Abstract
We propose a novel solution for assigning labels to topic models by using multiple weak labelers. The method leverages generative transformers to learn accurate representations of the most important topic terms and candidate labels. This is achieved by fine-tuning pre-trained BART models on a large number of potential labels generated by state of the art non-neural models for topic labeling, enriched with different techniques. The proposed BART-TL model is able to generate valuable and novel labels in a weakly-supervised manner and can be improved by adding other weak labelers or distant supervision on similar tasks.- Anthology ID:
- 2021.eacl-main.121
- Volume:
- Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1418–1425
- Language:
- URL:
- https://aclanthology.org/2021.eacl-main.121
- DOI:
- 10.18653/v1/2021.eacl-main.121
- Cite (ACL):
- Cristian Popa and Traian Rebedea. 2021. BART-TL: Weakly-Supervised Topic Label Generation. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 1418–1425, Online. Association for Computational Linguistics.
- Cite (Informal):
- BART-TL: Weakly-Supervised Topic Label Generation (Popa & Rebedea, EACL 2021)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2021.eacl-main.121.pdf