Learning to Learn and Predict: A Meta-Learning Approach for Multi-Label Classification

Jiawei Wu, Wenhan Xiong, William Yang Wang

Abstract
Many tasks in natural language processing can be viewed as multi-label classification problems. However, most of the existing models are trained with the standard cross-entropy loss function and use a fixed prediction policy (e.g., a threshold of 0.5) for all the labels, which completely ignores the complexity and dependencies among different labels. In this paper, we propose a meta-learning method to capture these complex label dependencies. More specifically, our method utilizes a meta-learner to jointly learn the training policies and prediction policies for different labels. The training policies are then used to train the classifier with the cross-entropy loss function, and the prediction policies are further implemented for prediction. Experimental results on fine-grained entity typing and text classification demonstrate that our proposed method can obtain more accurate multi-label classification results.
Anthology ID:
D19-1444
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
4354–4364
Language:
URL:
https://aclanthology.org/D19-1444
DOI:
10.18653/v1/D19-1444
Bibkey:
Cite (ACL):
Jiawei Wu, Wenhan Xiong, and William Yang Wang. 2019. Learning to Learn and Predict: A Meta-Learning Approach for Multi-Label Classification. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 4354–4364, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Learning to Learn and Predict: A Meta-Learning Approach for Multi-Label Classification (Wu et al., EMNLP-IJCNLP 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/D19-1444.pdf
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