TY - CHAP A1 - Hartung, Kai A1 - Herygers, Aaricia A1 - Kurlekar, Shubham Vijay A1 - Zakaria, Khabbab A1 - Volkan, Taylan A1 - Gröttrup, Sören A1 - Georges, Munir ED - Ekštein, Kamil ED - Pártl, František ED - Konopík, Miloslav T1 - Measuring Sentiment Bias in Machine Translation T2 - Text, Speech, and Dialogue: 26th International Conference: Proceedings UR - https://doi.org/10.1007/978-3-031-40498-6_8 KW - Machine translation KW - sentiment classification KW - bias Y1 - 2023 UR - https://doi.org/10.1007/978-3-031-40498-6_8 SN - 978-3-031-40498-6 SN - 1611-3349 SP - 82 EP - 93 PB - Springer CY - Cham ER - TY - CHAP A1 - Hartung, Kai A1 - Jäger, Gerhard A1 - Gröttrup, Sören A1 - Georges, Munir ED - Vylomova, Ekaterina ED - Ponti, Edoardo ED - Cotterell, Ryan T1 - Typological Word Order Correlations with Logistic Brownian Motion T2 - Proceedings of the 4th Workshop on Computational Typology and Multilingual NLP (SIGTYP 2022) N2 - In this study we address the question to what extent syntactic word-order traits of different languages have evolved under correlation and whether such dependencies can be found universally across all languages or restricted to specific language families.To do so, we use logistic Brownian Motion under a Bayesian framework to model the trait evolution for 768 languages from 34 language families. We test for trait correlations both in single families and universally over all families. Separate models reveal no universal correlation patterns and Bayes Factor analysis of models over all covered families also strongly indicate lineage specific correlation patters instead of universal dependencies. KW - Typology KW - Brownian Motion Y1 - 2022 UR - https://aclanthology.org/2022.sigtyp-1.3 SN - 978-1-955917-93-3 SP - 22 EP - 26 PB - Association for Computational Linguistics CY - Stroudsburg ER - TY - INPR A1 - Pagonis, Panagiotis A1 - Hartung, Kai A1 - Wu, Di A1 - Georges, Munir A1 - Gröttrup, Sören T1 - Analysis of Knowledge Tracing performance on synthesised student data N2 - Knowledge Tracing (KT) aims to predict the future performance of students by tracking the development of their knowledge states. Despite all the recent progress made in this field, the application of KT models in education systems is still restricted from the data perspectives: 1) limited access to real life data due to data protection concerns, 2) lack of diversity in public datasets, 3) noises in benchmark datasets such as duplicate records. To resolve these problems, we simulated student data with three statistical strategies based on public datasets and tested their performance on two KT baselines. While we observe only minor performance improvement with additional synthetic data, our work shows that using only synthetic data for training can lead to similar performance as real data. UR - https://doi.org/10.48550/arXiv.2401.16832 Y1 - 2024 UR - https://doi.org/10.48550/arXiv.2401.16832 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-44771 N1 - Die veröffentlichte Version dieses Preprints ist ebenfalls in diesem Repositorium verzeichnet, s. https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/4535 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Pagonis, Panagiotis A1 - Hartung, Kai A1 - Wu, Di A1 - Georges, Munir A1 - Gröttrup, Sören T1 - Analysis of Knowledge Tracing performance on synthesised student data Y1 - 2024 UR - https://sme.uni-bamberg.de/ai4ai/ N1 - Der Nachweis einer Preprint-Version dieser Veröffentlichung ist ebenfalls in diesem Repositorium verzeichnet, s. https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/4477 PB - Universität Bamberg CY - Bamberg ER -