Advertisement

Journal of Zhejiang University SCIENCE C

, Volume 15, Issue 4, pp 241–253 | Cite as

Topic-aware pivot language approach for statisticalmachine translation

  • Jin-song Su
  • Xiao-dong Shi
  • Yan-zhou Huang
  • Yang Liu
  • Qing-qiang Wu
  • Yi-dong Chen
  • Huai-lin Dong
Article

Abstract

The pivot language approach for statistical machine translation (SMT) is a good method to break the resource bottleneck for certain language pairs. However, in the implementation of conventional approaches, pivot-side context information is far from fully utilized, resulting in erroneous estimations of translation probabilities. In this study, we propose two topic-aware pivot language approaches to use different levels of pivot-side context. The first method takes advantage of document-level context by assuming that the bridged phrase pairs should be similar in the document-level topic distributions. The second method focuses on the effect of local context. Central to this approach are that the phrase sense can be reflected by local context in the form of probabilistic topics, and that bridged phrase pairs should be compatible in the latent sense distributions. Then, we build an interpolated model bringing the above methods together to further enhance the system performance. Experimental results on French-Spanish and French-German translations using English as the pivot language demonstrate the effectiveness of topic-based context in pivot-based SMT.

Key words

Natural language processing Pivot-based statistical machine translation Topical context information 

CLC number

TP391.1 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bertoldi, N., Federico, M., 2009. Domain adaptation for statistical machine translation with monolingual resources. Proc. 4th Workshop on Statistical Machine Translation, p.182–189. [doi:10.3115/1626431.1626468]Google Scholar
  2. Bertoldi, N., Barbaiani, M., Federico, M., et al., 2008. Phrase-based statistical machine translation with pivot languages. Proc. Int. Workshop on Spoken Language Translation, p.143–149.Google Scholar
  3. Blei, D.M., Ng, A.Y., Jordan, M.I., 2003. Latent Dirichlet allocation. J. Mach. Learn. Res., 3:993–1022.zbMATHGoogle Scholar
  4. Borin, L., 2000. You’ll take the high road and I’ll take the low road: using a third language to improve bilingual word alignment. Proc. 18th Conf. on Computational Linguistics, p.97–103. [doi:10.3115/990820.990835]Google Scholar
  5. Callison-Burch, C., Koehn, P., Osborne, M., 2006. Improved statistical machine translation using paraphrases. Proc. Main Conf. on Human Language Technology Conf. of the North American Chapter of the Association of Computational Linguistics, p.17–24. [doi:10.3115/1220835.1220838]Google Scholar
  6. Chen, B.X., Foster, G., Kuhn, R., 2010. Bilingual sense similarity for statistical machine translation. Proc. 48th Annual Meeting of the Association for Computational Linguistics, p.834–843.Google Scholar
  7. Clark, J.H., Dyer, C., Lavie, A., et al., 2011. Better hypothesis testing for statistical machine translation: controlling for optimizer instability. Proc. 49th Annual Meeting of the Association for Computational Linguistics, p.176–181.Google Scholar
  8. Cohn, T., Lapata, M., 2007. Machine translation by triangulation: making effective use of multi-parallel corpora. Proc. 45th Annual Meeting of the Association for Computational Linguistics, p.728–735.Google Scholar
  9. Costa-Jussà, M.R., Henríquez, C., Banchs, R.E., 2011. Enhancing scarce-resource language translation through pivot combinations. Proc. 5th Int. Joint Conf. on Natural Language Processing, p.1361–1365.Google Scholar
  10. Crego, J.M., Max, A., Yvon, F., 2010. Local lexical adaptation in machine translation through triangulation: SMT helping SMT. Proc. 23rd Int. Conf. on Computational Linguistics, p.232–240.Google Scholar
  11. de Gispert, A., Mariño, J.B., 2006. Catalan-English statistical machine translation without parallel corpus: bridging through Spanish. Proc. 5th Int. Conf. on Language Resources and Evaluation, p.65–68.Google Scholar
  12. Denkowski, M., Lavie, A., 2011. Meteor 1.3: automatic metric for reliable optimization and evaluation of machine translation systems. Proc. 6th Workshop on Statistical Machine Translation, p.85–91.Google Scholar
  13. Dinu, G., Lapata, M., 2010. Measuring distributional similarity in context. Proc. Conf. on Empirical Methods in Natural Language Processing, p.1162–1172.Google Scholar
  14. Filali, K., Bilmes, J., 2005. Leveraging multiple languages to improve statistical MT word alignments. Proc. IEEE Automatic Speech Recognition and Understanding Workshop, p.92–97.Google Scholar
  15. Gong, Z.X., Zhou, G.D., Li, L.Y., 2011. Improve SMT with source-side “topic-document” distributions. Proc. 13th Machine Translation Summit, p.496–502.Google Scholar
  16. Griffiths, T.L., Steyvers, M., 2004. Finding scientific topics. PNAS, p.90–95.Google Scholar
  17. Habash, N., Hu, J., 2009. Improving Arabic-Chinese statistical machine translation using English as pivot language. Proc. 4th Workshop on Statistical Machine Translation, p.173–181.Google Scholar
  18. He, Z.J., Liu, Q., Lin, S.X., 2008. Improving statistical machine translation using lexicalized rule selection. Proc. 22nd Int. Conf. on Computational Linguistics, p.321–328.Google Scholar
  19. Hildebrand, A.S., Eck, M., Vogel, S., et al., 2005. Adaptation of the translation model for statistical machine translation based on information retrieval. EAMT 10th Annual Conf., p.133–142.Google Scholar
  20. Huck, M., Ney, H., 2012. Pivot lightly-supervised training for statistical machine translation. Proc. 10th Conf. of the Association for Machine Translation in the Americas, p.50–57.Google Scholar
  21. Khalilov, M., Costa-Jussà, M.R., Henríquez, C.A., et al., 2008. The TALP&I2R SMT sytstems for IWSLT 2008. Proc. Int. Workshop on Spoken Language Translation, p.116–123.Google Scholar
  22. Koehn, P., 2004. Statistical significance tests for machine translation evaluation. Proc. Conf. on Empirical Methods in Natural Language Processing, p.388–395.Google Scholar
  23. Koehn, P., Och, F.J., Marcu, D., 2003. Statistical phrasebased translation. Proc. Conf. of the North American Chapter of the Association for Computational Linguistics, p.48–54. [doi:10.3115/1073445.1073462]Google Scholar
  24. Kumar, S., Och, F.J., Macherey, W., 2007. Improving word alignment with bridge languages. Proc. Joint Conf. on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, p.42–50.Google Scholar
  25. Mauser, A., Hasan, S., Ney, H., 2009. Extending statistical machine translation with discriminative and trigger-based lexicon models. Proc. Conf. on Empirical Methods in Natural Language Processing, p.210–218.Google Scholar
  26. Och, F.J., 2003. Minimum error rate training in statistical machine translation. Proc. 41st Annual Meeting on Association for Computational Linguistics, p.160–167. [doi:10.3115/1075096.1075117]Google Scholar
  27. Och, F.J., Ney, H., 2003. A systematic comparison of various statistical alignment models. Comput. Linguist., 29(1):19–51. [doi:10.1162/089120103321337421]zbMATHCrossRefGoogle Scholar
  28. Papineni, K., Roukos, S., Ward, T., et al., 2002. BLEU: a method for automatic evaluation of machine translation. Proc. 40th Annual Meeting on Association for Computational Linguistics, p.311–318. [doi:10.3115/1073083.1073135]Google Scholar
  29. Paul, M., Yamamoto, H., Sumita, E., et al., 2009. On the importance of pivot language selection for statistical machine translation. Proc. Annual Conf. of the North American Chapter of the Association for Computational Linguistics, p.221–224.Google Scholar
  30. Ruiz, N., Federico, M., 2011. Topic adaptation for lecture translation through bilingual latent semantic models. Proc. 6th Workshop on Statistical Machine Translation, p.294–302.Google Scholar
  31. Schwenk, H., 2008. Investigations on large-scale lightlysupervised training for statistical machine translation. Proc. Int. Workshop on Spoken Language Translation, p.182–189.Google Scholar
  32. Shen, L.B., Xu, J.X., Zhang, B., et al., 2009. Effective use of linguistic and contextual information for statistical machine translation. Proc. Conf. on Empirical Methods in Natural Language Processing, p.72–80.Google Scholar
  33. Stolcke, A., 2002. SRILM — an extensible language modeling toolkit. Proc. 7th Int. Conf. on Spoken Language Processing, p.901–904.Google Scholar
  34. Su, J.S., Wu, H., Wang, H.F., et al., 2012. Translation model adaptation for statistical machine translation with monolingual topic information. Proc. 50th Annual Meeting of the Association for Computational Linguistics, p.459–468.Google Scholar
  35. Tam, Y.C., Lane, I., Schultz, T., 2007. Bilingual LSA-based adaptation for statistical machine translation. Mach. Transl., 21(4):187–207. [doi:10.1007/s10590-008-9045-2]CrossRefGoogle Scholar
  36. Tanaka, R., Murakami, Y., Ishida, T., 2009. Context-based approach for pivot translation services. Proc. 21st Int. Joint Conf. on Artificial Intelligence, p.1555–1561.Google Scholar
  37. Ueffing, N., Haffari, G., Sarkar, A., 2007. Semi-supervised model adaptation for statistical machine translation. Mach. Transl., 21(2):77–94. [doi:10.1007/s10590-008-9036-3]CrossRefGoogle Scholar
  38. Utiyama, M., Isahara, H., 2007. A comparison of pivot methods for phrase-based statistical machine translation. Proc. Annual Conf. of the North American Chapter of the Association for Computational Linguistics, p.484–491.Google Scholar
  39. Wang, H.F., Wu, H., Liu, Z.Y., 2006. Word alignment for languages with scarce resources using bilingual corpora of other language pairs. Proc. 21st Int. Conf. on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, p.874–881.Google Scholar
  40. Wu, H., Wang, H.F., 2007. Pivot language approach for phrase-based statistical machine translation. Mach. Transl., 21(3):165–181. [doi:10.1007/s10590-008-9041-6]CrossRefGoogle Scholar
  41. Wu, H., Wang, H.F., 2009. Revisiting pivot language approach for machine translation. Proc. Joint Conf. of the 47th Annual Meeting of the Association for Computational Linguistics and the 4th Int. Joint Conf. on Natural Language Processing, p.154–162.Google Scholar
  42. Xiao, X.Y., Xiong, D.Y., Zhang, M., et al., 2012. A topic similarity model for hierarchical phrase-based translation. Proc. 50th Annual Meeting of the Association for Computational Linguistics, p.750–758.Google Scholar
  43. Zhang, Y., Vogel, S., Waibel, A., 2004. Interpreting BLEU/NIST scores: how much improvement do we need to have a better system? Proc. 4th Int. Conf. on Language Resources and Evaluation, p.2051–2054.Google Scholar
  44. Zhao, B., Xing, E.P., 2006. BiTAM: bilingual topic AdMixture models for word alignment. Proc. 21st Int. Conf. on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, p.969–976.Google Scholar
  45. Zhao, B., Xing, E.P., 2007. HM-BiTAM: bilingual topic exploration, word alignment, and translation. Proc. Advances in Neural Information Processing Systems, p.1689–1696.Google Scholar

Copyright information

© Journal of Zhejiang University Science Editorial Office and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Jin-song Su
    • 1
    • 2
  • Xiao-dong Shi
    • 3
  • Yan-zhou Huang
    • 3
  • Yang Liu
    • 4
  • Qing-qiang Wu
    • 1
    • 2
  • Yi-dong Chen
    • 3
  • Huai-lin Dong
    • 1
  1. 1.Software SchoolXiamen UniversityXiamenChina
  2. 2.Center for Digital Media ComputingXiamen UniversityXiamenChina
  3. 3.Cognitive Science DepartmentXiamen UniversityXiamenChina
  4. 4.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina

Personalised recommendations