  Recently, simultaneous translation has gathered a lot of attention since it
enables compelling applications such as subtitle translation for a live event
or real-time video-call translation. Some of these translation applications
allow editing of partial translation giving rise to re-translation approaches.
The current re-translation approaches are based on autoregressive sequence
generation models (ReTA), which generate tar-get tokens in the (partial)
translation sequentially. The multiple re-translations with sequential
generation inReTAmodelslead to an increased inference time gap between the
incoming source input and the corresponding target output as the source input
grows. Besides, due to the large number of inference operations involved, the
ReTA models are not favorable for resource-constrained devices. In this work,
we propose a faster re-translation system based on a non-autoregressive
sequence generation model (FReTNA) to overcome the aforementioned limitations.
We evaluate the proposed model on multiple translation tasks and our model
reduces the inference times by several orders and achieves a competitive
BLEUscore compared to the ReTA and streaming (Wait-k) models.The proposed model
reduces the average computation time by a factor of 20 when compared to the
ReTA model by incurring a small drop in the translation quality. It also
outperforms the streaming-based Wait-k model both in terms of computation time
(1.5 times lower) and translation quality.
