Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation

• URL: http://arxiv.org/abs/2201.11576v1
• Date: Thu, 27 Jan 2022 15:29:30 GMT
• Title: Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation
• Authors: Jixuan Wang, Kuan-Chieh Wang, Frank Rudzicz, Michael Brudno
• Abstract summary: We propose a novel conditional neural process-based approach for few-shot text classification. Our key idea is to represent each task using gradient information from a base model. Our approach outperforms traditional fine-tuning, sequential transfer learning, and state-of-the-art meta learning approaches.
• Score: 24.488427641442694
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large pretrained language models (LMs) like BERT have improved performance in many disparate natural language processing (NLP) tasks. However, fine tuning such models requires a large number of training examples for each target task. Simultaneously, many realistic NLP problems are "few shot", without a sufficiently large training set. In this work, we propose a novel conditional neural process-based approach for few-shot text classification that learns to transfer from other diverse tasks with rich annotation. Our key idea is to represent each task using gradient information from a base model and to train an adaptation network that modulates a text classifier conditioned on the task representation. While previous task-aware few-shot learners represent tasks by input encoding, our novel task representation is more powerful, as the gradient captures input-output relationships of a task. Experimental results show that our approach outperforms traditional fine-tuning, sequential transfer learning, and state-of-the-art meta learning approaches on a collection of diverse few-shot tasks. We further conducted analysis and ablations to justify our design choices.

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