@inproceedings{lee-etal-2023-cross,
title = "Cross Encoding as Augmentation: Towards Effective Educational Text Classification",
author = "Lee, Hyun Seung and
Choi, Seungtaek and
Lee, Yunsung and
Moon, Hyeongdon and
Oh, Shinhyeok and
Jeong, Myeongho and
Go, Hyojun and
Wallraven, Christian",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.137",
doi = "10.18653/v1/2023.findings-acl.137",
pages = "2184--2195",
abstract = "Text classification in education, usually called auto-tagging, is the automated process of assigning relevant tags to educational content, such as questions and textbooks. However, auto-tagging suffers from a data scarcity problem, which stems from two major challenges: 1) it possesses a large tag space and 2) it is multi-label. Though a retrieval approach is reportedly good at low-resource scenarios, there have been fewer efforts to directly address the data scarcity problem. To mitigate these issues, here we propose a novel retrieval approach CEAA that provides effective learning in educational text classification. Our main contributions are as follows: 1) we leverage transfer learning from question-answering datasets, and 2) we propose a simple but effective data augmentation method introducing cross-encoder style texts to a bi-encoder architecture for more efficient inference. An extensive set of experiments shows that our proposed method is effective in multi-label scenarios and low-resource tags compared to state-of-the-art models.",
}
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<abstract>Text classification in education, usually called auto-tagging, is the automated process of assigning relevant tags to educational content, such as questions and textbooks. However, auto-tagging suffers from a data scarcity problem, which stems from two major challenges: 1) it possesses a large tag space and 2) it is multi-label. Though a retrieval approach is reportedly good at low-resource scenarios, there have been fewer efforts to directly address the data scarcity problem. To mitigate these issues, here we propose a novel retrieval approach CEAA that provides effective learning in educational text classification. Our main contributions are as follows: 1) we leverage transfer learning from question-answering datasets, and 2) we propose a simple but effective data augmentation method introducing cross-encoder style texts to a bi-encoder architecture for more efficient inference. An extensive set of experiments shows that our proposed method is effective in multi-label scenarios and low-resource tags compared to state-of-the-art models.</abstract>
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%0 Conference Proceedings
%T Cross Encoding as Augmentation: Towards Effective Educational Text Classification
%A Lee, Hyun Seung
%A Choi, Seungtaek
%A Lee, Yunsung
%A Moon, Hyeongdon
%A Oh, Shinhyeok
%A Jeong, Myeongho
%A Go, Hyojun
%A Wallraven, Christian
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F lee-etal-2023-cross
%X Text classification in education, usually called auto-tagging, is the automated process of assigning relevant tags to educational content, such as questions and textbooks. However, auto-tagging suffers from a data scarcity problem, which stems from two major challenges: 1) it possesses a large tag space and 2) it is multi-label. Though a retrieval approach is reportedly good at low-resource scenarios, there have been fewer efforts to directly address the data scarcity problem. To mitigate these issues, here we propose a novel retrieval approach CEAA that provides effective learning in educational text classification. Our main contributions are as follows: 1) we leverage transfer learning from question-answering datasets, and 2) we propose a simple but effective data augmentation method introducing cross-encoder style texts to a bi-encoder architecture for more efficient inference. An extensive set of experiments shows that our proposed method is effective in multi-label scenarios and low-resource tags compared to state-of-the-art models.
%R 10.18653/v1/2023.findings-acl.137
%U https://aclanthology.org/2023.findings-acl.137
%U https://doi.org/10.18653/v1/2023.findings-acl.137
%P 2184-2195
Markdown (Informal)
[Cross Encoding as Augmentation: Towards Effective Educational Text Classification](https://aclanthology.org/2023.findings-acl.137) (Lee et al., Findings 2023)
ACL
- Hyun Seung Lee, Seungtaek Choi, Yunsung Lee, Hyeongdon Moon, Shinhyeok Oh, Myeongho Jeong, Hyojun Go, and Christian Wallraven. 2023. Cross Encoding as Augmentation: Towards Effective Educational Text Classification. In Findings of the Association for Computational Linguistics: ACL 2023, pages 2184–2195, Toronto, Canada. Association for Computational Linguistics.