Abstract
We study the problem of learning an event classifier from human needs category descriptions, which is challenging due to: (1) the use of highly abstract concepts in natural language descriptions, (2) the difficulty of choosing key concepts. To tackle these two challenges, we propose LeaPI, a zero-shot learning method that first automatically generate weak labels by instantiating high-level concepts with prototypical instances and then trains a human needs classifier with the weakly labeled data. To filter noisy concepts, we design a reinforced selection algorithm to choose high-quality concepts for instantiation. Experimental results on the human needs categorization task show that our method outperforms baseline methods, producing substantially better precision.- Anthology ID:
- 2020.findings-emnlp.421
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Trevor Cohn, Yulan He, Yang Liu
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4698–4704
- Language:
- URL:
- https://aclanthology.org/2020.findings-emnlp.421
- DOI:
- 10.18653/v1/2020.findings-emnlp.421
- Cite (ACL):
- Haibo Ding and Zhe Feng. 2020. Learning to Classify Events from Human Needs Category Descriptions. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 4698–4704, Online. Association for Computational Linguistics.
- Cite (Informal):
- Learning to Classify Events from Human Needs Category Descriptions (Ding & Feng, Findings 2020)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2020.findings-emnlp.421.pdf