Papers with multi-label models
Hierarchical Transfer Learning for Multi-label Text Classification (P19-1)
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| Challenge: | Multi-Label Hierarchical Text Classification (MLHTC) is a task of categorizing documents into one or more topics organized in an hierarchical taxonomy. |
| Approach: | They propose a transfer learning based strategy where binary classifiers at lower levels are initialized using parameters of the parent classifier and fine-tuned on the child category classification task. |
| Outcome: | The proposed method improves on micro-F1 and macro-F1, respectively, compared to binary classifiers trained from scratch. |
Trigger Warning Assignment as a Multi-Label Document Classification Problem (2023.acl-long)
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| Challenge: | a trigger warning is used to warn people about potentially disturbing content . a webis dataset of 1 million fanfiction works contains up to 36 different warnings per document . |
| Approach: | They introduce a multi-label task to assign a trigger warning to fanfiction . they map 41 million free-form tags assigned by authors into a single taxonomy of trigger warnings . |
| Outcome: | The proposed model achieves micro-F1 scores of about 0.5, which reveals the difficulty of the task. |