George Kokush
2023
Semantically-Informed Regressive Encoder Score
Vasiliy Viskov
|
George Kokush
|
Daniil Larionov
|
Steffen Eger
|
Alexander Panchenko
Proceedings of the Eighth Conference on Machine Translation
Machine translation is natural language generation (NLG) problem of translating source text from one language to another. As every task in machine learning domain it requires to have evaluation metric. The most obvious one is human evaluation but it is expensive in case of money and time consumption. In last years with appearing of pretrained transformer architectures and large language models (LLMs) state-of-the-art results in automatic machine translation evaluation got a huge quality step in terms of correlation with expert assessment. We introduce MRE-Score, seMantically-informed Regression Encoder Score, the approach with constructing automatic machine translation evaluation system based on regression encoder and contrastive pretraining for downstream problem.