We present a survey on multilingual neural machine translation (MNMT), which has gained a lot of traction in recent years. MNMT has been useful in improving translation quality as a …
S Wu, M Dredze - arXiv preprint arXiv:2005.09093, 2020 - arxiv.org
Multilingual BERT (mBERT) trained on 104 languages has shown surprisingly good cross- lingual performance on several NLP tasks, even without explicit cross-lingual signals …
X Pan, M Wang, L Wu, L Li - arXiv preprint arXiv:2105.09501, 2021 - arxiv.org
Existing multilingual machine translation approaches mainly focus on English-centric directions, while the non-English directions still lag behind. In this work, we aim to build a …
D Saunders - Journal of Artificial Intelligence Research, 2022 - jair.org
The development of deep learning techniques has allowed Neural Machine Translation (NMT) models to become extremely powerful, given sufficient training data and training time …
J Guo, Z Zhang, L Xu, HR Wei… - Advances in Neural …, 2020 - proceedings.neurips.cc
While large scale pre-trained language models such as BERT have achieved great success on various natural language understanding tasks, how to efficiently and effectively …
Neural approaches have achieved state-of-the-art accuracy on machine translation but suffer from the high cost of collecting large scale parallel data. Thus, a lot of research has …
J Zhang, C Zong - Science China Technological Sciences, 2020 - Springer
Abstract Machine translation (MT) is a technique that leverages computers to translate human languages automatically. Nowadays, neural machine translation (NMT) which …
Abstract Machine learning (ML) systems in natural language processing (NLP) face significant challenges in generalizing to out-of-distribution (OOD) data, where the test …
SM Singh, TD Singh - Neural Computing and Applications, 2022 - Springer
Abstract Machine translation requires a large amount of parallel data for a production level of translation quality. This is one of the significant factors behind the lack of machine …