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Titlebook: Auto-Grader - Auto-Grading Free Text Answers; Robin Richner Book 2022 The Editor(s) (if applicable) and The Author(s), under exclusive lic

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發(fā)表于 2025-3-21 17:02:27 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱(chēng)Auto-Grader - Auto-Grading Free Text Answers
影響因子2023Robin Richner
視頻videohttp://file.papertrans.cn/167/166073/166073.mp4
學(xué)科分類(lèi)BestMasters
圖書(shū)封面Titlebook: Auto-Grader - Auto-Grading Free Text Answers; Robin Richner Book 2022 The Editor(s) (if applicable) and The Author(s), under exclusive lic
影響因子Teachers spend a great amount of time grading free text answer type questions. To encounter this challenge an auto-grader system is proposed. The thesis illustrates that the auto-grader can be approached with simple, recurrent, and Transformer-based neural networks. Hereby, the Transformer-based models has the best performance. It is further demonstrated that geometric representation of question-answer pairs is a worthwhile strategy for an auto-grader. Finally, it is indicated that while the auto-grader could potentially assist teachers in saving time with grading, it is not yet on a level to fully replace teachers for this task.
Pindex Book 2022
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https://doi.org/10.1007/978-3-531-94266-7th the two best-performing models which will be referred to as “tuned 1”. This is followed by another hyperparameter tuning iteration. The new optimal hyperparameters are then utilized again for another training on all data for the previously stated two best-performing models referred to as “tuned 2”.
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Evaluation,th the two best-performing models which will be referred to as “tuned 1”. This is followed by another hyperparameter tuning iteration. The new optimal hyperparameters are then utilized again for another training on all data for the previously stated two best-performing models referred to as “tuned 2”.
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