Masarykova univerzita

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Filtrování publikací

    2023

  1. Applications of deep language models for reflective writings
    NEHYBA, Jan a Michal ŠTEFÁNIK. Applications of deep language models for reflective writings. EDUCATION AND INFORMATION TECHNOLOGIES. UNITED STATES: SPRINGER, 2023, roč. 28, č. 3, s. 2961-2999. ISSN 1360-2357. doi:10.1007/s10639-022-11254-7.
    Podrobněji: https://is.muni.cz/publication/1876027/cs
  2. People and Places of Historical Europe: Bootstrapping Annotation Pipeline and a New Corpus of Named Entities in Late Medieval Texts
    NOVOTNÝ, Vít, Kristýna LUGER, Michal ŠTEFÁNIK, Tereza VRABCOVÁ a Aleš HORÁK. People and Places of Historical Europe: Bootstrapping Annotation Pipeline and a New Corpus of Named Entities in Late Medieval Texts. In Findings of the Association for Computational Linguistics: ACL 2023. 2023.
    Podrobněji: https://is.muni.cz/publication/2289944/cs
  3. Resources and Few-shot Learners for In-context Learning in Slavic Languages
    ŠTEFÁNIK, Michal, Marek KADLČÍK, Piotr GRAMACKI a Petr SOJKA. Resources and Few-shot Learners for In-context Learning in Slavic Languages. In Jakub Piskorski, Michał Marcińczuk, Preslav Nakov, Maciej Ogrodniczuk, Senja Pollak, Pavel Přibáň, Piotr Rybak, Josef Steinberger, Roman Yangarber. Proceedings of the 9th Workshop on Slavic Natural Language Processing 2023 (SlavicNLP 2023). Dubrovnik, Croatia: Association for Computational Linguistics, 2023. s. 94-105.
    Podrobněji: https://is.muni.cz/publication/2288097/cs
  4. Soft Alignment Objectives for Robust Adaptation of Language Generation
    ŠTEFÁNIK, Michal, Marek KADLČÍK a Petr SOJKA. Soft Alignment Objectives for Robust Adaptation of Language Generation. Toronto, Canada: Association for Computational Linguistics, 2023.
    Podrobněji: https://is.muni.cz/publication/2288099/cs

    5. 2022

  5. AdaptOr: Objective-Centric Adaptation Framework for Language Models
    ŠTEFÁNIK, Michal, Vít NOVOTNÝ, Nikola GROVEROVÁ a Petr SOJKA. AdaptOr: Objective-Centric Adaptation Framework for Language Models. In Valerio Basile, Zornitsa Kozareva, Sanja Stajner. Proceedings of the 60th Conference of Association of Computational Linguistics, ACL 2022. Dublin, Irsko: Association for Computational Linguistics, ACL, 2022. s. 261-269. ISBN 978-1-955917-24-7. doi:10.18653/v1/2022.acl-demo.26.
    Podrobněji: https://is.muni.cz/publication/1847597/cs
  6. Combining Sparse and Dense Information Retrieval: Soft Vector Space Model and MathBERTa at ARQMath-3 Task 1 (Answer Retrieval)
    NOVOTNÝ, Vít a Michal ŠTEFÁNIK. Combining Sparse and Dense Information Retrieval: Soft Vector Space Model and MathBERTa at ARQMath-3 Task 1 (Answer Retrieval). In Guglielmo Faggioli, Nicola Ferro, Allan Hanbury, Martin Potthast. Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum. Bologna: CEUR-WS, 2022. s. 104-118. ISSN 1613-0073.
    Podrobněji: https://is.muni.cz/publication/2211178/cs
  7. Diverse Semantics Representation is King
    GELETKA, Martin, Vojtěch KALIVODA, Michal ŠTEFÁNIK, Marek TOMA a Petr SOJKA. Diverse Semantics Representation is King. In Guglielmo Faggioli, Nicola Ferro, Allan Hanbury, Martin Potthast. Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum. Bologna: CEUR.org, 2022. s. 28-39. ISSN 1613-0073.
    Podrobněji: https://is.muni.cz/publication/1876037/cs
  8. Information Extraction from Business Documents
    GELETKA, Martin, Mikuláš BANKOVIČ, Dávid MELUŠ, Šárka ŠČAVNICKÁ, Michal ŠTEFÁNIK a Petr SOJKA. Information Extraction from Business Documents. In Aleš Horák, Pavel Rychlý, Adam Rambousek. Recent Advances in Slavonic Natural Language Processing (RASLAN 2022). Brno: Tribun EU, 2022. s. 35-46. ISBN 978-80-263-1752-4.
    Podrobněji: https://is.muni.cz/publication/2233379/cs
  9. Methods for Estimating and Improving Robustness of Language Models.
    ŠTEFÁNIK, Michal. Methods for Estimating and Improving Robustness of Language Models. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop. Seattle, Washington + Online: Association for Computational Linguistics, 2022. s. 44-51. ISBN 978-1-7138-5621-4. doi:10.18653/v1/2022.naacl-srw.6.
    Podrobněji: https://is.muni.cz/publication/1875957/cs
  10. Towards General Document Understanding through Question Answering
    ŠČAVNICKÁ, Šárka, Michal ŠTEFÁNIK, Marek KADLČÍK, Martin GELETKA a Petr SOJKA. Towards General Document Understanding through Question Answering. In Recent Advances in Slavonic Natural Language Processing (RASLAN 2022). Recent Advances in Slavonic. Brno: Tribun EU, 2022. s. 181-188. ISBN 978-80-263-1752-4.
    Podrobněji: https://is.muni.cz/publication/2234001/cs
  11. When FastText Pays Attention: Efficient Estimation of Word Representations using Constrained Positional Weighting
    NOVOTNÝ, Vít, Michal ŠTEFÁNIK, Eniafe Festus AYETIRAN, Petr SOJKA a Radim ŘEHŮŘEK. When FastText Pays Attention: Efficient Estimation of Word Representations using Constrained Positional Weighting. Journal of Universal Computer Science. New York, USA: J.UCS Consortium, 2022, roč. 28, č. 2, s. 181-201. ISSN 0948-695X. doi:10.3897/jucs.69619.
    Podrobněji: https://is.muni.cz/publication/1762317/cs

    12. 2021

  12. Česko-Anglický reflektivní dataset (CEReD)
    ŠTEFÁNIK, Michal a Jan NEHYBA. Česko-Anglický reflektivní dataset (CEReD). 2021. doi:11372/LRT-3573.
    Podrobněji: https://is.muni.cz/publication/1733627/cs
  13. Ensembling Ten Math Information Retrieval Systems: MIRMU and MSM at ARQMath 2021
    NOVOTNÝ, Vít, Michal ŠTEFÁNIK, Dávid LUPTÁK, Martin GELETKA, Petr ZELINA a Petr SOJKA. Ensembling Ten Math Information Retrieval Systems: MIRMU and MSM at ARQMath 2021. In Guglielmo Faggioli. CEUR Workshop Proceedings. Bucharest, Romania: M. Jeusfeld c/o Redaktion Sun SITE, Informatik V, RWTH Aachen, 2021. s. 82-106. ISSN 1613-0073.
    Podrobněji: https://is.muni.cz/publication/1793382/cs
  14. On Eliminating Inductive Biases of Deep Language Models
    ŠTEFÁNIK, Michal. On Eliminating Inductive Biases of Deep Language Models. In ALPS 2021. 2021.
    Podrobněji: https://is.muni.cz/publication/1732357/cs
  15. One Size Does Not Fit All: Finding the Optimal Subword Sizes for FastText Models across Languages
    NOVOTNÝ, Vít, Eniafe Festus AYETIRAN, Dalibor BAČOVSKÝ, Dávid LUPTÁK, Michal ŠTEFÁNIK a Petr SOJKA. One Size Does Not Fit All: Finding the Optimal Subword Sizes for FastText Models across Languages. In Mitkov, Ruslan and Angelova, Galia. Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021). Varna, Bulgaria: INCOMA Ltd., 2021. s. 1068-1074. ISBN 978-954-452-072-4. doi:10.26615/978-954-452-072-4_120.
    Podrobněji: https://is.muni.cz/publication/1784399/cs
  16. Regressive Ensemble for Machine Translation Quality Evaluation
    ŠTEFÁNIK, Michal, Vít NOVOTNÝ a Petr SOJKA. Regressive Ensemble for Machine Translation Quality Evaluation. In Loïc Barrault et al. Proceedings of EMNLP 2021 Sixth Conference on Machine Translation (WMT 21). Online and Punta Cana, Dominican Republi: ACL, 2021. s. 1041-1048. ISBN 978-1-954085-94-7.
    Podrobněji: https://is.muni.cz/publication/1790577/cs
  17. Towards Domain Robustness of Neural Language Models
    ŠTEFÁNIK, Michal a Petr SOJKA. Towards Domain Robustness of Neural Language Models. In Horák, Rychlý, Rambousek. Recent Advances in Slavonic Natural Language Processing (RASLAN 2021). Brno: Tribun EU, 2021. s. 91-103. ISBN 978-80-263-1670-1.
    Podrobněji: https://is.muni.cz/publication/1809743/cs
  18. WebMIaS on Docker: Deploying Math-Aware Search in a Single Line of Code
    LUPTÁK, Dávid, Vít NOVOTNÝ, Michal ŠTEFÁNIK a Petr SOJKA. WebMIaS on Docker: Deploying Math-Aware Search in a Single Line of Code. In Fairouz Kamareddine and Claudio Sacerdotti-Coen. Intelligent Computer Mathematics: 14th International Conference, CICM 2021, Timisoara, Romania, July 26–31, 2021, Proceedings. LNAI 12833. Timisoara, Romania: Springer, 2021. s. 159-164. ISBN 978-3-030-81096-2. doi:10.1007/978-3-030-81097-9_13.
    Podrobněji: https://is.muni.cz/publication/1772932/cs

    19. 2020

  19. Automatická detekce reflektivního psaní u studentů učitelství
    NEHYBA, Jan, Michal ŠTEFÁNIK a Jana KRATOCHVÍLOVÁ. Automatická detekce reflektivního psaní u studentů učitelství. 2020.
    Podrobněji: https://is.muni.cz/publication/1685067/cs
  20. Three is Better than One: Ensembling Math Information Retrieval Systems
    NOVOTNÝ, Vít, Petr SOJKA, Michal ŠTEFÁNIK a Dávid LUPTÁK. Three is Better than One: Ensembling Math Information Retrieval Systems. CEUR Workshop Proceedings. Thessaloniki, Greece: M. Jeusfeld c/o Redaktion Sun SITE, Informatik V, RWTH Aachen., 2020, roč. 2020, č. 2696, s. 93-122. ISSN 1613-0073.
    Podrobněji: https://is.muni.cz/publication/1676805/cs
  21. Towards Useful Word Embeddings: Evaluation on Information Retrieval, Text Classification, and Language Modeling
    NOVOTNÝ, Vít, Michal ŠTEFÁNIK, Dávid LUPTÁK a Petr SOJKA. Towards Useful Word Embeddings: Evaluation on Information Retrieval, Text Classification, and Language Modeling. In Aleš Horák and Pavel Rychlý and Adam Rambousek. Proceedings of the Fourteenth Workshop on Recent Advances in Slavonic Natural Language Processing, RASLAN 2020. Brno: Tribun EU, 2020. s. 37-46. ISBN 978-80-263-1600-8.
    Podrobněji: https://is.muni.cz/publication/1699698/cs

    22. 2019

  22. Quo Vadis, Math Information Retrieval
    SOJKA, Petr, Vít NOVOTNÝ, Eniafe Festus AYETIRAN, Dávid LUPTÁK a Michal ŠTEFÁNIK. Quo Vadis, Math Information Retrieval. In Aleš Horák and Pavel Rychlý and Adam Rambousek. Proceedings of the Thirteenth Workshop on Recent Advances in Slavonic Natural Language Processing, RASLAN 2019. Brno: Tribun EU, 2019. s. 117-128. ISBN 978-80-263-1517-9.
    Podrobněji: https://is.muni.cz/publication/1585240/cs
  23. Video699: Interconnecting Lecture Recordings with Study Materials
    ŠTEFÁNIK, Michal a Vít NOVOTNÝ. Video699: Interconnecting Lecture Recordings with Study Materials. 2019.
    Podrobněji: https://is.muni.cz/publication/1525398/cs

    24. 2016

  24. Stream4Flow: Software for mining and analysis of the large volumes of network traffic (software)
    JIRSÍK, Tomáš, Milan ČERMÁK, Daniel TOVARŇÁK, Jakub Samuel PAULOVIČ a Michal ŠTEFÁNIK. Stream4Flow: Software for mining and analysis of the large volumes of network traffic. 2016.
    Podrobněji: https://is.muni.cz/publication/1372396/cs
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Zobrazeno: 10. 6. 2023 20:57