  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 further 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 an absolute
percentage reduction of $\approx\mathbf{3-25\%}$ on multiple NLP tasks while
achieving the same performance with no additional computation overhead.
