Titlebook: Artificial Intelligence in Education; 21st International C Ig Ibert Bittencourt,Mutlu Cukurova,Eva Millán Conference proceedings 2020 Sprin

choroid

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Yourself

https://doi.org/10.1007/978-3-8350-5424-0t phrases. The best model, based on an Universal Transformer architecture, achieved a BLEU score of 66.01. We also evaluated this model’s capability to perform similar transformation to texts that were simplified by human experts at different levels.

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https://doi.org/10.1007/978-3-658-18740-8hology theories, which provides a theoretical foundation for the newly proposed system. In addition, reinforcement learning methodology is adopted to learn dialogue policy to serve the designed dialogue system. Experimental results demonstrate that the developed dialogue system achieves its design objectives.

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Familienunternehmen und ihre StakeholderBy putting mastery learning heuristics on the same playing field as model-based algorithms, we can gain insights on their hidden assumptions about learning and why they might perform well in practice.

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https://doi.org/10.1007/978-3-322-82895-8e aspects when the test group is no longer gamified. Most studies focus on what happens when gamifying, but not when a group of students stops gamifying. The results obtained will serve to advance a part of the knowledge about gamification.

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refine

Investigating Transformers for Automatic Short Answer Gradingtaset on generalization and performance. We report up to 13% absolute improvement in macro-average-F1 over state-of-the-art results. We show that models trained with knowledge distillation are feasible for use in short answer grading. Furthermore, we compare multilingual models on a machine-translated version of the SemEval-2013 dataset.