Coarse2Fine: Fine-grained Text Classification on Coarsely-grained Annotated Data

• URL: http://arxiv.org/abs/2109.10856v1
• Date: Wed, 22 Sep 2021 17:29:01 GMT
• Title: Coarse2Fine: Fine-grained Text Classification on Coarsely-grained Annotated Data
• Authors: Dheeraj Mekala, Varun Gangal, Jingbo Shang
• Abstract summary: We introduce a new problem called coarse-to-fine grained classification, which aims to perform fine-grained classification on coarsely annotated data. Instead of asking for new fine-grained human annotations, we opt to leverage label surface names as the only human guidance. Our framework uses the fine-tuned generative models to sample pseudo-training data for training the classifier, and bootstraps on real unlabeled data for model refinement.
• Score: 22.81068960545234
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing text classification methods mainly focus on a fixed label set, whereas many real-world applications require extending to new fine-grained classes as the number of samples per label increases. To accommodate such requirements, we introduce a new problem called coarse-to-fine grained classification, which aims to perform fine-grained classification on coarsely annotated data. Instead of asking for new fine-grained human annotations, we opt to leverage label surface names as the only human guidance and weave in rich pre-trained generative language models into the iterative weak supervision strategy. Specifically, we first propose a label-conditioned finetuning formulation to attune these generators for our task. Furthermore, we devise a regularization objective based on the coarse-fine label constraints derived from our problem setting, giving us even further improvements over the prior formulation. Our framework uses the fine-tuned generative models to sample pseudo-training data for training the classifier, and bootstraps on real unlabeled data for model refinement. Extensive experiments and case studies on two real-world datasets demonstrate superior performance over SOTA zero-shot classification baselines.

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