# Czech Dataset for Cross-lingual Subjectivity Classification

arXiv: https://arxiv.org/abs/2204.13915 · HTML: https://mlsanity.com/paper/2204.13915/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 43 references that are in the map.
- First posted on arXiv 29 Apr 2022.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems](https://mlsanity.com/paper/1905.00537/) (2019)
- [XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization](https://mlsanity.com/paper/2003.11080/) (2020)
- [Entailment as Few-Shot Learner](https://mlsanity.com/paper/2104.14690/) (2021)
- [FLEX: Unifying Evaluation for Few-Shot NLP](https://mlsanity.com/paper/2107.07170/) (2021)
- [RobeCzech: Czech RoBERTa, a monolingual contextualized language representation model](https://mlsanity.com/paper/2105.11314/) (2021)
- [Czert -- Czech BERT-like Model for Language Representation](https://mlsanity.com/paper/2103.13031/) (2021)
- [A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors](https://mlsanity.com/paper/1805.05388/) (2018)
- [Self-Adaptive Hierarchical Sentence Model](https://mlsanity.com/paper/1504.05070/) (2015)
- [Are the Multilingual Models Better? Improving Czech Sentiment with Transformers](https://mlsanity.com/paper/2108.10640/) (2021)
- [Siamese BERT-based Model for Web Search Relevance Ranking Evaluated on a New Czech Dataset](https://mlsanity.com/paper/2112.01810/) (2021)
- [Translations as Additional Contexts for Sentence Classification](https://mlsanity.com/paper/1806.05516/) (2018)

## Built on by (most cited first, 5 of 5)

- [BenCzechMark : A Czech-centric Multitask and Multimetric Benchmark for Large Language Models with Duel Scoring Mechanism](https://mlsanity.com/paper/2412.17933/) (2024)
- [Improving Aspect-Based Sentiment with End-to-End Semantic Role Labeling Model](https://mlsanity.com/paper/2307.14785/) (2023)
- [The Skipped Beat: A Study of Sociopragmatic Understanding in LLMs for 64 Languages](https://mlsanity.com/paper/2310.14557/) (2023)
- [Prompt-Based Approach for Czech Sentiment Analysis](https://mlsanity.com/paper/2508.08651/) (2025)
- [Creation of the Estonian Subjectivity Dataset: Assessing the Degree of Subjectivity on a Scale](https://mlsanity.com/paper/2512.09634/) (2025)

## Abstract (arXiv)

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 kappa 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.