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 …

When training multilingual machine translation (MT) models that can translate to/from
multiple languages, we are faced with imbalanced training sets: some languages have …

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 …

To acquire a new skill, humans learn better and faster if a tutor, based on their current
knowledge level, informs them of how much attention they should pay to particular content or …

Unsupervised neural machine translation (UNMT) has recently achieved remarkable results
for several language pairs. However, it can only translate between a single language pair …

Active learning is an efficient approach for mitigating data dependency when training neural
machine translation (NMT) models. In this paper, we explore new training frameworks by …

Multilingual Neural Machine Translation (MNMT) facilitates knowledge sharing but often
suffers from poor zero-shot (ZS) translation qualities. While prior work has explored the …

Sparse language vectors from linguistic typology databases and learned embeddings from
tasks like multilingual machine translation have been investigated in isolation, without …

Back-translation is an effective strategy to improve the performance of Neural Machine
Translation~(NMT) by generating pseudo-parallel data. However, several recent works have …