Improve Meta-learning for Few-Shot Text Classification with All You Can Acquire from the Tasks (2024)

Abstract: Meta-learning has emerged as a prominent technology for few-shot text classification and has achieved promising performance. However, existing methods often encounter difficulties in drawing accurate class prototypes from support set samples, primarily due to probable large intra-class differences and small inter-class differences within the task. Recent approaches attempt to incorporate external knowledge or pre-trained language models to augment data, but this requires additional resources and thus does not suit many few-shot scenarios. In this paper, we propose a novel solution to address this issue by adequately leveraging the information within the task itself. Specifically, we utilize label information to construct a task-adaptive metric space, thereby adaptively reducing the intra-class differences and magnifying the inter-class differences. We further employ the optimal transport technique to estimate class prototypes with query set samples together, mitigating the problem of inaccurate and ambiguous support set samples caused by large intra-class differences. We conduct extensive experiments on eight benchmark datasets, and our approach shows obvious advantages over state-of-the-art models across all the tasks on all the datasets. For reproducibility, all the datasets and codes are available at https://github.com/YvoGao/LAQDA.

## introduction text classification is a fundamental task in the natural language processing (nlp) theme, which has been widely applied to various real applications. however, a deficiency of supervised data is often experienced in the real world. few-shot text classification @xcite aims to detect novel categories with very limited labeled examples by using knowledge learned from known categories, which is crucial for many applications but remains to be a challenging task. ## conclusion we propose a meta-learning method called laqda for few-shot text classification, fully utilizing the information within the task to mitigate the overfitting issue caused by a limited number of labeled samples. specifically, we propose two key modules: label-adapter uses label information to construct a task-adaptive metric space that generates intra-class closer and inter-class differences larger sample representations. query-data-augmenter leverages the query samples to calculate the class prototypes, mitigating the problem of inaccurate prototypes caused by the randomness of the sampled support sets and intra-class differences. last but not least, we merely use the information from the task itself instead of introducing external knowledge or llms. extensive experiments are conducted on eight benchmark datasets, and our approach outperforms the state-of-the-art methods.