Abstract
The dialogue act labelling task is the process of splitting and annotating a dialogue into dialogue meaningful units; the labelling task can be performed semi-automatically by using statistical models trained from previously annotated dialogues. The appropiate selection of training dialogues can make the process faster, and Active Learning is one suitable strategy for this selection. In this work, Active Learning based on two different criteria (Weighted Number of Hypothesis and Entropy) has been tested for the task of dialogue act labelling by using the N-gram Transducers model. The framework was tested against two heterogeneous corpora, DIHANA and SwitchBoard. The results confirm the goodness of this kind of selection strategy.
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Acknowledgements
Work supported by EC under FP7 project CasMaCat (FP7-28757), and by Spanish MINECO under projects STraDA (TIN2012-37475-C02-01) and Active2Trans (TIN2012-31723), and by Spanish MED/MICINN under the FPI scholarship BES-2009-028965, and by GVA under project AMIIS (ISIC/2012/004).
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Ghigi, F., Martínez-Hinarejos, CD., Benedí, JM. (2014). Active Learning to Speed-Up the Training Process for Dialogue Act Labelling. In: Vetulani, Z., Mariani, J. (eds) Human Language Technology Challenges for Computer Science and Linguistics. LTC 2011. Lecture Notes in Computer Science(), vol 8387. Springer, Cham. https://doi.org/10.1007/978-3-319-08958-4_21
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