings. Instead of expensive, time-consuming annotation, we propose a novel method that rewrites the target side of existing full-sentence corpus into simultaneous-style translation. Experiments on Chinese-to-English translation demonstrate about +2.7 BLEU improvements with the addition of newly generated pseudo references.
Simultaneous machine translation (SiMT) aims to translate a continuous input text stream into another language with the lowest latency and highest quality possible. The translation thus has to start with an incomplete source text, which is read progr
There has been great progress in improving streaming machine translation, a simultaneous paradigm where the system appends to a growing hypothesis as more source content becomes available. We study a related problem in which revisions to the hypothes
Simultaneous translation has many important application scenarios and attracts much attention from both academia and industry recently. Most existing frameworks, however, have difficulties in balancing between the translation quality and latency, i.e
Transformer-based models have achieved state-of-the-art performance on speech translation tasks. However, the model architecture is not efficient enough for streaming scenarios since self-attention is computed over an entire input sequence and the co
An important concern in training multilingual neural machine translation (NMT) is to translate between language pairs unseen during training, i.e zero-shot translation. Improving this ability kills two birds with one stone by providing an alternative