Neural Machine Translation (NMT) has seen tremendous growth in the last ten years since
the early 2000s and has already entered a mature phase. While considered the most widely …

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 …

Multilingual BERT (mBERT) trained on 104 languages has shown surprisingly good cross-
lingual performance on several NLP tasks, even without explicit cross-lingual signals …

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 …

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

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 …

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 …

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 …