Abstract
This paper introduces the first prompt-based methods for aspect-based sentiment analysis and sentiment classification in Czech. We employ the sequence-to-sequence models to solve the aspect-based tasks simultaneously and demonstrate the superiority of our prompt-based approach over traditional fine-tuning. In addition, we conduct zero-shot and few-shot learning experiments for sentiment classification and show that prompting yields significantly better results with limited training examples compared to traditional fine-tuning. We also demonstrate that pre-training on data from the target domain can lead to significant improvements in a zero-shot scenario.- Anthology ID:
- 2023.ranlp-1.118
- Volume:
- Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
- Month:
- September
- Year:
- 2023
- Address:
- Varna, Bulgaria
- Editors:
- Ruslan Mitkov, Galia Angelova
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd., Shoumen, Bulgaria
- Note:
- Pages:
- 1110–1120
- Language:
- URL:
- https://aclanthology.org/2023.ranlp-1.118
- DOI:
- Cite (ACL):
- Jakub Šmíd and Pavel Přibáň. 2023. Prompt-Based Approach for Czech Sentiment Analysis. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pages 1110–1120, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.
- Cite (Informal):
- Prompt-Based Approach for Czech Sentiment Analysis (Šmíd & Přibáň, RANLP 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2023.ranlp-1.118.pdf