应用合作机器学习加速大型多模态语料库中社会信号的标注
Applying Cooperative Machine Learning to Speed Up the Annotation of Social Signals in Large Multi-modal Corpora
摘要：
科学学科，如行为心理学，人类学和最近的社会信号处理都涉及人类行为的系统探索。典型的工作流程包括在相当大的多模态语料库中对社交信号进行手动注释（也称为编码）。对于所涉及的注释者来说，这是一项令人疲惫和耗时的任务。在手头的文章中，我们提出了一种新的方法，也提供了工具，以加快编码过程。为此，我们建议和评估使用合作机器学习（CML）技术，以减少人工标签的努力相结合的计算能力和人类智慧的力量。建议的CML策略从少量的标记实例开始，并首先集中于预测局部部分。然后，创建一个与会话无关的分类模型来完成数据库的其余部分。计算置信度值以指导预测的手动检查和校正。为了将所提出的方法应用到应用中，我们介绍了NOVA -一种用于协作和机器辅助注释的开源工具。特别是，它使贴标人员能够立即访问CML策略，并直接提供结果的视觉反馈。我们的实验表明，所提出的方法有可能显着减少人类标记的努力。
英文摘要：
Scientific disciplines, such as Behavioural Psychology, Anthropology and recently Social Signal Processing are concerned with the systematic exploration of human behaviour. A typical work-flow includes the manual annotation (also called coding) of social signals in multi-modal corpora of considerable size. For the involved annotators this defines an exhausting and time-consuming task. In the article at hand we present a novel method and also provide the tools to speed up the coding procedure. To this end, we suggest and evaluate the use of Cooperative Machine Learning (CML) techniques to reduce manual labelling efforts by combining the power of computational capabilities and human intelligence. The proposed CML strategy starts with a small number of labelled instances and concentrates on predicting local parts first. Afterwards, a session-independent classification model is created to finish the remaining parts of the database. Confidence values are computed to guide the manual inspection and correction of the predictions. To bring the proposed approach into application we introduce NOVA - an open-source tool for collaborative and machine-aided annotations. In particular, it gives labellers immediate access to CML strategies and directly provides visual feedback on the results. Our experiments show that the proposed method has the potential to significantly reduce human labelling efforts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
