Pretrained Foundation Models (PFMs) are regarded as the foundation for various
downstream tasks with different data modalities. A PFM (eg, BERT, ChatGPT, and GPT-4) is …

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 …

Adaptive training approaches, widely used in sequence-to-sequence models, commonly
reweigh the losses of different target tokens based on priors, eg word frequency. However …

Simultaneous machine translation (SiMT) outputs the translation while receiving the source
inputs, and hence needs to balance the received source information and translated target …

Transforming the large amounts of unstructured text on the Internet into structured event
knowledge is a critical, yet unsolved goal of NLP, especially when addressing document …

Most state-of-the-art neural machine translation (NMT) models progressively encode feature
representation in a bottom-up feed-forward fashion. This traditional encoding mechanism …

We outline the Great Misalignment Problem in natural language processing research, this
means simply that the problem definition is not in line with the method proposed and the …

Featured Application This paper introduces factual relation information into Transformer-
based neural machine translation to improve translation quality. Abstract Transformer-based …

In practical applications, the accuracy of domain terminology translation is an important
criterion for the performance evaluation of domain machine translation models. Aiming at the …

Knowledge distillation (KD) is a common approach to compress a teacher model to reduce
its inference cost and memory footprint, by training a smaller student model. However, in the …