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        <title>deepQuest: A Framework for Neural-based Quality Estimation</title>
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            <title>Proceedings of the 27th International Conference on Computational Linguistics</title>
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            <namePart type="given">Emily</namePart>
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    <abstract>Predicting Machine Translation (MT) quality can help in many practical tasks such as MT post-editing. The performance of Quality Estimation (QE) methods has drastically improved recently with the introduction of neural approaches to the problem. However, thus far neural approaches have only been designed for word and sentence-level prediction. We present a neural framework that is able to accommodate neural QE approaches at these fine-grained levels and generalize them to the level of documents. We test the framework with two sentence-level neural QE approaches: a state of the art approach that requires extensive pre-training, and a new light-weight approach that we propose, which employs basic encoders. Our approach is significantly faster and yields performance improvements for a range of document-level quality estimation tasks. To our knowledge, this is the first neural architecture for document-level QE. In addition, for the first time we apply QE models to the output of both statistical and neural MT systems for a series of European languages and highlight the new challenges resulting from the use of neural MT.</abstract>
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%0 Conference Proceedings
%T deepQuest: A Framework for Neural-based Quality Estimation
%A Ive, Julia
%A Blain, Frédéric
%A Specia, Lucia
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F ive-etal-2018-deepquest
%X Predicting Machine Translation (MT) quality can help in many practical tasks such as MT post-editing. The performance of Quality Estimation (QE) methods has drastically improved recently with the introduction of neural approaches to the problem. However, thus far neural approaches have only been designed for word and sentence-level prediction. We present a neural framework that is able to accommodate neural QE approaches at these fine-grained levels and generalize them to the level of documents. We test the framework with two sentence-level neural QE approaches: a state of the art approach that requires extensive pre-training, and a new light-weight approach that we propose, which employs basic encoders. Our approach is significantly faster and yields performance improvements for a range of document-level quality estimation tasks. To our knowledge, this is the first neural architecture for document-level QE. In addition, for the first time we apply QE models to the output of both statistical and neural MT systems for a series of European languages and highlight the new challenges resulting from the use of neural MT.
%U https://aclanthology.org/C18-1266
%P 3146-3157

Markdown (Informal)

[deepQuest: A Framework for Neural-based Quality Estimation](https://aclanthology.org/C18-1266) (Ive et al., COLING 2018)

• deepQuest: A Framework for Neural-based Quality Estimation (Ive et al., COLING 2018)

ACL

• Julia Ive, Frédéric Blain, and Lucia Specia. 2018. deepQuest: A Framework for Neural-based Quality Estimation. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3146–3157, Santa Fe, New Mexico, USA. Association for Computational Linguistics.