@inproceedings{HartungHerygersKurlekaretal.2023, author = {Hartung, Kai and Herygers, Aaricia and Kurlekar, Shubham Vijay and Zakaria, Khabbab and Volkan, Taylan and Gr{\"o}ttrup, S{\"o}ren and Georges, Munir}, title = {Measuring Sentiment Bias in Machine Translation}, booktitle = {Text, Speech, and Dialogue: 26th International Conference: Proceedings}, editor = {Ekštein, Kamil and P{\´a}rtl, František and Konop{\´i}k, Miloslav}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-40498-6}, issn = {1611-3349}, doi = {https://doi.org/10.1007/978-3-031-40498-6_8}, pages = {82 -- 93}, year = {2023}, language = {en} } @inproceedings{HartungJaegerGroettrupetal.2022, author = {Hartung, Kai and J{\"a}ger, Gerhard and Gr{\"o}ttrup, S{\"o}ren and Georges, Munir}, title = {Typological Word Order Correlations with Logistic Brownian Motion}, pages = {2022.sigtyp-1.3}, booktitle = {Proceedings of the 4th Workshop on Computational Typology and Multilingual NLP (SIGTYP 2022)}, editor = {Vylomova, Ekaterina and Ponti, Edoardo and Cotterell, Ryan}, publisher = {Association for Computational Linguistics}, address = {Stroudsburg}, isbn = {978-1-955917-93-3}, url = {https://aclanthology.org/2022.sigtyp-1.3}, pages = {22 -- 26}, year = {2022}, abstract = {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also strongly indicate lineage specific correlation patters instead of universal dependencies.}, language = {en} } @unpublished{PagonisHartungWuetal.2024, author = {Pagonis, Panagiotis and Hartung, Kai and Wu, Di and Georges, Munir and Gr{\"o}ttrup, S{\"o}ren}, title = {Analysis of Knowledge Tracing performance on synthesised student data}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2401.16832}, year = {2024}, abstract = {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.}, language = {en} } @inproceedings{PagonisHartungWuetal.2024, author = {Pagonis, Panagiotis and Hartung, Kai and Wu, Di and Georges, Munir and Gr{\"o}ttrup, S{\"o}ren}, title = {Analysis of Knowledge Tracing performance on synthesised student data}, publisher = {Universit{\"a}t Bamberg}, address = {Bamberg}, url = {https://sme.uni-bamberg.de/ai4ai/}, year = {2024}, language = {en} }