Abstract
Term translation of Chinese historical classics is very difficult and time-consuming work, and using term alignment methods to extract term translation pairs is of great help for historical term translation. However, the limited bilingual corpora resources of historical classics and special morphology of the ancient Chinese result in poor performance of term alignment. To this end, this paper proposes a historical term alignment method using modern Chinese as a pivot language. The method first identifies English terms by rules, then aligns them from English to modern Chinese and then from modern Chinese to ancient Chinese. The use of English-modern Chinese corpus and modern-ancient Chinese corpus instead of English-ancient Chinese corpus solves the shortage problem of the parallel corpus. Moreover, using modern Chinese as a pivot language effectively reduces the alignment errors caused by the abbreviations and the interchangeable characters of ancient Chinese. In the term alignment experiment on Shiji, our method outperformed the direct alignment method significantly, which proves the validity of our method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Huang, Z.: English translation of cultural classics and postgraduate teaching of translation in Suzhou University. Shanghai J. Translators 1, 56–58 (2007). (In Chinese)
Li, X., Che, C., Liu, X., Lin, H., Wang, R.: Corpus-based extraction of Chinese historical term translation equivalents. Int. J. Asian Lang. Process. 20(2), 63–74 (2010)
Che, C., Zheng, X.: The extraction of term translation pairs for chinese historical classics based on sub-words. J. Chin. Inf. Process. 30(3), 46–51 (2016). (In Chinese)
Yout, G.-W., Hwangt, S.-W., Song, Y.-I., et al.: Mining name translations from entity graph mapping. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 430–439. ACL, Stroudsburg (2010)
Feng, D., Lv, Y., Zhou, M.: A new approach for English-Chinese named entity alignment. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 372–379. ACL, Stroudsburg (2004)
Lee, C.-J., Chang, J.S., Jang, J.-S.R.: Alignment of bilingual named entities in parallel corpora using statistical models and multiple knowledge sources. ACM Trans. Asian Lang. Inf. Process. (TALIP) 5(2), 121–145 (2006)
Zhang, Y., Wang, Y., Cen, L., et al.: Fusion of multiple features and ranking SVM for web-based English-Chinese OOV term translation. In: 23rd International Conference on Computational Linguistics (COLING), pp. 1435–1443. ACM, New York (2010)
Chen, Y., Zong, C., Su, K.-Y.: On jointly recognizing and aligning bilingual named entities. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 631–639. ACL, Stroudsburg (2010)
Chen, Y., Zong, C., Su, K.-Y.: A joint model to identify and align bilingual named entities. Comput. Linguist. 39(2), 229–266 (2013)
Wang, M., Che, W., Manning, C.D.: Joint word alignment and bilin-gual named entity recognition using dual de-composition. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pp. 1073–1082. ACL, Stroudsburg (2013)
Wu, H., Wang, H.: Pivot language approach for phrase-based statistical machine translation. Mach. Transl. 21(3), 165–181 (2007)
Wu, H., Wang, H.: Revisiting pivot language approach for machine translation. In: Proceedings of the Meeting of the Association for Computational Linguistics and the International Joint Conference on Natural Language Processing of the AFNLP, pp. 154–162. ACL, Stroudsburg (2009)
Durrani, N., Koehn, P.: Improving machine translation via triangulation and transliteration. In: Conference of the European Association for Machine Translation, Dubrovnik, Croatia, pp. 71–78 (2014)
Zhu, X., He, Z., Wu, H., Zhu, C., Wang, H., Zhao, T.: Improving pivot-based statistical machine translation by pivoting the co-occurrence count of phrase pairs. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1665–1675. ACL, Stroudsburg (2014)
Brown, P.E., Pietra, S.A.D., Pietra, V.J.D., Mercer, R.L.: The mathematics of statistical machine translation: parameter estimation. Comput. Linguis. 19(2), 263–311 (1993)
Watson, B.: Records of the Grand Historian of China. Columbia University Press, New York (1961)
Watson, B.: Records of the Grand Historian: Qin Dynasty. Chinese University of Hong Kong/Columbia University Press, Hong Kong/New York (1993)
Och, F.J., Ney, H.: A systematic comparison of various statistical alignment models. Comput. Linguis. 29(1), 19–51 (2003)
Acknowledgements
This work is supported by the National Natural Science Foundation of China (No. 61402068).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Wu, X., Zhao, H., Jing, L., Che, C. (2019). Chinese Historical Term Translation Pairs Extraction Using Modern Chinese as a Pivot Language. In: Sun, M., Huang, X., Ji, H., Liu, Z., Liu, Y. (eds) Chinese Computational Linguistics. CCL 2019. Lecture Notes in Computer Science(), vol 11856. Springer, Cham. https://doi.org/10.1007/978-3-030-32381-3_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-32381-3_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32380-6
Online ISBN: 978-3-030-32381-3
eBook Packages: Computer ScienceComputer Science (R0)