Masarykova univerzita

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Filtrování publikací

    2024

    1. ŠTEFÁNIK, Michal, Marek KADLČÍK a Petr SOJKA. Concept-aware Data Construction Improves In-context Learning of Language Models. In ICLR 2024 Workshop on Mathematical and Empirical Understanding of Foundation Models. 2024.
    2. KADLČÍK, Marek, Michal ŠTEFÁNIK, Ondřej SOTOLÁŘ a Vlastimil MARTINEK. Self-Training Language Models in Arithmetic Reasoning. In ICLR 2024 Workshop on Large Language Model (LLM) Agents. 2024.
    3. MIKULA, Lukáš, Michal ŠTEFÁNIK, Marek PETROVIČ a Petr SOJKA. Think Twice: Measuring the Efficiency of Eliminating Prediction Shortcuts of Question Answering Models. In Yvette Graham, Matthew Purver. Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). St. Julian's, Malta: Association for Computational Linguistics, 2024, s. 2179-2193. ISBN 979-8-89176-088-2.

    2023

    1. 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. Dostupné z: https://dx.doi.org/10.1007/s10639-022-11254-7.
    2. KADLČÍK, Marek, Michal ŠTEFÁNIK, Ondřej SOTOLÁŘ a Vlastimil MARTINEK. Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic Systems. Online. In Houda Bouamor, Juan Pino, Kalika Bali. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Main track. Singapore: Association for Computational Linguistics, 2023, s. 12101-12108. ISBN 979-8-89176-060-8. Dostupné z: https://dx.doi.org/10.18653/v1/2023.emnlp-main.742.
    3. ŠTEFÁNIK, Michal a Marek KADLČÍK. Can In-context Learners Learn a Reasoning Concept from Demonstrations?. Online. In Proceedings of the 1st Workshop on Natural Language Reasoning and Structured Explanations (NLRSE). Toronto, Canada: The Association for Computational Linguistics, 2023, s. 107-115. ISBN 978-1-959429-94-4.
    4. ŠČAVNICKÁ, Šárka, Michal ŠTEFÁNIK a Petr SOJKA. Document Visual Question Answering with CIVQA: Czech Invoice Visual Question Answering Dataset. In Recent Advances in Slavonic Natural Language Processing (RASLAN 2023). Recent Advances in Slavonic. Brno: Tribun EU, 2023, s. 23-34. ISBN 978-80-263-1793-7.
    5. 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. Online. In Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki. Findings of the Association for Computational Linguistics: ACL 2023. Toronto, Canada: Association for Computational Linguistics, 2023, s. 14104-14113. ISBN 978-1-959429-62-3.
    6. ŠTEFÁNIK, Michal, Marek KADLČÍK, Piotr GRAMACKI a Petr SOJKA. Resources and Few-shot Learners for In-context Learning in Slavic Languages. Online. 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. ISBN 978-1-959429-57-9.
    7. ŠTEFÁNIK, Michal, Marek KADLČÍK a Petr SOJKA. Soft Alignment Objectives for Robust Adaptation of Language Generation. Online. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Toronto, Canada: Association for Computational Linguistics, 2023, s. 8837-8853. ISBN 978-1-959429-72-2. Dostupné z: https://dx.doi.org/10.18653/v1/2023.acl-long.492.

    2022

    1. ŠTEFÁNIK, Michal, Vít NOVOTNÝ, Nikola GROVEROVÁ a Petr SOJKA. AdaptOr: Objective-Centric Adaptation Framework for Language Models. Online. 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. Dostupné z: https://dx.doi.org/10.18653/v1/2022.acl-demo.26.
    2. NOVOTNÝ, Vít a Michal ŠTEFÁNIK. Combining Sparse and Dense Information Retrieval: Soft Vector Space Model and MathBERTa at ARQMath-3 Task 1 (Answer Retrieval). Online. 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.
    3. GELETKA, Martin, Vojtěch KALIVODA, Michal ŠTEFÁNIK, Marek TOMA a Petr SOJKA. Diverse Semantics Representation is King. Online. 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.
    4. 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.
    5. ŠTEFÁNIK, Michal. Methods for Estimating and Improving Robustness of Language Models. Online. 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. Dostupné z: https://dx.doi.org/10.18653/v1/2022.naacl-srw.6.
    6. 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.
    7. 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. Dostupné z: https://dx.doi.org/10.3897/jucs.69619.

    2021

    1. LÍŠKA, Martin, Dávid LUPTÁK, Vít STARÝ NOVOTNÝ, Michal RŮŽIČKA, Boris SHMINKE, Petr SOJKA, Michal ŠTEFÁNIK a Makarius WENZEL. CICM'21 Systems Entries. Online. In 978-3-030-81097-9. INTELLIGENT COMPUTER MATHEMATICS (CICM 2021). CHAM: SPRINGER INTERNATIONAL PUBLISHING AG, 2021, s. 245-248. ISBN 978-3-030-81096-2. Dostupné z: https://dx.doi.org/10.1007/978-3-030-81097-9_20.
    2. ŠTEFÁNIK, Michal a Jan NEHYBA. Česko-Anglický reflektivní dataset (CEReD). 2021. Dostupné z: https://dx.doi.org/11372/LRT-3573.
    3. 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. Online. 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.
    4. ŠTEFÁNIK, Michal. On Eliminating Inductive Biases of Deep Language Models. In ALPS 2021. 2021.
    5. 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. Dostupné z: https://dx.doi.org/10.26615/978-954-452-072-4_120.
    6. ŠTEFÁNIK, Michal, Vít NOVOTNÝ a Petr SOJKA. Regressive Ensemble for Machine Translation Quality Evaluation. Online. 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.
    7. 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.
    8. 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. Dostupné z: https://dx.doi.org/10.1007/978-3-030-81097-9_13.

    2020

    1. 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.
    2. 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.

    2019

    1. 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.
    2. ŠTEFÁNIK, Michal a Vít NOVOTNÝ. Video699: Interconnecting Lecture Recordings with Study Materials. 2019.

    2016

    1. 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.
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Zobrazeno: 17. 7. 2024 15:56