@inproceedings{viskov-etal-2023-semantically,
title = "Semantically-Informed Regressive Encoder Score",
author = "Viskov, Vasiliy and
Kokush, George and
Larionov, Daniil and
Eger, Steffen and
Panchenko, Alexander",
editor = "Koehn, Philipp and
Haddow, Barry and
Kocmi, Tom and
Monz, Christof",
booktitle = "Proceedings of the Eighth Conference on Machine Translation",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.wmt-1.69",
doi = "10.18653/v1/2023.wmt-1.69",
pages = "815--821",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Semantically-Informed Regressive Encoder Score
%A Viskov, Vasiliy
%A Kokush, George
%A Larionov, Daniil
%A Eger, Steffen
%A Panchenko, Alexander
%Y Koehn, Philipp
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Monz, Christof
%S Proceedings of the Eighth Conference on Machine Translation
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F viskov-etal-2023-semantically
%X 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.
%R 10.18653/v1/2023.wmt-1.69
%U https://aclanthology.org/2023.wmt-1.69
%U https://doi.org/10.18653/v1/2023.wmt-1.69
%P 815-821
Markdown (Informal)
[Semantically-Informed Regressive Encoder Score](https://aclanthology.org/2023.wmt-1.69) (Viskov et al., WMT 2023)
ACL
- Vasiliy Viskov, George Kokush, Daniil Larionov, Steffen Eger, and Alexander Panchenko. 2023. Semantically-Informed Regressive Encoder Score. In Proceedings of the Eighth Conference on Machine Translation, pages 815–821, Singapore. Association for Computational Linguistics.