Learning how to Active Learn: A Deep Reinforcement Learning Approach

Meng Fang, Yuan Li, Trevor Cohn


Abstract
Active learning aims to select a small subset of data for annotation such that a classifier learned on the data is highly accurate. This is usually done using heuristic selection methods, however the effectiveness of such methods is limited and moreover, the performance of heuristics varies between datasets. To address these shortcomings, we introduce a novel formulation by reframing the active learning as a reinforcement learning problem and explicitly learning a data selection policy, where the policy takes the role of the active learning heuristic. Importantly, our method allows the selection policy learned using simulation to one language to be transferred to other languages. We demonstrate our method using cross-lingual named entity recognition, observing uniform improvements over traditional active learning algorithms.
Anthology ID:
D17-1063
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
595–605
URL:
https://www.aclweb.org/anthology/D17-1063
DOI:
10.18653/v1/D17-1063
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PDF:
https://www.aclweb.org/anthology/D17-1063.pdf
Video:
 https://vimeo.com/238234005