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Predicting Students' Performance: Incremental Interaction Classifiers., , , and . L@S, page 217-220. ACM, (2016)Validation of the Cybervictimization Questionnaire (CYVIC) for adolescents., , , and . Comput. Hum. Behav., (2017)eGraph tool: graphing the learning process in LMSs., , , and . LAK, page 273-274. ACM, (2014)Noisy UGC Translation at the Character Level: Revisiting Open-Vocabulary Capabilities and Robustness of Char-Based Models., , and . W-NUT, page 199-211. Association for Computational Linguistics, (2021)LIMSI @ WMT 2020., , , and . WMT@EMNLP, page 803-812. Association for Computational Linguistics, (2020)Comparison between NMT and PBSMT Performance for Translating Noisy User-Generated Content., , and . NODALIDA, page 2-14. Linköping University Electronic Press, (2019)Multi-way Variational NMT for UGC: Improving Robustness in Zero-shot Scenarios via Mixture Density Networks., , and . NoDaLiDa, page 447-459. University of Tartu Library, (2023)Understanding the Impact of UGC Specificities on Translation Quality., , and . W-NUT, page 189-198. Association for Computational Linguistics, (2021)A detailed study of resampling algorithms for cyberattack classification in engineering applications., , , and . PeerJ Comput. Sci., (2024)Phonetic Normalization for Machine Translation of User Generated Content., , and . W-NUT@EMNLP, page 407-416. Association for Computational Linguistics, (2019)