Title: Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study,
Abstract: Several recent papers investigate Active Learning (AL) for mitigating the
data dependence of deep learning for natural language processing. However, the
applicability of AL to real-world problems remains an open question. While in
supervised learning, practitioners can try many different methods, evaluating
each against a validation set before selecting a model, AL affords no such
luxury. Over the course of one AL run, an agent annotates its dataset
exhausting its labeling budget. Thus, given a new task, an active learner has
no opportunity to compare models and acquisition functions. This paper provides
a large scale empirical study of deep active learning, addressing multiple
tasks and, for each, multiple datasets, multiple models, and a full suite of
acquisition functions. We find that across all settings, Bayesian active
learning by disagreement, using uncertainty estimates provided either by
Dropout or Bayes-by Backprop significantly improves over i.i.d. baselines and
usually outperforms classic uncertainty sampling.