G. Tur, “Co-adaptation: Adaptive co-training for semi-supervised learning,” 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, Taipei, Taiwan, 2009, pp. 3721-3724, doi: 10.1109/ICASSP.2009.4960435.
Inspired by popular co-training and domain adaptation methods, we propose a co-adaptation algorithm. The goal is improving the performance of a dialog act segmentation model by exploiting the vast amount of unlabeled data. This task provides a nice framework for multiview learning, as it has been shown that lexical and prosodic features provide complementary information. Instead of simply adding machine-labeled data to the set of manually labeled data, co-adaptation technique adapts the existing models. While both co-training and domain adaptation techniques have been employed for dialog act segmentation, our experiments show that the proposed co-adaptation algorithm results in significantly better performance.