Evaluating Pre-Trained Sentence-BERT with Class Embeddings in Active Learning for Multi-Label Text Classification
Lukas Wertz, Jasmina Bogojeska, Katsiaryna Mirylenka, Jonas Kuhn
Abstract
The Transformer Language Model is a powerful tool that has been shown to excel at various NLP tasks and has become the de-facto standard solution thanks to its versatility. In this study, we employ pre-trained document embeddings in an Active Learning task to group samples with the same labels in the embedding space on a legal document corpus. We find that the calculated class embeddings are not close to the respective samples and consequently do not partition the embedding space in a meaningful way. In addition, we explore using the class embeddings as an Active Learning strategy with dramatically reduced results compared to all baselines.- Anthology ID:
- 2022.aacl-short.45
- Volume:
- Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
- Month:
- November
- Year:
- 2022
- Address:
- Online only
- Editors:
- Yulan He, Heng Ji, Sujian Li, Yang Liu, Chua-Hui Chang
- Venues:
- AACL | IJCNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 366–372
- Language:
- URL:
- https://aclanthology.org/2022.aacl-short.45
- DOI:
- Cite (ACL):
- Lukas Wertz, Jasmina Bogojeska, Katsiaryna Mirylenka, and Jonas Kuhn. 2022. Evaluating Pre-Trained Sentence-BERT with Class Embeddings in Active Learning for Multi-Label Text Classification. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 366–372, Online only. Association for Computational Linguistics.
- Cite (Informal):
- Evaluating Pre-Trained Sentence-BERT with Class Embeddings in Active Learning for Multi-Label Text Classification (Wertz et al., AACL-IJCNLP 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2022.aacl-short.45.pdf