Learning how to Active Learn: A Deep Reinforcement Learning Approach Learning how to Active Learn: A Deep Reinforcement Learning Approach
Paper summary Active learning (choosing which examples to annotate for training) is proposed as a reinforcement learning problem. The Q-learning network predicts for each sentence whether it should be annotated, and is trained based on the performance improvement from the main task. Evaluation is done on NER, with experiments on transferring the trained Q-learning function to other languages. https://i.imgur.com/5rXm5vZ.png
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Learning how to Active Learn: A Deep Reinforcement Learning Approach
Fang, Meng and Li, Yuan and Cohn, Trevor
Empirical Methods on Natural Language Processing (EMNLP) - 2017 via Local Bibsonomy
Keywords: dblp


Summary by Marek Rei 2 months ago
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