37 citations · 39 across the 3 of their papers we have counts for
3 papers
cs.IR2021★ 2 cited
Siamese BERT-based Model for Web Search Relevance Ranking Evaluated on a New Czech Dataset
Matěj Kocián, Jakub Náplava, Daniel Štancl +1
Web search engines focus on serving highly relevant results within hundreds of milliseconds. Pre-trained language transformer models such as BERT are therefore hard to use in this…
cs.CL2021
Understanding Model Robustness to User-generated Noisy Texts
Jakub Náplava, Martin Popel, Milan Straka +1
Sensitivity of deep-neural models to input noise is known to be a challenging problem. In NLP, model performance often deteriorates with naturally occurring noise, such as spelling…
cs.CL2021★ 37 cited
RobeCzech: Czech RoBERTa, a monolingual contextualized language representation model
Milan Straka, Jakub Náplava, Jana Straková +1
We present RobeCzech, a monolingual RoBERTa language representation model trained on Czech data. RoBERTa is a robustly optimized Transformer-based pretraining approach. We show tha…