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.

中文翻译：

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用于情绪识别的基于跨任务不一致的主动学习（CTIAL）


情感识别是情感计算的关键组成部分。训练准确的机器学习模型进行情感识别通常需要大量标记数据。由于情感的微妙性和复杂性，每个情感样本通常需要多个评估者才能获得其真实标签，这是昂贵的。为了节省标记成本，本文提出了一种基于不一致性的主动学习方法，用于情感分类和估计之间的跨任务迁移。情感规范被用作先验知识来连接类别情感和维度情感的标签空间。然后，利用未标记样本对两个任务的预测不一致来指导目标任务的主动学习中的样本选择。语料库内和跨语料库迁移的实验表明，跨任务不一致可能是主动学习中非常有价值的指标。据我们所知，这是第一个利用不同任务中的情感规范和数据的先验知识来促进新任务的主动学习的工作，即使这两个任务来自不同的数据集。