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    <titleInfo>
        <title>Assessing Quality Estimation Models for Sentence-Level Prediction</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>
            <namePart type="given">M</namePart>
            <namePart type="family">Bender</namePart>
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            <namePart type="given">Leon</namePart>
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            <namePart type="given">Pierre</namePart>
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    <abstract>This paper provides an evaluation of a wide range of advanced sentence-level Quality Estimation models, including Support Vector Regression, Ride Regression, Neural Networks, Gaussian Processes, Bayesian Neural Networks, Deep Kernel Learning and Deep Gaussian Processes. Beside the accurateness, our main concerns are also the robustness of Quality Estimation models. Our work raises the difficulty in building strong models. Specifically, we show that Quality Estimation models often behave differently in Quality Estimation feature space, depending on whether the scale of feature space is small, medium or large. We also show that Quality Estimation models often behave differently in evaluation settings, depending on whether test data come from the same domain as the training data or not. Our work suggests several strong candidates to use in different circumstances.</abstract>
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        <url>https://aclanthology.org/C18-1129</url>
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        <date>2018-08</date>
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%0 Conference Proceedings
%T Assessing Quality Estimation Models for Sentence-Level Prediction
%A Cuong, Hoang
%A Xu, Jia
%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 cuong-xu-2018-assessing
%X This paper provides an evaluation of a wide range of advanced sentence-level Quality Estimation models, including Support Vector Regression, Ride Regression, Neural Networks, Gaussian Processes, Bayesian Neural Networks, Deep Kernel Learning and Deep Gaussian Processes. Beside the accurateness, our main concerns are also the robustness of Quality Estimation models. Our work raises the difficulty in building strong models. Specifically, we show that Quality Estimation models often behave differently in Quality Estimation feature space, depending on whether the scale of feature space is small, medium or large. We also show that Quality Estimation models often behave differently in evaluation settings, depending on whether test data come from the same domain as the training data or not. Our work suggests several strong candidates to use in different circumstances.
%U https://aclanthology.org/C18-1129
%P 1521-1533

Markdown (Informal)

[Assessing Quality Estimation Models for Sentence-Level Prediction](https://aclanthology.org/C18-1129) (Cuong & Xu, COLING 2018)

• Assessing Quality Estimation Models for Sentence-Level Prediction (Cuong & Xu, COLING 2018)

ACL

• Hoang Cuong and Jia Xu. 2018. Assessing Quality Estimation Models for Sentence-Level Prediction. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1521–1533, Santa Fe, New Mexico, USA. Association for Computational Linguistics.