Polish-ASTE: Aspect-Sentiment Triplet Extraction Datasets for Polish

Marta Lango, Borys Naglik, Mateusz Lango, Iwo Naglik

Abstract
Aspect-Sentiment Triplet Extraction (ASTE) is one of the most challenging and complex tasks in sentiment analysis. It concerns the construction of triplets that contain an aspect, its associated sentiment polarity, and an opinion phrase that serves as a rationale for the assigned polarity. Despite the growing popularity of the task and the many machine learning methods being proposed to address it, the number of datasets for ASTE is very limited. In particular, no dataset is available for any of the Slavic languages. In this paper, we present two new datasets for ASTE containing customer opinions about hotels and purchased products expressed in Polish. We also perform experiments with two ASTE techniques combined with two large language models for Polish to investigate their performance and the difficulty of the assembled datasets. The new datasets are available under a permissive licence and have the same file format as the English datasets, facilitating their use in future research.
Anthology ID:
2024.lrec-main.1122
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
12821–12828
Language:
URL:
https://aclanthology.org/2024.lrec-main.1122
DOI:
Bibkey:
Cite (ACL):
Marta Lango, Borys Naglik, Mateusz Lango, and Iwo Naglik. 2024. Polish-ASTE: Aspect-Sentiment Triplet Extraction Datasets for Polish. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 12821–12828, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Polish-ASTE: Aspect-Sentiment Triplet Extraction Datasets for Polish (Lango et al., LREC-COLING 2024)
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https://preview.aclanthology.org/nschneid-patch-4/2024.lrec-main.1122.pdf
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