Abstract
In an increasingly networked world, the availability of high quality translations is critical for success, especially in the context of international competition. International companies need to provide well translated, high quality technical documentation not only to be successful in the market but also to meet legal regulations. We seek to evaluate translation quality, specifically concerning technical documentation, and formulate a method to evaluate the translation quality of technical documents both when we do have access to the original documents and when we do not. We rely on state-of-the-art machine learning algorithms and translation evaluation metrics in the context of a knowledge discovery process. Our evaluation is performed on a sentence level where each sentence is classified as either professionally translated or machine translated. The results for each sentence is then combined to evaluate the full document. The research is based on a database that contains 22,327 sentences and 32 translation evaluation attributes, which are used to optimize Decision Trees that are used to evaluate translation quality. Our method achieves an accuracy of 70.48 % on sentence level for texts in the database and can accurately classify documents with at least 100 sentences.
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Notes
- 1.
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References
Albrecht, J., Hwa, R.: Regression for sentence-level MT evaluation with pseudo references. In: Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pp. 296–303 (2007)
Albrecht, J.S., Hwa, R.: The role of pseudo references in MT evaluation. In: Proceedings of the Third Workshop on Statistical Machine Translation, pp. 187–190. Association for Computational Linguistics (2008)
Doddington, G.: Automatic evaluation of machine translation quality using n-gram co-occurrence statistics. In: Proceedings of the Second International Conference on Human Language Technology Research, pp. 138–145. Morgan Kaufmann Publishers Inc. (2002)
Gamon, M., Aue, A., Smets, M.: Sentence-level MT evaluation without reference translations: beyond language modeling. In: Proceedings of the 10th Annual Conference of the European Association for Machine Translation (EAMT), pp. 103–111 (2005)
Kothes, L.: Grundlagen der Technischen Dokumentation: Anleitungen verständlich und normgerecht erstellen. Springer, Heidelberg (2010)
Kulesza, A., Shieber, S.M.: A learning approach to improving sentence-level MT evaluation. In: Proceedings of the 10th International Conference on Theoretical and Methodological Issues in Machine Translation, pp. 75–84 (2004)
Lavie, A., Agarwal, A.: METEOR: an automatic metric for MT evaluation with high levels of correlation with human judgments. In: Proceedings of the Second Workshop on Statistical Machine Translation, pp. 228–231. Association for Computational Linguistics (2007)
Luckert, M., Schaefer-Kehnert, M.: Using machine learning methods for evaluating the quality of technical documents. Master’s thesis, Linnaeus University, Sweden (2016). http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-52087
Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 311–318. Association for Computational Linguistics (2002)
Popović, M., Vilar, D., Avramidis, E., Burchardt, A.: Evaluation without references: IBM1 scores as evaluation metrics. In: Proceedings of the Sixth Workshop on Statistical Machine Translation, pp. 99–103. Association for Computational Linguistics (2011)
Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)
Rokach, L., Maimon, O.: Data Mining with Decision Trees: Theory and Applications. World Scientific, River Edge (2014)
Shapira, D., Storer, J.A.: Edit distance with move operations. In: Apostolico, A., Takeda, M. (eds.) CPM 2002. LNCS, vol. 2373, pp. 85–98. Springer, Heidelberg (2002)
Snover, M., Dorr, B., Schwartz, R., Micciulla, L., Makhoul, J.: A study of translation edit rate with targeted human annotation. In: Proceedings of Association for Machine Translation in the Americas, pp. 223–231 (2006)
Somers, H.: Round-trip translation: what is it good for? In: Proceedings of the Australasian Language Technology Workshop, pp. 127–133 (2005)
Acknowledgements
We are grateful for Andreas Kerren’s and Ola Peterson’s valuable feedback on the Master’s thesis project [8] that this research is based on.
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Luckert, M., Schaefer-Kehnert, M., Löwe, W., Ericsson, M., Wingkvist, A. (2016). A Classifier to Determine Whether a Document is Professionally or Machine Translated. In: Řepa, V., Bruckner, T. (eds) Perspectives in Business Informatics Research. BIR 2016. Lecture Notes in Business Information Processing, vol 261. Springer, Cham. https://doi.org/10.1007/978-3-319-45321-7_24
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