Adversarial active learning for sequence labeling and generation

38Citations
Citations of this article
76Readers
Mendeley users who have this article in their library.

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.

Cite

CITATION STYLE

APA

Deng, Y., Chen, K., Shen, Y., & Jin, H. (2018). Adversarial active learning for sequence labeling and generation. In IJCAI International Joint Conference on Artificial Intelligence (Vol. 2018-July, pp. 4012–4018). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2018/558

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free