Quality Estimation of Machine Translation for Low-Resource Languages
Organization
TartuNLP
Abstract
The goal of this thesis is to apply the mechanistic interpretability of neural networks to automatic prediction of translation quality.
Our team works on machine translation for low-resource Finno-Ugric languages. To evaluate the quality of the translation during a training phase, we compare the translations with human references. However, for the online system, the evaluation should be done in real time, without relying on human references.
Our team works on machine translation for low-resource Finno-Ugric languages. To evaluate the quality of the translation during a training phase, we compare the translations with human references. However, for the online system, the evaluation should be done in real time, without relying on human references.
Graduation Theses defence year
2024-2025
Supervisor
Lisa Yankovskaya, Mark FiĊĦel
Spoken language (s)
English
Requirements for candidates
Level
Masters
Keywords
Application of contact
Name
Lisa Yankovsksya
Phone
E-mail