Pavel Přibáň


2022

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Czech Dataset for Cross-lingual Subjectivity Classification
Pavel Přibáň | Josef Steinberger
Proceedings of the Thirteenth Language Resources and Evaluation Conference

In this paper, we introduce a new Czech subjectivity dataset of 10k manually annotated subjective and objective sentences from movie reviews and descriptions. Our prime motivation is to provide a reliable dataset that can be used with the existing English dataset as a benchmark to test the ability of pre-trained multilingual models to transfer knowledge between Czech and English and vice versa. Two annotators annotated the dataset reaching 0.83 of the Cohen’s K inter-annotator agreement. To the best of our knowledge, this is the first subjectivity dataset for the Czech language. We also created an additional dataset that consists of 200k automatically labeled sentences. Both datasets are freely available for research purposes. Furthermore, we fine-tune five pre-trained BERT-like models to set a monolingual baseline for the new dataset and we achieve 93.56% of accuracy. We fine-tune models on the existing English dataset for which we obtained results that are on par with the current state-of-the-art results. Finally, we perform zero-shot cross-lingual subjectivity classification between Czech and English to verify the usability of our dataset as the cross-lingual benchmark. We compare and discuss the cross-lingual and monolingual results and the ability of multilingual models to transfer knowledge between languages.

2021

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Are the Multilingual Models Better? Improving Czech Sentiment with Transformers
Pavel Přibáň | Josef Steinberger
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

In this paper, we aim at improving Czech sentiment with transformer-based models and their multilingual versions. More concretely, we study the task of polarity detection for the Czech language on three sentiment polarity datasets. We fine-tune and perform experiments with five multilingual and three monolingual models. We compare the monolingual and multilingual models’ performance, including comparison with the older approach based on recurrent neural networks. Furthermore, we test the multilingual models and their ability to transfer knowledge from English to Czech (and vice versa) with zero-shot cross-lingual classification. Our experiments show that the huge multilingual models can overcome the performance of the monolingual models. They are also able to detect polarity in another language without any training data, with performance not worse than 4.4 % compared to state-of-the-art monolingual trained models. Moreover, we achieved new state-of-the-art results on all three datasets.

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Czert – Czech BERT-like Model for Language Representation
Jakub Sido | Ondřej Pražák | Pavel Přibáň | Jan Pašek | Michal Seják | Miloslav Konopík
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

This paper describes the training process of the first Czech monolingual language representation models based on BERT and ALBERT architectures. We pre-train our models on more than 340K of sentences, which is 50 times more than multilingual models that include Czech data. We outperform the multilingual models on 9 out of 11 datasets. In addition, we establish the new state-of-the-art results on nine datasets. At the end, we discuss properties of monolingual and multilingual models based upon our results. We publish all the pre-trained and fine-tuned models freely for the research community.

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Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing
Bogdan Babych | Olga Kanishcheva | Preslav Nakov | Jakub Piskorski | Lidia Pivovarova | Vasyl Starko | Josef Steinberger | Roman Yangarber | Michał Marcińczuk | Senja Pollak | Pavel Přibáň | Marko Robnik-Šikonja
Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing

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Slav-NER: the 3rd Cross-lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across Slavic Languages
Jakub Piskorski | Bogdan Babych | Zara Kancheva | Olga Kanishcheva | Maria Lebedeva | Michał Marcińczuk | Preslav Nakov | Petya Osenova | Lidia Pivovarova | Senja Pollak | Pavel Přibáň | Ivaylo Radev | Marko Robnik-Sikonja | Vasyl Starko | Josef Steinberger | Roman Yangarber
Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing

This paper describes Slav-NER: the 3rd Multilingual Named Entity Challenge in Slavic languages. The tasks involve recognizing mentions of named entities in Web documents, normalization of the names, and cross-lingual linking. The Challenge covers six languages and five entity types, and is organized as part of the 8th Balto-Slavic Natural Language Processing Workshop, co-located with the EACL 2021 Conference. Ten teams participated in the competition. Performance for the named entity recognition task reached 90% F-measure, much higher than reported in the first edition of the Challenge. Seven teams covered all six languages, and five teams participated in the cross-lingual entity linking task. Detailed valuation information is available on the shared task web page.

2020

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UWB at SemEval-2020 Task 1: Lexical Semantic Change Detection
Ondřej Pražák | Pavel Přibáň | Stephen Taylor | Jakub Sido
Proceedings of the Fourteenth Workshop on Semantic Evaluation

In this paper, we describe our method for detection of lexical semantic change, i.e., word sense changes over time. We examine semantic differences between specific words in two corpora, chosen from different time periods, for English, German, Latin, and Swedish. Our method was created for the SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection. We ranked 1st in Sub-task 1: binary change detection, and 4th in Sub-task 2: ranked change detection. We present our method which is completely unsupervised and language independent. It consists of preparing a semantic vector space for each corpus, earlier and later; computing a linear transformation between earlier and later spaces, using Canonical Correlation Analysis and orthogonal transformation;and measuring the cosines between the transformed vector for the target word from the earlier corpus and the vector for the target word in the later corpus.

2019

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The Second Cross-Lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across Slavic Languages
Jakub Piskorski | Laska Laskova | Michał Marcińczuk | Lidia Pivovarova | Pavel Přibáň | Josef Steinberger | Roman Yangarber
Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing

We describe the Second Multilingual Named Entity Challenge in Slavic languages. The task is recognizing mentions of named entities in Web documents, their normalization, and cross-lingual linking. The Challenge was organized as part of the 7th Balto-Slavic Natural Language Processing Workshop, co-located with the ACL-2019 conference. Eight teams participated in the competition, which covered four languages and five entity types. Performance for the named entity recognition task reached 90% F-measure, much higher than reported in the first edition of the Challenge. Seven teams covered all four languages, and five teams participated in the cross-lingual entity linking task. Detailed evaluation information is available on the shared task web page.

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ZCU-NLP at MADAR 2019: Recognizing Arabic Dialects
Pavel Přibáň | Stephen Taylor
Proceedings of the Fourth Arabic Natural Language Processing Workshop

In this paper, we present our systems for the MADAR Shared Task: Arabic Fine-Grained Dialect Identification. The shared task consists of two subtasks. The goal of Subtask– 1 (S-1) is to detect an Arabic city dialect in a given text and the goal of Subtask–2 (S-2) is to predict the country of origin of a Twitter user by using tweets posted by the user. In S-1, our proposed systems are based on language modelling. We use language models to extract features that are later used as an input for other machine learning algorithms. We also experiment with recurrent neural networks (RNN), but these experiments showed that simpler machine learning algorithms are more successful. Our system achieves 0.658 macro F1-score and our rank is 6th out of 19 teams in S-1 and 7th in S-2 with 0.475 macro F1-score.

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Machine Learning Approach to Fact-Checking in West Slavic Languages
Pavel Přibáň | Tomáš Hercig | Josef Steinberger
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

Fake news detection and closely-related fact-checking have recently attracted a lot of attention. Automatization of these tasks has been already studied for English. For other languages, only a few studies can be found (e.g. (Baly et al., 2018)), and to the best of our knowledge, no research has been conducted for West Slavic languages. In this paper, we present datasets for Czech, Polish, and Slovak. We also ran initial experiments which set a baseline for further research into this area.

2018

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UWB at SemEval-2018 Task 1: Emotion Intensity Detection in Tweets
Pavel Přibáň | Tomáš Hercig | Ladislav Lenc
Proceedings of the 12th International Workshop on Semantic Evaluation

This paper describes our system created for the SemEval-2018 Task 1: Affect in Tweets (AIT-2018). We participated in both the regression and the ordinal classification subtasks for emotion intensity detection in English, Arabic, and Spanish. For the regression subtask we use the AffectiveTweets system with added features using various word embeddings, lexicons, and LDA. For the ordinal classification we additionally use our Brainy system with features using parse tree, POS tags, and morphological features. The most beneficial features apart from word and character n-grams include word embeddings, POS count and morphological features.

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UWB at IEST 2018: Emotion Prediction in Tweets with Bidirectional Long Short-Term Memory Neural Network
Pavel Přibáň | Jiří Martínek
Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

This paper describes our system created for the WASSA 2018 Implicit Emotion Shared Task. The goal of this task is to predict the emotion of a given tweet, from which a certain emotion word is removed. The removed word can be sad, happy, disgusted, angry, afraid or a synonym of one of them. Our proposed system is based on deep-learning methods. We use Bidirectional Long Short-Term Memory (BiLSTM) with word embeddings as an input. Pre-trained DeepMoji model and pre-trained emoji2vec emoji embeddings are also used as additional inputs. Our System achieves 0.657 macro F1 score and our rank is 13th out of 30.