  Active learning (AL), which aims to construct an effective training set by
iteratively curating the most formative unlabeled data for annotation, has been
widely used in low-resource tasks. Most active learning techniques in
classification rely on the model's uncertainty or disagreement to choose
unlabeled data, suffering from the problem of over-confidence in superficial
patterns and a lack of exploration. Inspired by the cognitive processes in
which humans deduce and predict through causal information, we take an initial
attempt towards integrating rationales into AL and propose a novel Explainable
Active Learning framework (XAL) for low-resource text classification, which
aims to encourage classifiers to justify their inferences and delve into
unlabeled data for which they cannot provide reasonable explanations.
Specifically, besides using a pre-trained bi-directional encoder for
classification, we employ a pre-trained uni-directional decoder to generate and
score the explanation. We further facilitate the alignment of the model with
human reasoning preference through a proposed ranking loss. During the
selection of unlabeled data, the predicted uncertainty of the encoder and the
explanation score of the decoder complement each other as the final metric to
acquire informative data. Extensive experiments on six datasets show that XAL
achieves consistent improvement over 9 strong baselines. Analysis indicates
that the proposed method can generate corresponding explanations for its
predictions.
