Co-adaptation: Adaptive co-training for semi-supervised learning

Authors:
Gokhan Tur
Affiliations:
SRI International, Speech Technology and Research Lab, Menlo Park, CA, 94025, USA
Venue:
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Year:
2009

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Abstract

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.