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 …

We present a survey covering the state of the art in low-resource machine translation (MT)
research. There are currently around 7,000 languages spoken in the world and almost all …

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

Recently, neural machine translation (NMT) has been extended to multilinguality, that is to
handle more than one translation direction with a single system. Multilingual NMT showed …

Large generative language models have been very successful for English, but other
languages lag behind, in part due to data and computational limitations. We propose a …

We present Samanantar, the largest publicly available parallel corpora collection for Indic
languages. The collection contains a total of 49.7 million sentence pairs between English …

We present effective pre-training strategies for neural machine translation (NMT) using
parallel corpora involving a pivot language, ie, source-pivot and pivot-target, leading to a …

Transfer learning or multilingual model is essential for low-resource neural machine
translation (NMT), but the applicability is limited to cognate languages by sharing their …

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 …