A Generic Human–Machine Annotation Framework Based on Dynamic Cooperative Learning

The task of obtaining meaningful annotations is a tedious work, incurring considerable costs and time consumption. Dynamic active learning and cooperative learning are recently proposed approaches to reduce human effort of annotating data with subjective phenomena. In this paper, we introduce a novel generic annotation framework, with the aim to achieve the optimal tradeoff between label reliability and cost reduction by making efficient use of human and machine work force. To this end, we use dropout to assess model uncertainty and thereby to decide which instances can be automatically labeled by the machine and which ones require human inspection. In addition, we propose an early stopping criterion based on inter-rater agreement in order to focus human resources on those ambiguous instances that are difficult to label. In contrast to the existing algorithms, the new confidence measures are not only applicable to binary classification tasks but also regression problems. The proposed method is evaluated on the benchmark datasets for non-native English prosody estimation, provided in the Interspeech computational paralinguistics challenge. In the result, the novel dynamic cooperative learning algorithm yields 0.424 Spearman's correlation coefficient compared to 0.413 with passive learning, while reducing the amount of human annotations by 74%.