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An application of answer set programming to the field of second language acquisition

Published online by Cambridge University Press:  22 January 2014

DANIELA INCLEZAN
DANIELA INCLEZAN*
Affiliation:
Department of Computer Science and Software Engineering Miami University Oxford, OH 45056, USA (e-mail: [email protected])
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Abstract

This paper explores the contributions of Answer Set Programming (ASP) to the study of an established theory from the field of Second Language Acquisition: Input Processing. The theory describes default strategies that learners of a second language use in extracting meaning out of a text based on their knowledge of the second language and their background knowledge about the world. We formalized this theory in ASP, and as a result we were able to determine opportunities for refining its natural language description, as well as directions for future theory development. We applied our model to automating the prediction of how learners of English would interpret sentences containing the passive voice. We present a system, PIas, that uses these predictions to assist language instructors in designing teaching materials.

Keywords

answer set programmingsecond language acquisitionqualitative scientific theoriesnatural language
Type
Rapid Publications from the 12th International Conference on Logic Programming and Nonmonotonic Reasoning
Information
Theory and Practice of Logic Programming , Volume 15 , Issue 1 , January 2015 , pp. 1 - 17
Copyright
Copyright © Cambridge University Press 2014 

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References

Balduccini, M. and Gelfond, M. 2003. Diagnostic reasoning with A-Prolog. Journal of Theory and Practice of Logic Programming (TPLP) 3, 4–5, 425–461.CrossRefGoogle Scholar
Balduccini, M. and Girotto, S. 2010. Formalization of psychological knowledge in Answer Set Programming and its application. Journal of Theory and Practice of Logic Programming (TPLP) 10, 4–6, 725–740.CrossRefGoogle Scholar
Balduccini, M. and Girotto, S. 2011. ASP as a cognitive modeling tool: Short-term memory and long-term memory. In Logic Programming, Knowledge Representation, and Nonmonotonic Reasoning, Balduccini, M. and Son, T. C., Eds. Lecture Notes in Computer Science, vol. 6565. Springer, Berlin, Germany, 377–397.CrossRefGoogle Scholar
Baral, C. and Gelfond, M. 1994. Logic programming and knowledge representation. Journal of Logic Programming 19, 20, 73–148.CrossRefGoogle Scholar
Baral, C., Gelfond, M. and Rushton, N. 2009. Probabilistic reasoning with answer sets. Journal of Theory and Practice of Logic Programming (TPLP) 9, 1, 57–144.CrossRefGoogle Scholar
Dijkstra, T. and Van Heuven, W. 2002. The architecture of the bilingual word recognition system: From identification to decision. Bilingualism: Language and Cognition 33, 600–629.Google Scholar
Dijkstra, T., Van Heuven, W. and Grainger, J. 1998. Simulating cross-language competition with the bilingual interactive activation model. Psychologica Belgica 38, 177–196.CrossRefGoogle Scholar
Drescher, C., Gebser, M., Grote, T., Kaufmann, B., König, A., Ostrowski, M. and Schaub, T. 2008. Conflict-driven disjunctive answer set solving. In Proceedings of the Eleventh International Conference on Principles of Knowledge Representation and Reasoning (KR-08), Brewka, G. and Lang, J., Eds. AAAI Press, Palo Alto, CA, 422–432.Google Scholar
Gelfond, M. and Lifschitz, V. 1991. Classical negation in logic programs and disjunctive databases. New Generation Computing 9, 3–4, 365–386.CrossRefGoogle Scholar
Gelfond, M. and Lifschitz, V. 1998. Action languages. Electronic Transactions on AI 3, 16, 193–210.Google Scholar
Inclezan, D. 2012. Modeling a theory of second language acquisition in ASP. In Proceedings of the 14th International Workshop on Non-Monotonic Reasoning (NMR-12), Rosati, R. and Woltran, S., Eds. Rome, Italy, June 8–10.Google Scholar
Lee, J. F. and VanPatten, B. 2003. Making Communicative Language Teaching Happen. McGraw-Hill, New York, NY.Google Scholar
Marek, V. W. and Truszczynski, M. 1999. Stable Models and an Alternative Logic Programming Paradigm. In The Logic Programming Paradigm: A 25-Year Perspective, Apt, K. R., Marek, V. W., Truszczyński, M. and Warren, D. S., Eds. Springer Verlag, Berlin, Germany, 375–398.CrossRefGoogle Scholar
Niemelä, I. 1998. Logic programs with stable model semantics as a constraint programming paradigm. In Proceedings of the Workshop on Computational Aspects of Nonmonotonic Reasoning, 72–79.Google Scholar
Qin, J. 2008. The effect of processing instruction and dictogloss tasks on acquisition of the English passive voice. Language Teaching Research 12, 61–82.CrossRefGoogle Scholar
VanPatten, B. 1984. Learners' comprehension of clitic pronouns: More evidence for a word order strategy. Hispanic Linguistics 1, 57–67.Google Scholar
VanPatten, B. 1993. Grammar teaching for the acquisition-rich classroom. Foreign Language Annals 26, 435–450.CrossRefGoogle Scholar
VanPatten, B. 2002. Processing instruction: An update. Language Learning 52, 4, 755.–803.CrossRefGoogle Scholar
VanPatten, B. 2003. From Input to Output: A Teacher's Guide to Second Language Acquisition. McGraw-Hill, New York, NY.Google Scholar
VanPatten, B. 2004. Input Processing in Second Language Acquisition. Lawrence Erlbaum, Mahwah, NJ, 5–32.Google Scholar
VanPatten, B. and Cadierno, T. 1993. Explicit instruction and input processing. Studies in Second Language Acquisition 15, 225–243.CrossRefGoogle Scholar
VanPatten, B., Inclezan, D., Salazar, H. and Farley, A. P. 2009. Processing instruction and dictogloss: A study on object pronouns and word order in Spanish. Foreign Language Annals 42, 3, 557–575.CrossRefGoogle Scholar