  While deep learning is a powerful tool for natural language processing (NLP)
problems, successful solutions to these problems rely heavily on large amounts
of annotated samples. However, manually annotating data is expensive and
time-consuming. Active Learning (AL) strategies reduce the need for huge
volumes of labeled data by iteratively selecting a small number of examples for
manual annotation based on their estimated utility in training the given model.
In this paper, we argue that since AL strategies choose examples independently,
they may potentially select similar examples, all of which may not contribute
significantly to the learning process. Our proposed approach,
Active$\mathbf{^2}$ Learning (A$\mathbf{^2}$L), actively adapts to the deep
learning model being trained to eliminate such redundant examples chosen by an
AL strategy. We show that A$\mathbf{^2}$L is widely applicable by using it in
conjunction with several different AL strategies and NLP tasks. We empirically
demonstrate that the proposed approach is further able to reduce the data
requirements of state-of-the-art AL strategies by $\approx \mathbf{3-25\%}$ on
an absolute scale on multiple NLP tasks while achieving the same performance
with virtually no additional computation overhead.
