Driven by the goal of eradicating language barriers on a global scale, machine translation
has solidified itself as a key focus of artificial intelligence research today. However, such …

Non-autoregressive (NAR) generation, which is first proposed in neural machine translation
(NMT) to speed up inference, has attracted much attention in both machine learning and …

Driven by the goal of eradicating language barriers on a global scale, machine translation
has solidified itself as a key focus of artificial intelligence research today. However, such …

Non-autoregressive neural machine translation (NAMT) has received increasing attention
recently in virtue of its promising acceleration paradigm for fast decoding. However, these …

This paper addresses the task of contextual translation using multi-segment models.
Specifically we show that increasing model capacity further pushes the limits of this …

Lexical disambiguation is a major challenge for machine translation systems, especially if
some senses of a word are trained less often than others. Identifying patterns of …

Non-autoregressive sequence generation (NAR) attempts to generate the entire or partial
output sequences in parallel to speed up the generation process and avoid potential issues …

Non-autoregressive approaches aim to improve the inference speed of translation models
by only requiring a single forward pass to generate the output sequence instead of iteratively …

Striking an optimal balance between minimal drafting latency and high speculation accuracy
to enhance the inference speed of Large Language Models remains a significant challenge …

Non-autoregressive machine translation (NAT) has recently made great progress. However,
most works to date have focused on standard translation tasks, even though some edit …