Active learning for dialogue act labelling

Abstract

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. © 2011 Springer-Verlag.Work supported by the EC (FEDER/FSE) and the Spanish MEC/MICINN under the MIPRCV “Consolider Ingenio 2010” program (CSD2007-00018), MITTRAL (TIN2009-14633-C03-01) projects and the FPI scholarship (BES-2009-028965). Also supported by the Generalitat Valenciana under grant Prometeo/2009/014 and GV/2010/067Ghigi, F.; Tamarit Ballester, V.; Martínez-Hinarejos, C.; Benedí Ruiz, JM. (2011). Active learning for dialogue act labelling. En Lecture Notes in Computer Science. Springer Verlag (Germany). 6669:652-659. https://doi.org/10.1007/978-3-642-21257-4_81S6526596669Alcácer, N., Benedí, J.M., Blat, F., Granell, R., Martínez, C.D., Torres, F.: Acquisition and Labelling of a Spontaneous Speech Dialogue Corpus. In: SPECOM, Greece, pp. 583–586 (2005)Benedí, J.M., Lleida, E., Varona, A., Castro, M.J., Galiano, I., Justo, R., López, I., Miguel, A.: Design and acquisition of a telephone spontaneous speech dialogue corpus in spanish: DIHANA. In: Fifth LREC, Genova, Italy, pp. 1636–1639 (2006)Bunt, H.: Context and dialogue control. THINK Quarterly 3 (1994)Casacuberta, F., Vidal, E., Picó, D.: Inference of finite-state transducers from regular languages. Pat. Recognition 38(9), 1431–1443 (2005)Dybkjær, L., Minker, W. (eds.): Recent Trends in Discourse and Dialogue. Text, Speech and Language Technology, vol. 39. Springer, Dordrecht (2008)Gorin, A., Riccardi, G., Wright, J.: How may I help you? Speech Comm. 23, 113–127 (1997)Hwa, R.: Sample selection for statistical grammar induction. In: Proceedings of the 2000 Joint SIGDAT, pp. 45–52. Association for Computational Linguistics, Morristown (2000)Lavie, A., Levin, L., Zhan, P., Taboada, M., Gates, D., Lapata, M.M., Clark, C., Broadhead, M., Waibel, A.: Expanding the domain of a multi-lingual speech-to-speech translation system. In: Proceedings of the Workshop on Spoken Language Translation, ACL/EACL 1997 (1997)Martínez-Hinarejos, C.D., Tamarit, V., Benedí, J.M.: Improving unsegmented dialogue turns annotation with N-gram transducers. In: Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation (PACLIC23), vol. 1, pp. 345–354 (2009)Robinson, D.W.: Entropy and uncertainty, vol. 10, pp. 493–506 (2008)Stolcke, A., Coccaro, N., Bates, R., Taylor, P., van Ess-Dykema, C., Ries, K., Shriberg, E., Jurafsky, D., Martin, R., Meteer, M.: Dialogue act modelling for automatic tagging and recognition of conversational speech. Computational Linguistics 26(3), 1–34 (2000)Tamarit, V., Benedí, J., Martínez-Hinarejos, C.: Estimating the number of segments for improving dialogue act labelling. In: Proceedings of the First International Workshop of Spoken Dialog Systems Technology (2009)Young, S.: Probabilistic methods in spoken dialogue systems. Philosophical Trans. Royal Society (Series A) 358(1769), 1389–1402 (2000