Abstract
In general, large amount of segmented and labeled data is needed to estimate statistical language understanding systems. In recent years, different approaches have been proposed to reduce the segmentation and labeling effort by means of unsupervised o semi-supervised learning techniques. We propose an active learning approach to the estimation of statistical language understanding models that involves the transcription, labeling and segmentation of a small amount of data, along with the use of raw data. We use this approach to learn the understanding component of a Spoken Dialog System. Some experiments that show the appropriateness of our approach are also presented.
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García, F., Hurtado, LF., Sanchis, E., Segarra, E. (2011). An Active Learning Approach for Statistical Spoken Language Understanding. In: San Martin, C., Kim, SW. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2011. Lecture Notes in Computer Science, vol 7042. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25085-9_67
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DOI: https://doi.org/10.1007/978-3-642-25085-9_67
Publisher Name: Springer, Berlin, Heidelberg
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