Validation of Automated Scores of Human Translation of Legal Texts against Non-test Indicators of Translation Competence
Abstract
This study tries to explore the validity of lexical Automated Translation Quality Evaluation Understudy Metrics (ATQEUM) in scoring Certified Translator Accreditation Tests in Islamic Republic of Iran as an instance of legal texts. This is conducted against non-test indicators of translation competence, including the participants records of their scores in “The Translation of Documents and Deeds I and II” courses, their average score of all courses of practical translation nature, and their BA GPA score in Translation Studies Major. Although, they have not revealed a significant correlation with the nontest indicators of translation competence, they have had a highly significant correlation with the scores granted by human expert scorers on the two sample tests. Therefore, according to all the data collected and analyzed, it has ultimately been concluded that a collection of “-PER, -TERp-A, BLUE-1, NIST-1, ROUGE-1, GTM-1” lexical ATQEUMs (including all various lexical similarity-based techniques of edit distance, precision, recall, and F-measure) can be considered as the optimal translation meta-evaluation set to score certified translator accreditation tests.
Keywords:
Automated scoring, translation evaluation, validity, lexical ATQEUMsReferences
Bowker, L. (2001). Towards a Methodology for a Corpus-Based Approach to Translation Evaluation. Meta: Translators' Journal, 46(2), 345-364.
Chapelle, C., Enright, M., & Jamieson, J. (2008). Test score interpretation and use. In C. Chapelle, M. Enright, & J. Jameison (Eds.), Building a validity argument for the Test of English as a Foreign LanguageTM. New York, NY: Routledge
Chatzikoumi, E. (n.d.). How to evaluate machine translation: A review of automated and human metrics. Natural Language Engineering, 1-25.
Coughlin, D. (2003). Correlating Automated and Human Assessments of Machine Translation Quality. Proceedings of Machine Translation Summit IX (pp. 23–27).
Doddington, G. (2002). Automatic Evaluation of Machine Translation Quality Using N-gram Co- Occurrence Statistics. Proceedings of the 2nd International Conference on Human Language Technology, (pp. 138–145).
Fellbaum, C. (Ed.). (1998). WordNet. An Electronic Lexical Database. The MIT Press
Gime ́nez Linares, J. A. (2009). Empirical Machine Translation and its Evaluation. TALP Research Center. Universitat Polite`cnica de Catalunya, Barcelona, Spain.
Giménez, J., & Màrquez, L. (2010). Linguistic measures for automatic machine translation evaluation. Mach Translat, 24, 209–240.
Gonz`alez, M., & Gim ́enez, J. (2014). Asiya: An Open Toolkit for Automatic Machine Translation (Meta-)Evaluation, Technical Manual, Version 3.0. Retrieved from TALP Research Center Project Management:
http://nlp.lsi.upc.edu/asiya/Asiya_technical_manual_v3.0.pdf
Kane, M. (2001). Validating high-stakes testing programs. Educational Measurement: Issues and Practice, 21(1), 31–35.
Levenshtein, V. I. (1966). Binary Codes Capable of Correcting Deletions, Insertions and Reversals. Soviet Physics Doklady, 8(10), 707–710.
Lin, C.-Y., & Och, F. J. (2004). Automatic Evaluation of Machine Translation Quality Using Longest Common Subsequence and Skip-Bigram Statistics. ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics.
M`arquez, L. (2013). Automatic Evaluation of Machine Translation Quality. Invited talk at Dialogue 2013. Bekasovo Resort, Russia: TALP Research Center, Technical University of Catalonia (UPC).
Melamed, I. D., Green, R., & Turian, J. (2003). Precision and Recall of Machine Translation. Proceedings of the Joint Conference on Human Language Technology and the North American Chapter of the Association for Computational Linguistics (HLT-NAACL).
Mislevy, R. J., Steinberg, L. S., & Almond, R. G. (2003). On the structure of educational assessments. Measurement: Interdisciplinary Research and Perspectives, 1, 3-62.
Nießen, S., Och, F. J., Leusch, G., & Ney, H. (2000). An Evaluation Tool for Machine Translation: Fast Evaluation for MT Research. Proceedings of the 2nd International Conference on Language Resources and Evaluation (LREC).
Olohan, M. (2004). Introducing Corpora in Translation Studies. New York: Routledge.
Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2002). BLEU: a Method for Automatic Evaluation of Machine Translation. Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), (pp. 311-318). Philadelphia.
Saldanha, G., & O'Brien, S. (2014). Research Methodologies in Translation Studies. London and New York: Routledge, Taylor and Francis Group.
Snover, M., Dorr, B., Schwartz, R., Micciulla, L., & Makhoul, J. (2006). A Study of Translation Edit Rate with Targeted Human Annotation. Proceedings of the 7th Conference of the Association for Machine Translation in the Americas (AMTA) (pp. 223–231).
Tillmann, C., Vogel, S., Ney, H., Zubiaga, A., & Sawaf, H. (1997). Accelerated DP based Search for Statistical Translation. Proceedings of European Conference on Speech Communication and Technology.
Williams, J., & Chesterman, A. (2002). The Map: A Beginner's Guide to Doing Research in Translation Studies. Manchester: St. Jerome Publishing.
Weigle, S. C. (2011). Validation of Automated Scores of TOEFL iBT® Tasks Against Nontest Indicators of Writing Ability. TOEFL iBT® Research Report. ETS, Georgia State University, Atlanta.
Yang, Y., Buckendahl, C. W., Jusziewicz, P. J., & Bhola, D. S. (2002). A review of strategies for validating computer-automated scoring. Applied Measurement in Education, 15, 391–412.
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Copyright Licensee: Iranian Journal of Translation Studies. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution–NonCommercial 4.0 International (CC BY-NC 4.0 license).
