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Active Learning for Dialogue Act Labelling

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNIP,volume 6669)


Active learning is a useful technique that allows for a considerably reduction of the amount of data we need to manually label in order to reach a good performance of a statistical model. In order to apply active learning to a particular task we need to previously define an effective selection criteria, that picks out the most informative samples at each iteration of active learning process. This is still an open problem that we are going to face in this work, in the task of dialogue annotation at dialogue act level. We present two different criteria, weighted number of hypothesis and entropy, that we have applied to the Sample Selection Algorithm for the task of dialogue act labelling, that retrieved appreciably improvements in our experimental approach.


  • Dialogue System
  • Weighted Number
  • Input Word
  • Active Learning Strategy
  • Active Learn Algorithm

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© 2011 Springer-Verlag Berlin Heidelberg

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Ghigi, F., Tamarit, V., Martínez-Hinarejos, CD., Benedí, JM. (2011). Active Learning for Dialogue Act Labelling. In: Vitrià, J., Sanches, J.M., Hernández, M. (eds) Pattern Recognition and Image Analysis. IbPRIA 2011. Lecture Notes in Computer Science, vol 6669. Springer, Berlin, Heidelberg.

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21256-7

  • Online ISBN: 978-3-642-21257-4

  • eBook Packages: Computer ScienceComputer Science (R0)