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 …

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

Having numerous potential applications and great impact, end-to-end speech translation
(ST) has long been treated as an independent task, failing to fully draw strength from the …

Hallucinations and off-target translation remain unsolved problems in machine translation,
especially for low-resource languages and massively multilingual models. In this paper, we …

State-of-the-art multilingual machine translation relies on a universal encoder-decoder,
which requires retraining the entire system to add new languages. In this paper, we propose …

Multilingual neural machine translation (MNMT) trained in multiple language pairs has
attracted considerable attention due to fewer model parameters and lower training costs by …

We present a new approach to perform zero-shot cross-modal transfer between speech and
text for translation tasks. Multilingual speech and text are encoded in a joint fixed-size …

Multilingual neural machine translation (NMT) has led to impressive accuracy improvements
in low-resource scenarios by sharing common linguistic information across languages …

Multilingual machine translation suffers from negative interference across languages. A
common solution is to relax parameter sharing with language-specific modules like …