{"type":"ScholarlyArticle","title":"Explainable Text Classification via Attentive and Targeted Mixing Data Augmentation","authors":[{"name":"Songhao Jiang","slug":"songhao-jiang","given":"Songhao","family":"Jiang","affiliation":"Institute of Information Engineering , Chinese Academy of Sciences; School of Cyber Security , University of Chinese Academy of Sciences; CNCERT/CC"},{"name":"Yan Chu","slug":"yan-chu","given":"Yan","family":"Chu","affiliation":"Harbin Engineering University"},{"name":"Zhengkui Wang","slug":"zhengkui-wang","given":"Zhengkui","family":"Wang","affiliation":"InfoComm Technology Cluster , Singapore Institute of Technology"},{"name":"Tianxing Ma","slug":"tianxing-ma","given":"Tianxing","family":"Ma","affiliation":"Institute of Information Engineering , Chinese Academy of Sciences; School of Cyber Security , University of Chinese Academy of Sciences"},{"name":"Hanlin Wang","slug":"hanlin-wang","given":"Hanlin","family":"Wang","affiliation":"Harbin Engineering University"},{"name":"Wenxuan Lu","slug":"wenxuan-lu","given":"Wenxuan","family":"Lu","affiliation":"Institute of Information Engineering , Chinese Academy of Sciences; School of Cyber Security , University of Chinese Academy of Sciences"},{"name":"Tianning Zang","slug":"tianning-zang","given":"Tianning","family":"Zang","affiliation":"Institute of Information Engineering , Chinese Academy of Sciences; School of Cyber Security , University of Chinese Academy of Sciences"},{"name":"Bo Wang","slug":"bo-wang","given":"Bo","family":"Wang","affiliation":"CNCERT/CC"}],"abstract":"Mixing data augmentation methods have been widely used in text classification recently. However, existing methods do not control the quality of augmented data and have low model explainability. To tackle these issues, this paper proposes an explainable text classification solution based on attentive and targeted mixing data augmentation, ATMIX. Instead of selecting data for augmentation without control, ATMIX focuses on the misclassified training samples as the target for augmentation to better improve the model's capability. Meanwhile, to generate meaningful augmented samples, it adopts a self-attention mechanism to understand the importance of the subsentences in a text, and cut and mix the subsentences between the misclassified and correctly classified samples wisely. Furthermore, it employs a novel dynamic augmented data selection framework based on the loss function gradient to dynamically optimize the augmented samples for model training. In the end, we develop a new model explainability evaluation method based on subsentence attention and conduct extensive evaluations over multiple real-world text datasets. The results indicate that ATMIX is more effective with higher explainability than the typical classification models, hidden-level, and input-level mixup models.","doi":"10.24963/ijcai.2023/565","url":"https://ijcai.org/proceedings/2023/565","pdf":"https://ijcai.org/proceedings/2023/0565.pdf","markdown":"https://ijcai.org/proceedings/2023/565.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/565","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Natural Language Processing","track":"Main Track","number":565,"pages":{"first":5085,"last":5094},"keywords":["Natural Language Processing: NLP: Sentiment analysis, stylistic analysis, and argument mining","Natural Language Processing: NLP: Text classification","Natural Language Processing: NLP: Tools"],"videos":[],"references":[{"position":1,"raw":"[Chen and Ji, 2019] Hanjie Chen and Yangfeng Ji. 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