Automatic Discovery of Speech Act Categories in Educational Games

In this paper we address the important task of automated discovery of speech act categories in dialogue-based, multi-party educational games. Speech acts are important in dialogue-based educational systems because they help infer the student speaker’s intentions (the task of speech act classification) which in turn is crucial to providing adequate feedback and scaffolding. A key step in the speech act classification task is defining the speech act categories in an underlying speech act taxonomy. Most research to date has relied on taxonomies which are guided by experts’ intuitions, which we refer to as an extrinsic design of the speech act taxonomies. A pure data-driven approach would discover the natural groupings of dialogue utterances and therefore reveal the intrinsic speech act categories. To this end, this paper presents a fully-automated data-driven method to discover speech act taxonomies based on utterance clustering. Experiments were conducted on three datasets from three online educational games. This work is a step towards building speech act taxonomies based on both extrinsic (expert-driven) and intrinsic aspects (datadriven) of the target domain.

[1]  Srinivas Bangalore,et al.  Incremental Parsing Models for Dialog Task Structure , 2009, EACL.

[2]  Kristy Elizabeth Boyer,et al.  Dialogue Act Modeling in a Complex Task-Oriented Domain , 2010, SIGDIAL Conference.

[3]  Csr Young,et al.  How to Do Things With Words , 2009 .

[4]  A. van den Bosch,et al.  Finding Classes of Dialogue Utterances with Kohonen Networks , 1997 .

[5]  Andreas Stolcke,et al.  Dialogue act modeling for automatic tagging and recognition of conversational speech , 2000, CL.

[6]  Norbert Reithinger.  Some experiments in speech act prediction , 1994 .

[7]  Barbara Di Eugenio,et al.  FLSA: Extending Latent Semantic Analysis with Features for Dialogue Act Classification , 2004, ACL.

[8]  Ted Pedersen,et al.  WordNet::Similarity - Measuring the Relatedness of Concepts , 2004, NAACL.

[9]  Peter Wiemer-Hastings,et al.  Classification of Speech Acts in Tutorial Dialog , 2000 .

[10]  Jan Alexanderssony,et al.  Dialogue acts in VERBMOBIL-2 , 1997 .

[11]  James H. Martin,et al.  Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition, 2nd Edition , 2000, Prentice Hall series in artificial intelligence.

[12]  E. Maier,et al.  Dialogue Acts in VERBMOBIL , 1995 .

[13]  J. Sadock.  Speech acts , 2007 .

[14]  Norbert Reithinger,et al.  Utilizing Statistical Dialogue Act Processing in Verbrnobil , 1995, ACL.

[15]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[16]  Klaus Ries,et al.  HMM and neural network based speech act detection , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[17]  Arthur C. Graesser,et al.  Utterance Classification in AutoTutor , 2003, HLT-NAACL 2003.

[18]  David R. Traum,et al.  20 Questions on Dialogue Act Taxonomies , 2000, J. Semant..

[19]  M. Wish,et al.  Speech act theory in quantitative research on interpersonal behavior , 1985 .

[20]  Arthur C. Graesser,et al.  Automated Speech Act Classification For Online Chat , 2011, MAICS.

[21]  Marina Sbisà,et al.  Speech act theory , 2009 .

[22]  E. Gilder,et al.  The Authors , 1977 .

[23]  Kristy Elizabeth Boyer,et al.  Discovering Tutorial Dialogue Strategies with Hidden Markov Models , 2009, AIED.