Abstract
Few-shot methods for accurate modeling under sparse label-settings have improved significantly. However, the applications of few-shot modeling in natural language processing remain solely in the field of document classification. With recent performance improvements, supervised few-shot methods, combined with a simple topic extraction method pose a significant challenge to unsupervised topic modeling methods. Our research shows that supervised few-shot learning, combined with a simple topic extraction method, can outperform unsupervised topic modeling techniques in terms of generating coherent topics, even when only a few labeled documents per class are used. The code is available at the following link: https://github.com/AnFreTh/STREAM- Anthology ID:
- 2024.lrec-main.736
- 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:
- 8395–8405
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.736
- DOI:
- Cite (ACL):
- Anton F. Thielmann, Christoph Weisser, and Benjamin Säfken. 2024. Human in the Loop: How to Effectively Create Coherent Topics by Manually Labeling Only a Few Documents per Class. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 8395–8405, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Human in the Loop: How to Effectively Create Coherent Topics by Manually Labeling Only a Few Documents per Class (Thielmann et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2024.lrec-main.736.pdf