Understanding Student Language: An Unsupervised Dialogue Act Classification Approach

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Feb 24, 2015
Aysu Ezen-Can Kristy Elizabeth Boyer

Abstract

Within the landscape of educational data, textual natural language is an increasingly vast source of learning-centered interactions. In natural language dialogue, student contributions hold important information about knowledge and goals. Automatically modeling the dialogue act of these student utterances is crucial for scaling natural language understanding of educational dialogues. Automatic dialogue act modeling has long been addressed with supervised classification techniques that require substantial manual time and effort. Recently, there is emerging interest in unsupervised dialogue act classification, which addresses the challenges related to manually labeling corpora. This paper builds on the growing body of work in unsupervised dialogue act classification and reports on the novel application of an information retrieval technique, the Markov Random Field, for the task of unsupervised dialogue act classification. Evaluation against manually labeled dialogue acts on a tutorial dialogue corpus in the domain of introductory computer science demonstrates that the proposed technique outperforms existing approaches to education-centered unsupervised dialogue act classification. Unsupervised dialogue act classification techniques have broad application in educational data mining in areas such as collaborative learning, online message boards, classroom discourse, and intelligent tutoring systems.

How to Cite

Ezen-Can, A., & Boyer, K. E. (2015). Understanding Student Language: An Unsupervised Dialogue Act Classification Approach. JEDM | Journal of Educational Data Mining, 7(1), 51-78. https://doi.org/10.5281/zenodo.3554707
Abstract 820 | PDF Downloads 204

##plugins.themes.bootstrap3.article.details##

Keywords

unsupervised dialogue act classification, Markov Random Field, natural language dialogue

References
ALEVEN, V., POPESCU, O., AND KOEDINGER, K. R. 2001. Towards tutorial dialog to support self- explanation : Adding natural language understanding to a Cognitive Tutor. Proceedings of Artificial Intelligence in Education, 246–255.

ALLEN, J. F., SCHUBERT, L. K., FERGUSON, G., HEEMAN, P., HWANG, C. H., KATO, T.,

LIGHT, M., MARTIN, N., MILLER, B., POESIO, M., ET AL. 1995. The TRAINS project: A case study in building a conversational planning agent. Journal of Experimental & Theoretical Artificial Intelligence 7, 1, 7–48.

ATAPATTU, T., FALKNER, K., AND FALKNER, N. 2014. Acquisition of triples of knowledge from lecture notes: A natural language processing approach. In Proceedings of the International Conference on Educational Data Mining. 193–196.

AUSTIN, J. L. 1975. How to do things with words. Vol. 1955. Oxford university press.

BANGALORE, S., DI FABBRIZIO, G., AND STENT, A. 2008. Learning the structure of taskdriven human–human dialogs. IEEE Transactions on Audio, Speech, and Language Processing 16, 7, 1249–1259.

BECKER, L., BASU, S., AND VANDERWENDE, L. 2012. Mind the gap: learning to choose gaps for question generation. In Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 742–751.

BOYER, K. E., HA, E. Y., PHILLIPS, R., WALLIS, M. D., VOUK, M. A., AND LESTER, J. C. 2010. Dialogue act modeling in a complex task-oriented domain. In Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue. Association for Computational Linguistics, 297–305.

BOYER, K. E., PHILLIPS, R., INGRAM, A., HA, E. Y., WALLIS, M., VOUK, M., AND

LESTER, J. 2011. Investigating the relationship between dialogue structure and tutoring effectiveness: a hidden Markov modeling approach. International Journal of Artificial Intelligence in Education 21, 1, 65–81.

BOYER, K. E., VOUK, M. A., AND LESTER, J. C. 2007. The influence of learner characteristics on task-oriented tutorial dialogue. In Proceedings of the 13th International Conference on Artificial Intelligence in Education (AIED). 365–372.

CHEN, S. S. AND GOPALAKRISHNAN, P. S. 1998. Clustering via the Bayesian information criterion with applications in speech recognition. In Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing. Vol. 2. 645–648.

CORE, M. G. AND ALLEN, J. 1997. Coding dialogs with the DAMSL annotation scheme. In Proceedings of the AAAI Fall Symposium on Communicative Action in Humans and Machines. 28–35.

CROOK, N., GRANELL, R., AND PULMAN, S. 2009. Unsupervised classification of dialogue acts using a Dirichlet process mixture model. In Proceedings of the SIGDIAL 2009 Conference. Association for Computational Linguistics, 341–348.

DI EUGENIO, B., XIE, Z., AND SERAFIN, R. 2010. Dialogue act classification, higher order dialogue structure, and instance-based learning. Dialogue & Discourse 1, 2, 1–24.

D’MELLO, S., OLNEY, A., AND PERSON, N. 2010. Mining collaborative patterns in tutorial dialogues. Journal of Educational Data Mining 2, 1, 2–37.

EZEN-CAN, A. AND BOYER, K. E. 2013. Unsupervised classification of student dialogue acts with query-likelihood clustering. In Proceedings of the International Conference on Educational Data Mining. 20–27.

EZEN-CAN, A. AND BOYER, K. E. 2014a. A Preliminary Investigation of Learner Characteristics for Unsupervised Dialogue Act Classification. In Proceedings of the 7th International Conference on Educational Data Mining (EDM). 373–374.

EZEN-CAN, A. AND BOYER, K. E. 2014b. Combining task and dialogue streams in unsupervised dialogue act models. In Proceedings of the 15th Annual SIGDIAL Meeting on Discourse and Dialogue. 113–122.

FERGUSON, R., WEI, Z., HE, Y., AND BUCKINGHAM SHUM, S. 2013. An evaluation of learning analytics to identify exploratory dialogue in online discussions. In Proceedings of the Third International Conference on Learning Analytics and Knowledge. ACM, 85–93.

FORBES-RILEY, K. AND LITMAN, D. J. 2005. Using bigrams to identify relationships between student certainness states and tutor responses in a spoken dialogue corpus. In Proceedings of the 6th SIGDIAL Workshop on Discourse and Dialogue. 87–96.

FORSYTH, C. M., GRAESSER, A. C., PAVLIK JR, P., CAI, Z., BUTLER, H., HALPERN, D., AND MILLIS, K. 2013. Operation aries!: Methods, mystery, and mixed models: Discourse features predict affect in a serious game. Journal of Educational Data Mining 5, 1, 147–189.

GONZ´ALEZ-BRENES, J. P., MOSTOW, J., AND DUAN, W. 2011. How to classify tutorial dialogue? comparing feature vectors vs. sequences. In Proceedings of the International Conference on Educational Data Mining. 169–178.

GRAESSER, A., PERSON, N. K., AND MAGLIANO, J. P. 1995. Collaborative dialogue patterns in naturalistic one-to-one tutoring. Applied cognitive psychology 9, 6, 495–522.

GRAESSER, A. C., VANLEHN, K., ROS´E, C. P., JORDAN, P. W., AND HARTER, D. 2001. Intelligent tutoring systems with conversational dialogue. AI magazine 22, 4, 39.

HIGASHINAKA, R., KAWAMAE, N., SADAMITSU, K., MINAMI, Y., MEGURO, T., DOHSAKA, K., AND INAGAKI, H. 2011. Unsupervised clustering of utterances using non-parametric Bayesian methods. In INTERSPEECH. 2081–2084.

JOTY, S., CARENINI, G., AND LIN, C.-Y. 2011. Unsupervised modeling of dialog acts in asynchronous conversations. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence. 1807–1813.

JURAFSKY, D. AND MARTIN, J. H. 2000. Speech & language processing. Pearson Education.

KLEIN, D. AND MANNING, C. D. 2003. Accurate unlexicalized parsing. Proceedings of the 41st Meeting of the Association for Computational Linguistics, 423–430.

KUMAR, R., BEUTH, J. L., AND ROS´E , C. P. 2011. Conversational strategies that support idea generation productivity in groups. In Proceedings of the Computer Supported Collaborative Learning (CSCL) Conference. 398–405.

LEE, D., JEONG, M., KIM, K., RYU, S., AND LEE, G. 2013. Unsupervised spoken language understanding for a multi-domain dialog system. In IEEE Transactions On Audio, Speech, and Language Processing. Vol. 21. 2451–2464.

LITMAN, D. J., ROS´E, C. P., FORBES-RILEY, K., VANLEHN, K., BHEMBE, D., AND SILLIMAN, S. 2006. Spoken versus typed human and computer dialogue tutoring. International Journal of Artificial Intelligence in Education 16, 2, 145–170.

MANNING, C. D., RAGHAVAN, P., AND SCH¨U TZE, H. 2008. Introduction to information retrieval. Vol. 1. Cambridge University Press.

MARCUS, M. P., SANTORINI, B., AND MARCINKIEWICZ, M. A. 1993. Building a large annotated corpus of English: The Penn Treebank. Computational Linguistics 19, 2, 313– 330.

MARINEAU, J., WIEMER-HASTINGS, P., HARTER, D., OLDE, B., CHIPMAN, P., KARNAVAT, A., POMEROY, V., RAJAN, S., GRAESSER, A., GROUP, T. R., ET AL. 2000. Classification of speech acts in tutorial dialog. In Proceedings of the Workshop on Modeling Human Teaching Tactics and Strategies at the Intelligent Tutoring Systems 2000 Conference. 65– 71.

METZLER, D. AND CROFT, W. B. 2005. A Markov random field model for term dependencies. In Proceedings of the 28th Annual International ACM SIGIR Conference on Research and development in information retrieval. 472–479.

MOSTOW, J., BECK, J., CEN, H., CUNEO, A., GOUVEA, E., AND HEINER, C. 2005. An educational data mining tool to browse tutor-student interactions: Time will tell. In Proceedings of the Workshop on Educational Data Mining, National Conference on Artificial Intelligence. 15–22.

NG, R. T. AND HAN, J. 1994. Efficient and effective clustering methods for spatial data mining. In Proceedings of the 20th International Conference on Very Large Data Bases. 144–155.

NIRAULA, N. B., RUS, V., STEFANESCU, D., AND GRAESSER, A. C. 2014. Mining gapfill questions from tutorial dialogues. In Proceedings of the International Conference on Educational Data Mining. 265–268.

QUARTERONI, S., IVANOV, A. V., AND RICCARDI, G. 2011. Simultaneous dialog act segmentation and classification from human-human spoken conversations. In IEEE International Conference on Acoustics, Speech and Signal Processing. 5596–5599.

RANGARAJAN SRIDHAR, V. K., BANGALORE, S., AND NARAYANAN, S. 2009. Combining lexical, syntactic and prosodic cues for improved online dialog act tagging. Computer Speech & Language 23, 4, 407–422.

RICARDO, B.-Y. AND RIBEIRO-NETO, B. 1999. Modern information retrieval. Vol. 463. ACM press, New York.

RITTER, A., CHERRY, C., AND DOLAN, B. 2010. Unsupervised modeling of Twitter conversations. In Proceedings of the Association for Computational Linguistics. 172–180.

RUS, V., MOLDOVAN, C., NIRAULA, N., AND GRAESSER, A. C. 2012. Automated discovery of speech act categories in educational games. In Proceedings of the International Educational Data Mining Society. 25–32.

SADOHARA, K., KOJIMA, H., NARITA, T., NIHEI, M., KAMATA, M., ONAKA, S., FUJITA, Y., AND INOUE, T. 2013. Sub-lexical dialogue act classification in a spoken dialogue system support for the elderly with cognitive disabilities. In Proceedings of the Fourth Workshop on Speech and Language Processing for Assistive Technologies. 93–98.

SAMEI, B., LI, H., KESHTKAR, F., RUS, V., AND GRAESSER, A. C. 2014. Context-based speech act classification in intelligent tutoring systems. In Proceedings of International Conference on Intelligent Tutoring Systems. 236–241.

SEARLE, J. R. 1969. Speech acts: An essay in the philosophy of language. Cambridge university press.

SERAFIN, R. AND DI EUGENIO, B. 2004. FLSA: Extending latent semantic analysis with features for dialogue act classification. In Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics. 692–699.

SHRIBERG, E., STOLCKE, A., JURAFSKY, D., COCCARO, N., METEER, M., BATES, R.,

TAYLOR, P., RIES, K., MARTIN, R., AND VAN ESS-DYKEMA, C. 1998. Can prosody aid the automatic classification of dialog acts in conversational speech? Language and speech 41, 3-4, 443–492.

STEFANESCU, D., RUS, V., AND GRAESSER, A. C. 2014. Towards assessing students’ prior knowledge from tutorial dialogues. In Proceedings of the International Conference on Educational Data Mining. 197–200.

STOLCKE, A., RIES, K., COCCARO, N., SHRIBERG, E., BATES, R., JURAFSKY, D., TAYLOR, P., MARTIN, R., VAN ESS-DYKEMA, C., AND METEER, M. 2000. Dialogue act modeling for automatic tagging and recognition of conversational speech. Computational linguistics 26, 3, 339–373.

STROHMAN, T., METZLER, D., TURTLE, H., AND CROFT, W. B. 2005. Indri: A language model-based search engine for complex queries. In Proceedings of the International Conference on Intelligent Analysis. Vol. 2. 2–6.

TRAUM, D. R. 1999. Speech acts for dialogue agents. In Foundations of Rational Agency. Springer, 169–201.

WEERASINGHE, A., MITROVIC, A., AND MARTIN, B. 2009. Towards individualized dialogue support for ill-defined domains. International Journal of Artificial Intelligence in Education 19, 4, 357–379.

WEN, M., YANG, D., AND ROS´E , C. P. 2014. Sentiment analysis in MOOC discussion forums: What does it tell us? In Proceedings of the International Conference on Educational Data Mining.

XIONG, W. AND LITMAN, D. 2014. Evaluating topic-word review analysis for understand- ing student peer review performance. In Proceedings of the International Conference on Educational Data Mining.

XU, X., MURRAY, T., WOOLF, B. P., AND SMITH, D. 2013. Mining social deliberation in online communication–if you were me and I were you. In Proceedings of the International Conference on Educational Data Mining.

YOO, J. AND KIM, J. 2014. Capturing difficulty expressions in student online Q&A discussions. In Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence. 208–214.

ZHAI, C. AND LAFFERTY, J. 2001. A study of smoothing methods for language models applied to ad-hoc information retrieval. In Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 334–342.
Section
Articles