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 …

The main challenge in the field of unsupervised machine translation (UMT) is to associate
source-target sentences in the latent space. As people who speak different languages share …

Sign languages are visual languages using manual articulations and non-manual elements
to convey information. For sign language recognition and translation, the majority of existing …

Most machine-learning methods focus on classifying instances whose classes have already
been seen in training. In practice, many applications require classifying instances whose …

Multilingual neural machine translation (NMT) enables training a single model that supports
translation from multiple source languages into multiple target languages. In this paper, we …

Massively multilingual models for neural machine translation (NMT) are theoretically
attractive, but often underperform bilingual models and deliver poor zero-shot translations. In …

We introduce our efforts towards building a universal neural machine translation (NMT)
system capable of translating between any language pair. We set a milestone towards this …

Machine translation systems achieve near human-level performance on some languages,
yet their effectiveness strongly relies on the availability of large amounts of parallel …

In spite of the recent success of neural machine translation (NMT) in standard benchmarks,
the lack of large parallel corpora poses a major practical problem for many language pairs …