Little Things With Big Effects
On the Identification and Interpretation of Tokens for Error Diagnosis in ICALL
DOI:
https://doi.org/10.1558/cj.v26i3.580-591Keywords:
ICALL, Tokenization, Feedback, Learner Language, Natural Language Processing, Annotation-based ArchitecturesAbstract
Error diagnosis in ICALL typically analyzes learner input in an attempt to abstract and identify indicators of the learner's (mis)conceptions of linguistic properties. For written input, this process usually starts with the identification of tokens that will serve as the atomic building blocks of the analysis. In this paper, we discuss the consequences of mismatches between the learner's perception of a given token and the system's interpretation of its linguistic properties. Based on our analysis of the interaction of beginning learners of Portuguese with the ICALL system TAGARELA, we discuss why tokenization and the interpretation of accented characters deserve particular attention in a system used by language learners. On the computational side, we argue that the mismatches arising in such cases can be addressed in a general way by building ICALL systems on an annotation-based natural language processing architecture which monotonically enriches the representation of learner input.
References
Amaral, L., & Meurers, D. (2006, May 19). Where does ICALL fit into foreign language teaching? Presentation at the annual CALICO Conference, University of Hawai’i, Manoa, Hawai’i. Retrieved May 11, 2009, from http://purl.org/net/icall/handouts/calico06-amaral-meurers.pdf
Amaral, L., & Meurers, D. (2007). Putting activity models in the driver’s seat: Towards a demand-driven NLP architecture for ICALL. Presentation at the EUROCALL 2007 Symposium on NLP in CALL. University of Ulster, Coleraine Campus, Coleraine, Ireland. Retrieved April 10, 2009, from http://purl.org/net/icall/handouts/eurocall07-amaral-meurers.pdf
Amaral, L., & Meurers, D. (2008). From recording linguistic competence to supporting inferences about language acquisition in context: Extending the conceptualization of student models for intelligent computer-assisted language learning. Computer-Assisted Language Learning, 21, 323–338.
Bailey, S., & Meurers, D. (2008). Diagnosing meaning errors in short answers to reading comprehension questions. In J. Tetreault, J. Burstein, & R. De Felice (Eds.), Proceedings of the 3rd Workshop on Innovative Use of NLP for Building Educational Applications, held at ACL 2008 (pp. 107-115). Columbus, OH: Association for Computational Linguistics. Retrieved April 10, 2009, from http://aclweb.org/anthology-new/W/W08/W08-0913.pdf
Bick, E. (2000). The parsing system “Palavras”: Automatic grammatical analysis of Portuguese in a constraint grammar framework. Aarhus, Denmark: Aarhus University Press.
Bick, E. (2004). PaNoLa: Integrating constraint grammar and CALL. In H. Holmboe (Ed.), Nordic Language Technology, Arbog for Nordisk Sprogteknologisk Forsknings Program 2000-2004 (Yearbook 2003) (pp. 183-190). Copenhagen: Museum Tusculanum.
Heift, T. (2003). Multiple learner errors and meaningful feedback: A challenge for ICALL systems. CALICO Journal, 20, 533–548. Retrieved April 10, 2009, from https://calico.org/page.php?id=5
Hyland, K., & Hyland, F. (2006). Feedback on second language students’ writing. Language Teaching 39(2), 1-46.
Karlsson, F., Voutilainen, A., Heikkilä, J., & Anttila, A. (Eds.). (1995). Constraint grammar: A languageindependent system for parsing unrestricted text. Berlin and New York: Mouton de Gruyter.
Kuenning, G. (2005). International Ispell, Version 3.3.02. Available at: http://ficus-www.cs.ucla.edu/geoff/ispell.html
Lee, J., & Seneff, S. (2006). Automatic grammar correction for second-language learners. In A. El-Jaroudi (Ed.), INTERSPEECH 2006–ICSLP (pp. 1978-1981). Pittsburgh, PA: International Speech Communication Association (ISCA). Retrieved April 10, 2009, from http://groups.csail.mit.edu/sls/publications/2006/IS061299.pdf
Levin, L., & Evans, D. (1995). ALICE-chan: A case study in ICALL theory and practice. In V. Holland, J. Kaplan, & M. Sams (Eds.), Intelligent tutoring systems. Theory shaping technology (pp. 77-97). Mahwah, NJ: Lawrence Erlbaum Associates.
Martins, R., Hasegawa, R., & das Graças Volpe Nunes, M. (2006). Curupira: A functional parser for Brazilian Portuguese. In N. J. Mamede, J. Baptista, I. Trancoso, & M. das Graças Volpe Nunes, Computational Processing of the Portuguese Language, 6th International Workshop, PROPOR. (Lecture Notes in Computer Science 2721) (pp. 179-183). Berlin, Heidelberg: Springer Verlag.
Nagata, N. (2009). Robo-Sensei NLP-based error detection and feedback generation. CALICO Journal, 26, 562-579. Retrieved April 10, 2009, from https://calico.org/page.php?id=5
Oerder, M., & Ney, H. (1993). Word graphs: An efficient interface between continuous-speech recognition and language understanding. In IEEE International Conference on Acoustics, Speech, and Signal Processing, 1993 (ICASSP 93) (Vol. 2, pp. 119-122). Los Alamitos, CA: IEEE Computer Society.
