Search Results for author: Michal Štefánik

Found 7 papers, 5 papers with code

Adaptor: Objective-Centric Adaptation Framework for Language Models

1 code implementation ACL 2022 Michal Štefánik, Vít Novotný, Nikola Groverová, Petr Sojka

This paper introduces Adaptor library, which transposes traditional model-centric approach composed of pre-training + fine-tuning steps to objective-centric approach, composing the training process by applications of selected objectives. We survey research directions that can benefit from enhanced objective-centric experimentation in multitask training, custom objectives development, dynamic training curricula, or domain adaptation. Adaptor aims to ease reproducibility of these research directions in practice.

Unsupervised Domain Adaptation

Adapt$\mathcal{O}$r: Objective-Centric Adaptation Framework for Language Models

1 code implementation8 Mar 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.

Unsupervised Domain Adaptation

Regressive Ensemble for Machine Translation Quality Evaluation

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.

Machine Translation Translation

WebMIaS on Docker: Deploying Math-Aware Search in a Single Line of Code

no code implementations1 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.

When FastText Pays Attention: Efficient Estimation of Word Representations using Constrained Positional Weighting

1 code implementation19 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.

Language Modelling Machine Translation +1

Text classification with word embedding regularization and soft similarity measure

1 code implementation10 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.

Classification Document Classification +4

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