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.

翻译：主动学习（AL）旨在通过迭代筛选最具信息价值的未标注数据进行标注，以构建高效的训练集，已在低资源任务中得到广泛应用。现有分类任务中的主动学习方法通常依赖模型的不确定性或分歧度选择未标注数据，存在对表层模式过度自信、缺乏探索性的问题。受人类通过因果信息进行推理和预测的认知过程启发，我们首次将解释机制引入主动学习，提出面向低资源文本分类的可解释主动学习框架（XAL）。该框架旨在促使分类器验证自身推理依据，并深入挖掘无法提供合理解释的未标注数据。具体而言，除采用预训练双向编码器进行分类外，我们引入预训练单向解码器生成并评估解释文本。通过设计的排序损失函数，进一步推动模型与人类推理偏好对齐。在未标注数据筛选阶段，编码器的预测不确定度与解码器的解释评分相互补充，形成最终的信息量度量指标。在六个数据集上的实验表明，XAL在九个强基线方法上实现了一致性提升。分析结果表明，本方法能够为其预测结果生成相应的解释。