Issue #115 – Revisiting Low-Resource Neural Machine Translation: A Case Study
28 Jan21
Issue #115 – Revisiting Low-Resource Neural Machine Translation: A Case Study
Author: Akshai Ramesh, Machine Translation Scientist @ Iconic
Introduction
Although deep neural models produce state-of-the-art results in many translation tasks, they are found to underperform phrase-based statistical machine translation in resource poor conditions. The majority of research on low-resource neural machine translation (NMT) focuses on the exploitation of monolingual or parallel data involving other language pairs. There is notably less attention into the research of low-resource NMT without the use of auxiliary data.
In today’s blog post, we will look at the work of Sennrich and Zhang, 2019 that comes from the University of Edinburgh. This paper investigates the best practices for low-resource recurrent NMT models and shows that more efficient use