Estimating the number of segments for improving dialogue act labelling

In dialogue systems it is important to label the dialogue turns with dialogue-related meaning. Each turn is usually divided into segments and these segments are labelled with dialogue acts (DAs). A DA is a representation of the functional role of the segment. Each segment is labelled with one DA, representing its role in the ongoing discourse. The sequence of DAs given a dialogue turn is used by the dialogue manager to understand the turn. Probabilistic models that perform DA labelling can be used on segmented or unsegmented turns. The last option is more likely for a practical dialogue system, but it provides poorer results. In that case, a hypothesis for the number of segments can be provided to improve the results. We propose some methods to estimate the probability of the number of segments based on the transcription of the turn. The new labelling model includes the estimation of the probability of the number of segments in the turn. We tested this new approach with two different dialogue corpora: SwitchBoard and Dihana. The results show that this inclusion significantly improves the labelling accuracy. © Copyright Cambridge University Press 2011.Work supported by the EC (FEDER/FSE), the Spanish Government (MEC, MICINN, MITyC, MAEC, "Plan E", under grants MIPRCV "Consolider Ingenio 2010" CSD2007-00018, MITTRAL TIN2009-14633-C03-01, erudito.com TSI-020110-2009-439, FPI fellowship BES-2007-16834), and Generalitat Valenciana (grant Prometeo/2009/014 and grant ACOMP/2010/051).Tamarit Ballester, V.; Martínez-Hinarejos, C.; Benedí Ruiz, JM. (2012). Estimating the number of segments for improving dialogue act labelling. Natural Language Engineering. 18(1):1-19. doi:10.1017/S135132491000032XS119181Dybkjær, L., & Minker, W. (Eds.). (2008). Recent Trends in Discourse and Dialogue. Text, Speech and Language Technology. doi:10.1007/978-1-4020-6821-8Stolcke, A., Ries, K., Coccaro, N., Shriberg, E., Bates, R., Jurafsky, D., … Meteer, M. (2000). Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech. Computational Linguistics, 26(3), 339-373. doi:10.1162/089120100561737Fraser, N. M., & Gilbert, G. N. (1991). Simulating speech systems. Computer Speech & Language, 5(1), 81-99. doi:10.1016/0885-2308(91)90019-mSchatzmann J. , Thomson B. and Young S. 2007. Statistical user simulation with a hidden agenda. In Proceedings of the SIGdial Workshop on Discourse and Dialogue, pp. 273–82.Levin L. , Ries K. , Thymé-Gobbel A. , and Levie A. 1999. Tagging of speech acts and dialogue games in Spanish call home. In Workshop: Towards Standards and Tools for Discourse Tagging, pp. 42–7.Hinarejos, C. D. M., Granell, R., & Benedí, J. M. (2006). Segmented and unsegmented dialogue-act annotation with statistical dialogue models. Proceedings of the COLING/ACL on Main conference poster sessions -. doi:10.3115/1273073.1273146Benedí J. M. , Lleida E. , Varona A. , Castro M. J. , Galiano I. , Justo R. , López de Letona I. , and Miguel A. 2006. Design and acquisition of a telephone spontaneous speech dialogue corpus in Spanish: Dihana. In Fifth International Conference on Language Resources and Evaluation (LREC), pp. 1636–9.Young, S. J. (2000). Probabilistic methods in spoken–dialogue systems. Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 358(1769), 1389-1402. doi:10.1098/rsta.2000.0593Martínez-Hinarejos, C.-D., Benedí, J.-M., & Granell, R. (2008). Statistical framework for a Spanish spoken dialogue corpus. Speech Communication, 50(11-12), 992-1008. doi:10.1016/j.specom.2008.05.011Bisani M. and Ney H. 2004. Bootstrap estimates for confidence intervals in asr performance evaluation. In Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP '04). IEEE International Conference on, vol. 1, pp. 1:I–409–12.Garcia, P., & Vidal, E. (1990). Inference of k-testable languages in the strict sense and application to syntactic pattern recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(9), 920-925. doi:10.1109/34.57687Jurafsky D. , Shriberg E. and Biasca D. 1997. Switchboard SWBD-DAMSL shallow discourse function annotation coders manual - draft 13. Technical Report 97-01, University of Colorado Institute of Cognitive Science.Gorin, A. ., Riccardi, G., & Wright, J. . (1997). How may I help you? Speech Communication, 23(1-2), 113-127. doi:10.1016/s0167-6393(97)00040-xWalker, M. A. (2000). An Application of Reinforcement Learning to Dialogue Strategy Selection in a Spoken Dialogue System for Email. Journal of Artificial Intelligence Research, 12, 387-416. doi:10.1613/jair.713Core M. G. and Allen J. F. 2007. Coding dialogues with the DAMSL annotation scheme. In Fall Symposium on Communicative Action in Humans and Machines. American Association for Artificial Intelligence, pp. 28–35.Fukada T. , Koll D. , Waibel A. and Tanigaki K. 1998. Probabilistic dialogue act extraction for concept based multilingual translation systems. ICSLP 98 2771–4

Active learning for dialogue act labelling

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

J Acquir Immune Defic Syndr

African Americans and Hispanics in the United States have much higher rates of HIV than non-minorities. There is now strong evidence that a range of behavioral interventions are efficacious in reducing sexual risk behavior in these populations. Although a handful of these programs are just beginning to be disseminated widely, we still have not implemented effective programs to a level that would reduce the population incidence of HIV for minorities. We proposed that innovative approaches involving computational technologies be explored for their use in both developing new interventions and in supporting wide-scale implementation of effective behavioral interventions. Mobile technologies have a place in both of these activities. First, mobile technologies can be used in sensing contexts and interacting to the unique preferences and needs of individuals at times where intervention to reduce risk would be most impactful. Second, mobile technologies can be used to improve the delivery of interventions by facilitators and their agencies. Systems science methods including social network analysis, agent-based models, computational linguistics, intelligent data analysis, and systems and software engineering all have strategic roles that can bring about advances in HIV prevention in minority communities. Using an existing mobile technology for depression and 3 effective HIV prevention programs, we illustrated how 8 areas in the intervention/implementation process can use innovative computational approaches to advance intervention adoption, fidelity, and sustainability.P20 MH090318/MH/NIMH NIH HHS/United StatesP20MH090318/MH/NIMH NIH HHS/United StatesP30 AI050409/AI/NIAID NIH HHS/United StatesP30 AI073961/AI/NIAID NIH HHS/United StatesP30 DA027828/DA/NIDA NIH HHS/United StatesP30 MH074678/MH/NIMH NIH HHS/United StatesP30AI050409/AI/NIAID NIH HHS/United StatesP30AI073961/AI/NIAID NIH HHS/United StatesP30DA027828/DA/NIDA NIH HHS/United StatesP30MH074678/MH/NIMH NIH HHS/United StatesR01 DA025192/DA/NIDA NIH HHS/United StatesR01 DA030452/DA/NIDA NIH HHS/United StatesR01 MH066302/MH/NIMH NIH HHS/United StatesR01DA025192/DA/NIDA NIH HHS/United StatesR01DA030452/DA/NIDA NIH HHS/United StatesR01MH066302/MH/NIMH NIH HHS/United StatesR13 HD074468/HD/NICHD NIH HHS/United StatesR13 MH-081733-01A1/MH/NIMH NIH HHS/United StatesR13 MH081733/MH/NIMH NIH HHS/United StatesU01-PS000671/PS/NCHHSTP CDC HHS/United StatesUL1 TR000150/TR/NCATS NIH HHS/United StatesUL1 TR000460/TR/NCATS NIH HHS/United StatesUL1TR000460/TR/NCATS NIH HHS/United States2014-06-01T00:00:00Z23673892PMC374676

A computational future for preventing HIV in minority communities: How advanced technology can improve implementation of effective programs

Abstract African Americans and Hispanics in the U.S. have much higher rates of HIV than non-minorities. There is now strong evidence that a range of behavioral interventions are efficacious in reducing sexual risk behavior in these populations. While a handful of these programs are just beginning to be disseminated widely, we still have not implemented effective programs to a level that would reduce the population incidence of HIV for minorities. We propose that innovative approaches involving computational technologies be explored for their use in both developing new interventions as well as in supporting wide-scale implementation of effective behavioral interventions. Mobile technologies have a place in both of these activities. First, mobile technologies can be used in sensing contexts and interacting to the unique preferences and needs of individuals at times where intervention to reduce risk would be most impactful. Secondly, mobile technologies can be used to improve the delivery of interventions by facilitators and their agencies. Systems science methods, including social network analysis, agent based models, computational linguistics, intelligent data analysis, and systems and software engineering all have strategic roles that can bring about advances in HIV prevention in minority communities. Using an existing mobile technology for depression and three effective HIV prevention programs, we illustrate how eight areas in the intervention/implementation process can use innovative computational approaches to advance intervention adoption, fidelity, and sustainability