  Recently, Deep Neural Networks (DNNs) have made remarkable progress for text
classification, which, however, still require a large number of labeled data.
To train high-performing models with the minimal annotation cost, active
learning is proposed to select and label the most informative samples, yet it
is still challenging to measure informativeness of samples used in DNNs. In
this paper, inspired by piece-wise linear interpretability of DNNs, we propose
a novel Active Learning with DivErse iNterpretations (ALDEN) approach. With
local interpretations in DNNs, ALDEN identifies linearly separable regions of
samples. Then, it selects samples according to their diversity of local
interpretations and queries their labels. To tackle the text classification
problem, we choose the word with the most diverse interpretations to represent
the whole sentence. Extensive experiments demonstrate that ALDEN consistently
outperforms several state-of-the-art deep active learning methods.
