| Challenge: | Traditional text classifiers are limited to predicting over a fixed set of labels, but real-world applications require dynamic classification. |
| Approach: | They propose to replace the traditional fixed-size output layer with a learned metric space . they propose to add or remove support points in the metric and fine-tune the resulting metric . |
| Outcome: | The proposed method is robust to changes in the label space and improves performance in low data regime. |
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| Challenge: | Existing methods for few-shot text classification often encounter problems drawing accurate class prototypes from support set samples. |
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| Challenge: | Few-shot text classification systems are infeasible to deploy and use reliably due to their dependence on prompting and billion-parameter language models. |
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Knowledge Guided Metric Learning for Few-Shot Text Classification (2021.naacl-main)
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| Challenge: | Existing methods to learn from limited examples are insufficient for many-shot text classification tasks. |
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Diverse Few-Shot Text Classification with Multiple Metrics (N18-1)
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Mo Yu, Xiaoxiao Guo, Jinfeng Yi, Shiyu Chang, Saloni Potdar, Yu Cheng, Gerald Tesauro, Haoyu Wang, Bowen Zhou
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Joint Embedding of Words and Labels for Text Classification (P18-1)
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Guoyin Wang, Chunyuan Li, Wenlin Wang, Yizhe Zhang, Dinghan Shen, Xinyuan Zhang, Ricardo Henao, Lawrence Carin
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From Text Segmentation to Enhanced Representation Learning: A Novel Approach to Multi-Label Classification for Long Texts (2024.findings-emnlp)
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Fusing Label Embedding into BERT: An Efficient Improvement for Text Classification (2021.findings-acl)
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| Challenge: | Existing methods to improve text classification performance of pre-trained models have been used to improve their performance. |
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Robust Text Classifier on Test-Time Budgets (D19-1)
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Feature Projection for Improved Text Classification (2020.acl-main)
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| Challenge: | In sentiment classification, there are some good features that are indicative of class labels, but there are also many common features that do not discriminate for classification. |
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