Low-resource MT: few-shot learning and historical language normalisation

Abstract: Huge progress has been seen in machine translation (MT), spurred on by advances in neural architectures. However, challenges still remain. One of the big challenges is dealing with scenarios that are low-resource (where there is little parallel data available). In this talk, I will present several works dealing with low-resource scenarios and efforts to improve translation and translation-like tasks, including (i) few-shot learning to integrate new vocabulary into MT, and (ii) experiments for the normalization of Modern French into contemporary French.