{"type":"ScholarlyArticle","title":"Adversarial Active Learning for Sequences Labeling and Generation","authors":[{"name":"Yue Deng","slug":"yue-deng","given":"Yue","family":"Deng","affiliation":"AI Center , Samsung Research America , Mountain View , CA , USA"},{"name":"KaWai Chen","slug":"kawai-chen","given":"KaWai","family":"Chen","affiliation":"Department of Electrical and Computer Engineering , University of California , San Diego"},{"name":"Yilin Shen","slug":"yilin-shen","given":"Yilin","family":"Shen","affiliation":"AI Center , Samsung Research America , Mountain View , CA , USA"},{"name":"Hongxia Jin","slug":"hongxia-jin","given":"Hongxia","family":"Jin","affiliation":"AI Center , Samsung Research America , Mountain View , CA , USA"}],"abstract":"We introduce an active learning framework for general sequence learning tasks including sequence labeling and generation. Most existing active learning algorithms mainly rely on an uncertainty measure derived from the probabilistic classifier for query sample selection. However, such approaches suffer from two shortcomings in the context of sequence learning including 1) cold start problem and 2) label sampling dilemma. To overcome these shortcomings, we propose a deep-learning-based active learning framework to directly identify query samples from the perspective of adversarial learning.  Our approach intends to offer labeling  priorities for sequences whose information content are least covered by existing labeled data. We verify our sequence-based active learning approach  on two tasks including sequence labeling and sequence generation.","doi":"10.24963/ijcai.2018/558","url":"https://ijcai.org/proceedings/2018/558","pdf":"https://ijcai.org/proceedings/2018/0558.pdf","markdown":"https://ijcai.org/proceedings/2018/558.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/558","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Natural Language Processing","track":"Main track","number":558,"pages":{"first":4012,"last":4018},"keywords":["Machine Learning: Deep Learning","Natural Language Processing: Natural Language Processing","Machine Learning: Active Learning"],"videos":[],"references":[{"position":1,"raw":"[Bao et al., 2017] Feng Bao, Yue Deng, Mulong Du, Zhi- quan Ren, Qingzhao Zhang, Yanyu Zhao, Jinli Suo, Zhengdong Zhang, Meilin Wang, and Qionghai Dai. 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