Abstract
Active learning has been widely used in the task of text classification for its ability to select the most valuable samples to annotate while improving the model performance. However, the efficiency of active learning in multi-label text classification tasks has been under-explored due to the label imbalanceness problem. In this paper, we conduct an empirical study of active learning on multi-label text classification and evaluate the efficiency of five active learning strategies on six multi-label text classification tasks. The experiments show that some strategies in the single-label setting especially in imbalanced datasets.- Anthology ID:
- 2023.insights-1.12
- Volume:
- Proceedings of the Fourth Workshop on Insights from Negative Results in NLP
- Month:
- May
- Year:
- 2023
- Address:
- Dubrovnik, Croatia
- Editors:
- Shabnam Tafreshi, Arjun Akula, João Sedoc, Aleksandr Drozd, Anna Rogers, Anna Rumshisky
- Venues:
- insights | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 94–102
- Language:
- URL:
- https://aclanthology.org/2023.insights-1.12
- DOI:
- 10.18653/v1/2023.insights-1.12
- Cite (ACL):
- Mengqi Wang and Ming Liu. 2023. An Empirical Study on Active Learning for Multi-label Text Classification. In Proceedings of the Fourth Workshop on Insights from Negative Results in NLP, pages 94–102, Dubrovnik, Croatia. Association for Computational Linguistics.
- Cite (Informal):
- An Empirical Study on Active Learning for Multi-label Text Classification (Wang & Liu, insights-WS 2023)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.insights-1.12.pdf