Seminar by Adrien Bardet, PhD Student at LIUM, Team LST
Multilingual neural architectures for natural language processing
Machine translation using little data leads to poor performance. The use of multilingual systems is one solution to this problem. Multilingual machine translation systems make it possible to translate several languages within the same system. They allow languages with little data to be learned alongside languages with more data, thus improving the performance of the translation system. This thesis focuses on multilingual machine translation approaches to improve performance for languages with limited data. I have worked on several multilingual translation approaches based on different transfer techniques between languages. The different approaches proposed, as well as additional analyses, have revealed the impact of the relevant criteria for transfer. They also show the importance, sometimes neglected, of the balance of languages within multilingual approaches.