After more than 70 years of evolution, great achievements have been made in machine
translation. Especially in recent years, translation quality has been greatly improved with the …

Abstract Machine translation (MT) is an important sub-field of natural language processing
that aims to translate natural languages using computers. In recent years, end-to-end neural …

The recent emergence of Large Language Models based on the Transformer architecture
has enabled dramatic advancements in the field of Natural Language Processing. However …

Deep neural networks (DNNs) have achieved unprecedented success in the field of artificial
intelligence (AI), including computer vision, natural language processing, and speech …

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 …

Non-autoregressive machine translation (NAT) systems predict a sequence of output tokens
in parallel, achieving substantial improvements in generation speed compared to …

Recent work on non-autoregressive neural machine translation (NAT) aims at improving the
efficiency by parallel decoding without sacrificing the quality. However, existing NAT …

Knowledge distillation (KD) is essential for training non-autoregressive translation (NAT)
models by reducing the complexity of the raw data with an autoregressive teacher model. In …

Fully non-autoregressive neural machine translation (NAT) is proposed to simultaneously
predict tokens with single forward of neural networks, which significantly reduces the …

Both accuracy and efficiency are crucial for image captioning in real-world scenarios.
Although Transformer-based models have gained significant improved captioning …