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• W2815204851 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." @default.
• W2815204851 created "2018-07-19" @default.
• W2815204851 creator A5064649079 @default.
• W2815204851 creator A5071924020 @default.
• W2815204851 date "2018-08-01" @default.
• W2815204851 modified "2023-09-28" @default.
• W2815204851 title "Assessing Quality Estimation Models for Sentence-Level Prediction" @default.
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• W2815204851 hasPublicationYear "2018" @default.
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