利用情绪不一致性挑选样本，降低情感识别标注成本
Cross-Task Inconsistency Based Active Learning (CTIAL) for Emotion Recognition
- 跨任务用情感规范连接分类与维度情绪标签空间
- 未标注样本在两任务预测不一致程度高者优先标注
- 适用于标注昂贵的情感计算场景，尤其跨数据集迁移
情感识别是情感计算的关键。训练精准的机器学习模型通常需要大量标注数据。由于情绪微妙复杂，每个情感样本常需多位评估者确定真实标签，成本高昂。本文提出一种基于不一致性的主动学习方法，用于情绪分类与估计间的跨任务迁移。利用情感规范作为先验知识，连接类别型与维度型情绪的标签空间；通过未标注样本在两个任务上的预测不一致性，指导目标任务的样本选择。在同语料库与跨语料库迁移实验中均表明，跨任务不一致性可成为主动学习中极有价值的选样指标。据我们所知，这是首个将情感规范及异任务数据作为先验知识，以促进新任务主动学习的工作，即使两任务来自不同数据集。
原文摘要 · Abstract (English)
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.
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