At this week’s lab meeting, Nishant will present a brief review tracking the current state of multilingual machine translation:
Abstract: Multilingual Neural Machine Translation has been useful in improving translation quality as a result of inherent transfer learning. It is promising and interesting because end-to-end modeling and distributed representations over several languages at once open new avenues for research on machine translation. We first categorize various approaches based on their central use-case and then further categorize them based on resource scenarios, underlying modeling principles, core-issues and challenges. Wherever possible we address the strengths and weaknesses of several techniques by comparing them with each other.
Wednesday, 9 Aug at 12pm
This will be a hybrid meeting at TASC1 9408. The zoom link will be posted on zulip.
