Abstract
The Human Value Detection shared task\cite{kiesel:2023} aims to classify whether or not the argument draws on a set of 20 value categories, given a textual argument. This is a difficult task as the discrimination of human values behind arguments is often implicit. Moreover, the number of label categories can be up to 20 and the distribution of data is highly imbalanced. To address these issues, we employ a multi-label classification model and utilize a class-balanced loss function. Our system wins 5 first places, 2 second places, and 6 third places out of 20 categories of the Human Value Detection shared task, and our overall average score of 0.54 also places third. The code is publicly available at \url{https://www.github.com/diqiuzhuanzhuan/semeval2023}.- Anthology ID:
- 2023.semeval-1.34
- Volume:
- Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Giovanni Da San Martino, Harish Tayyar Madabushi, Ritesh Kumar, Elisa Sartori
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 256–261
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.34
- DOI:
- 10.18653/v1/2023.semeval-1.34
- Cite (ACL):
- Long Ma, Zeye Sun, Jiawei Jiang, and Xuan Li. 2023. PAI at SemEval-2023 Task 4: A General Multi-label Classification System with Class-balanced Loss Function and Ensemble Module. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 256–261, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- PAI at SemEval-2023 Task 4: A General Multi-label Classification System with Class-balanced Loss Function and Ensemble Module (Ma et al., SemEval 2023)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/2023.semeval-1.34.pdf