Abstract
Prepositions pose a particular challenge for many language learners, in part because of their seemingly arbitrary usage with certain verbs that does not necessarily translate directly across languages. As a consequence, many preposition errors in writing can be attributed to a direct transfer from a writer’s native language. While research in scalable tooling for second-language writing assistance has largely focused on automated error detection and correction, relatively little attention has been given to explaining why the errors may have occurred. A system that can distinguish interlingual errors – arising from an over-literal translation from a writer’s native language – from non-interlingual errors could serve as an insightful tool for language learners and teachers alike. In this work, we demonstrate the feasibility of classifying English preposition errors produced by native speakers of Spanish as interlingual. We propose a corpus-based method that exploits translation probabilities to estimate the likelihood that a writer has translated word-for-word from Spanish to English. We then show that this method correlates well with human judgments on the interlingual status of preposition errors and can be a basis for developing a tool for explaining one key source of errors in second-language writing.
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Notes
- 1.
One sentence was discarded after responses were collected due to an error in the set-up of the annotation task.
References
iWeb: The 14 billion word web corpus. https://ww.english-corpora.org/iweb/. Accessed 31 Dec 2021
spaCy: Industrial-strength natural language processing. https://spacy.io/. Accessed 19 Jan 2022
Alonso, M.R.A.: Language transfer: interlingual errors in Spanish students of English as a foreign language. Rev. Alicantina Estud. Ingleses 10, 7–14 (1997)
Bryant, C., Felice, M., Andersen, Ø.E., Briscoe, T.: The BEA-2019 shared task on grammatical error correction. In: Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, pp. 52–75. Association for Computational Linguistics, Florence (2019). https://doi.org/10.18653/v1/W19-4406
Dyer, C., Chahuneau, V., Smith, N.A.: A simple, fast, and effective reparameterization of IBM model 2. In: Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 644–648. Association for Computational Linguistics, Atlanta (2013)
Fan, A., et al.: Beyond English-centric multilingual machine translation. J. Mach. Learn. Res. 22(107), 1–48 (2021)
Ferris, D.R.: Treatment of Error in Second Language Student Writing. The Micahigan Series on Teaching Multilingual Writers, 2nd edn. The University of Michigan Press, Ann Arbor (2011)
Graën, J., Schneider, G.: Crossing the border twice: reimporting prepositions to alleviate L1-specific transfer errors. In: Proceedings of the Joint Workshop on NLP for Computer Assisted Language Learning and NLP for Language Acquisition, pp. 18–26. LiU Electronic Press, Gothenburg (2017)
James, C.: Errors in Language Learning and Use. Applied Linguistics and Language Study, Addison Wesley Longman, New York (1998)
Kim, D.H.: Explicitness in CALL feedback for enhancing advanced esl learners’ grammar skills. Ph.D. thesis, University of Illinois at Urbana-Champaign (2009)
Koban, D.: A case study of Turkish ESL learners at LaGuardia Community College, NYC error analysis. In: Dan, C. (ed.) Languages, Literature, and Linguistics. International Proceedings of Economics Development and Research, vol. 26. IACSIT Press (2011)
Ng, H.T., Wu, S.M., Wu, Y., Hadiwinoto, C., Tetreault, J.: The CoNLL-2013 shared task on grammatical error correction. In: Proceedings of the Seventeenth Conference on Computational Natural Language Learning: Shared Task, pp. 1–12. Association for Computational Linguistics, Sofia (2013)
Ramos, M.A., et al.: Towards a motivated annotation schema of collocation errors in learner corpora. In: Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC 2010), Valletta, Malta (2010)
Schwenk, H.: Filtering and mining parallel data in a joint multilingual space. In: Proceedings of the 56th annual meeting of the association for computational linguistics, pp. 228–234. Association for Computational Linguistics, Melbourne (2018). https://doi.org/10.18653/v1/P18-2037
Schwenk, H., Li, X.: A corpus for multilingual document classification in eight languages. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki (2018)
Schwenk, H., Wenzek, G., Edunov, S., Grave, E., Joulin, A., Fan, A.: CCMatrix: mining billions of high-quality parallel sentences on the web. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, pp. 6490–6500. Association for Computational Linguistics (2021). https://doi.org/10.18653/v1/2021.acl-long.507
Sumonsriworakun, P., Pongpairoj, N.: Systematicity of L1 Thai learners’ English interlanguage of dependent prepositions. Indon. J. Appl. Linguist. 6(2), 246–259 (2017)
Swan, M., Smith, B.: Learner English: A Teacher’s Guide to Interference and Other Problems. Cambridge Handbooks for Language Teachers, 2nd edn. Cambridge University Press, Cambridge (2001). https://doi.org/10.1017/CBO9780511667121
Tarnaoui, M.M.: Analyse contrastive FLE/ Tachelhit: le cas des prepositions diagnostic des difficultés et remédiations didactiques. Stud. Gramaticǎ Contrastivǎ 30, 69–81 (2018)
Tiedemann, J.: Parallel data, tools and interfaces in OPUS. In: Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC 2012), pp. 2214–2218. European Language Resources Association (ELRA), Istanbul (2012)
Tomasello, M., Herron, C.: Feedback for language transfer errors: the garden path technique. Stud. Second. Lang. Acquis. 11(4), 385–395 (1989)
Yannakoudakis, H., Briscoe, T., Medlock, B.: A new dataset and method for automatically grading ESOL texts. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 180–189 (2011)
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Monaikul, N., Di Eugenio, B. (2023). Detecting Interlingual Errors: The Case of Prepositions. In: Frasson, C., Mylonas, P., Troussas, C. (eds) Augmented Intelligence and Intelligent Tutoring Systems. ITS 2023. Lecture Notes in Computer Science, vol 13891. Springer, Cham. https://doi.org/10.1007/978-3-031-32883-1_10
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