Abstract
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.- Anthology ID:
- 2022.lrec-1.148
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 1381–1391
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.148
- DOI:
- Cite (ACL):
- Pavel Přibáň and Josef Steinberger. 2022. Czech Dataset for Cross-lingual Subjectivity Classification. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 1381–1391, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Czech Dataset for Cross-lingual Subjectivity Classification (Přibáň & Steinberger, LREC 2022)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2022.lrec-1.148.pdf
- Code
- pauli31/czech-subjectivity-dataset + additional community code
- Data
- Czech Subjectivity Dataset, SUBJ