Papers by Scott Allan Orr
BERT-Flow-VAE: A Weakly-supervised Model for Multi-Label Text Classification (2022.coling-1)
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| Challenge: | Multi-label Text Classification (MLTC) is a task of categorizing documents into one or more topics. Fully-supervised learning methods are undesirable for this task because of the diversity of domains of application and cost of manual labelling. |
| Approach: | They propose a Weakly-Supervised Multi-Label Text Classification model that produces BERT sentence embeddings and calibrates them using a flow model. |
| Outcome: | The proposed model outperforms baseline models in key metrics and achieves 84% performance on multi-label datasets. |