{"type":"ScholarlyArticle","title":"MEDA: Meta-Learning with Data Augmentation for Few-Shot Text Classification","authors":[{"name":"Pengfei Sun","slug":"pengfei-sun","given":"Pengfei","family":"Sun","affiliation":""},{"name":"Yawen Ouyang","slug":"yawen-ouyang","given":"Yawen","family":"Ouyang","affiliation":""},{"name":"Wenming Zhang","slug":"wenming-zhang","given":"Wenming","family":"Zhang","affiliation":""},{"name":"Xin-yu Dai","slug":"xin-yu-dai","given":"Xin-Yu","family":"Dai","affiliation":""}],"abstract":"Meta-learning has recently emerged as a promising technique to address the challenge of few-shot learning. However, standard meta-learning methods mainly focus on visual tasks, which makes it hard for them to deal with diverse text data directly. In this paper, we introduce a novel framework for few-shot text classification, which is named as MEta-learning with Data Augmentation (MEDA). MEDA is composed of two modules, a ball generator and a meta-learner, which are learned jointly. The ball generator is to increase the number of shots per class by generating more samples, so that meta-learner can be trained with both original and augmented samples. It is worth noting that ball generator is agnostic to the choice of the meta-learning methods. Experiment results show that on both datasets, MEDA outperforms existing state-of-the-art methods and significantly improves the performance of meta-learning on few-shot text classification.","doi":"10.24963/ijcai.2021/541","url":"https://ijcai.org/proceedings/2021/541","pdf":"https://ijcai.org/proceedings/2021/0541.pdf","markdown":"https://ijcai.org/proceedings/2021/541.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/541","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Natural Language Processing","track":"Main Track","number":541,"pages":{"first":3929,"last":3935},"keywords":["Natural Language Processing: Text Classification","Natural Language Processing: Natural Language Processing"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}