no code implementations • 11 May 2023 • Lukáš Mikula, Michal Štefánik, Marek Petrovič, Petr Sojka
We find that while existing debiasing methods can mitigate reliance on a chosen spurious feature, the OOD performance gains of these methods can not be explained by mitigated reliance on biased features, suggesting that biases are shared among different QA datasets.
1 code implementation • 4 Apr 2023 • Michal Štefánik, Marek Kadlčík, Piotr Gramacki, Petr Sojka
Despite the rapid recent progress in creating accurate and compact in-context learners, most recent work focuses on in-context learning (ICL) for tasks in English.
1 code implementation • 29 Nov 2022 • Michal Štefánik, Marek Kadlčík, Petr Sojka
Domain adaptation allows generative language models to address specific flaws caused by the domain shift of their application.
no code implementations • 14 Jun 2022 • Michal Balazia, Katerina Hlavackova-Schindler, Petr Sojka, Claudia Plant
We apply the graphical Granger model (GGM) to obtain the so-called Granger causal graph among joints as a discriminative and visually interpretable representation of a person's gait.
1 code implementation • ACL 2022 • Michal Štefánik, Vít Novotný, Nikola Groverová, Petr Sojka
Progress in natural language processing research is catalyzed by the possibilities given by the widespread software frameworks.
no code implementations • 8 Oct 2021 • Michal Růžička, Petr Sojka
In this paper, we investigate mathematical content representations suitable for the automated classification of and the similarity search in STEM documents using standard machine learning algorithms: the Latent Dirichlet Allocation (LDA) and the Latent Semantic Indexing (LSI).
1 code implementation • WMT (EMNLP) 2021 • Michal Štefánik, Vít Novotný, Petr Sojka
This work introduces a simple regressive ensemble for evaluating machine translation quality based on a set of novel and established metrics.
no code implementations • 1 Jun 2021 • Dávid Lupták, Vít Novotný, Michal Štefánik, Petr Sojka
Math informational retrieval (MIR) search engines are absent in the wide-spread production use, even though documents in the STEM fields contain many mathematical formulae, which are sometimes more important than text for understanding.
1 code implementation • 19 Apr 2021 • Vít Novotný, Michal Štefánik, Eniafe Festus Ayetiran, Petr Sojka, Radim Řehůřek
In 2018, Mikolov et al. introduced the positional language model, which has characteristics of attention-based neural machine translation models and which achieved state-of-the-art performance on the intrinsic word analogy task.
no code implementations • 27 Feb 2021 • Eniafe Festus Ayetiran, Petr Sojka, Vít Novotný
We report evaluation results on 11 benchmark datasets involving WSD and Word Similarity tasks and show that our method for enhancing distributional semantic structures improves embeddings quality on the baselines.
no code implementations • RANLP 2021 • Vít Novotný, Eniafe Festus Ayetiran, Dalibor Bačovský, Dávid Lupták, Michal Štefánik, Petr Sojka
In our work, we find the optimal subword sizes on the English, German, Czech, Italian, Spanish, French, Hindi, Turkish, and Russian word analogy tasks.
1 code implementation • 10 Mar 2020 • Vít Novotný, Eniafe Festus Ayetiran, Michal Štefánik, Petr Sojka
In our work, we investigate the individual and joint effect of the two word embedding regularization techniques on the document processing speed and the task performance of the SCM and the WMD on text classification.
Ranked #2 on Document Classification on Amazon
no code implementations • 24 Aug 2017 • Michal Balazia, Petr Sojka
This paper contributes to the state-of-the-art with a statistical approach for extracting robust gait features directly from raw data by a modification of Linear Discriminant Analysis with Maximum Margin Criterion.
no code implementations • WS 2017 • Jan Rygl, Jan Pomik{\'a}lek, Radim {\v{R}}eh{\r{u}}{\v{r}}ek, Michal R{\r{u}}{\v{z}}i{\v{c}}ka, V{\'\i}t Novotn{\'y}, Petr Sojka
We empirically demonstrate its querying performance and quality by applying this solution to the task of semantic searching over a dense vector representation of the entire English Wikipedia.
no code implementations • 28 Jun 2017 • Michal Balazia, Petr Sojka
This work offers a design of a video surveillance system based on a soft biometric -- gait identification from MoCap data.
no code implementations • 4 Jan 2017 • Michal Balazia, Petr Sojka
As a contribution to reproducible research, this paper presents a framework and a database to improve the development, evaluation and comparison of methods for gait recognition from motion capture (MoCap) data.
no code implementations • 22 Sep 2016 • Michal Balazia, Petr Sojka
MoCap-based human identification, as a pattern recognition discipline, can be optimized using a machine learning approach.
no code implementations • 14 Sep 2016 • Michal Balazia, Petr Sojka
In the field of gait recognition from motion capture data, designing human-interpretable gait features is a common practice of many fellow researchers.
1 code implementation • Workshop On New Challenges For NLP Frameworks 2010 • Radim Řehůřek, Petr Sojka
Large corpora are ubiquitous in today’s world and memory quickly becomes the limiting factor in practical applications of the Vector Space Model (VSM).