Emotion recognition is a critical component of affective computing. Training accurate machine learning models for emotion recognition typically requires a large amount of labeled data. Due to the subtleness and complexity of emotions, multiple evaluators are usually needed for each affective sample to obtain its ground-truth label, which is expensive. To save the labeling cost, this paper proposes an inconsistency-based active learning approach for cross-task transfer between emotion classification and estimation. Affective norms are utilized as prior knowledge to connect the label spaces of categorical and dimensional emotions. Then, the prediction inconsistency on the two tasks for the unlabeled samples is used to guide sample selection in active learning for the target task. Experiments on within-corpus and cross-corpus transfers demonstrated that cross-task inconsistency could be a very valuable metric in active learning. To our knowledge, this is the first work that utilizes prior knowledge on affective norms and data in a different task to facilitate active learning for a new task, even the two tasks are from different datasets.

翻译：情感识别是情感计算的关键组成部分。训练准确的情感识别机器学习模型通常需要大量标注数据。由于情感的微妙性与复杂性，每个情感样本通常需要多名评估者才能获得其真实标签，这一过程成本高昂。为降低标注成本，本文提出一种基于不一致性的主动学习方法，用于情感分类与情感维度估计之间的跨任务迁移。研究利用情感规范作为先验知识，连接分类情感与维度情感两种标签空间。随后，通过未标注样本在两个任务上的预测不一致性，指导目标任务的主动学习样本选择。在语料库内与跨语料库迁移实验表明，跨任务不一致性可作为主动学习中极具价值的度量指标。据我们所知，本研究首次利用不同任务中的情感规范先验知识与数据，促进新任务的主动学习——即使两个任务来自不同数据集。