Papers with multi-label scenarios
Cross Encoding as Augmentation: Towards Effective Educational Text Classification (2023.findings-acl)
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Hyun Seung Lee, Seungtaek Choi, Yunsung Lee, Hyeongdon Moon, Shinhyeok Oh, Myeongho Jeong, Hyojun Go, Christian Wallraven
| Challenge: | Existing methods to improve text classification in education suffer from data scarcity . authors propose a retrieval approach that provides effective learning in educational text classification. |
| Approach: | They propose a retrieval approach that provides effective learning in educational text classification by introducing cross-encoder style texts to a bi-encoding architecture. |
| Outcome: | The proposed method is effective in multi-label scenarios and low-resource tags compared to state-of-the-art models. |
Establishing Annotation Quality in Multi-label Annotations (2022.coling-1)
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| Challenge: | Multi-label annotations allow multiple interpretations of a single item, but they also affect the chance that two coders agree with each other. |
| Approach: | They propose a bootstrapped method to obtain chance agreement for each measure and a method to get an adjusted agreement coefficient that is more interpretable. |
| Outcome: | The proposed method allows for an adjusted agreement coefficient that is more interpretable on simulated datasets. |