The development of deep learning techniques has allowed Neural Machine Translation
(NMT) models to become extremely powerful, given sufficient training data and training time …

Abstract Domain Adaptation (DA) has been a well-known transfer learning algorithm used in
Neural Machine Translation (NMT) task. Adding domain-related corpora in training data to …

Unavailability of parallel corpora for training text style transfer (TST) models is a very
challenging yet common scenario. Also, TST models implicitly need to preserve the content …

Back-translation has been proven to be effective in unsupervised domain adaptation of
neural machine translation (NMT). However, the existing back-translation methods mainly …

One of the significant challenges of Machine Translation (MT) is the scarcity of large
amounts of data, mainly parallel sentence aligned corpora. If the evaluation is as rigorous as …

Unsupervised on-the-fly back-translation, in conjunction with multilingual pretraining, is the
dominant method for unsupervised neural machine translation. Theoretically, however, the …

Abstract “近年来, 预训练方法在自然语言处理领域引起了广泛关注, 但是在比如藏汉机器等低
资源的任务设定下, 由于双语监督信息无法直接参与预训练, 限制了预训练模型在此类任务上的 …

General translation models often still struggle to generate accurate translations in
specialized domains. To guide machine translation practitioners and characterize the …

Within the field of neural machine translation (NMT), transfer learning and domain
adaptation techniques have emerged as central solutions to overcome the data scarcity …

Abstract Machine Translation is an area of Natural Language Processing that involves
building systems that translate information from one language to another. Over the past few …