Active learning (AL) is a technique for reducing manual annotation effort during the annotation of training data for machine learning classifiers. For NLP tasks, pool-based and stream-based sampling techniques have been used to select new instances for AL while generating new, artificial instances via Membership Query Synthesis was, up to know, considered to be infeasible for NLP problems. We present the first successful attempt to use Membership Query Synthesis for generating AL queries for natural language processing, using Variational Autoencoders for query generation. We evaluate our approach in a text classification task and demonstrate that query synthesis shows competitive performance to pool-based AL strategies while substantially reducing annotation time.
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