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Enriching Character-Based Neural Machine Translation with Modern Documents for Achieving an Orthography Consistency in Historical Documents

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New Trends in Image Analysis and Processing – ICIAP 2019 (ICIAP 2019)

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Abstract

The nature of human language and the lack of a spelling convention make historical documents hard to handle for natural language processing. Spelling normalization tackles this problem by adapting their spelling to modern standards in order to get an orthography consistency. In this work, we compare several character-based machine translation approaches, and propose a method to profit from modern documents to enrich neural machine translation models. We tested our proposal with four different data sets, and observed that the enriched models successfully improved the normalization quality of the neural models. Statistical models, however, yielded a better result.

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References

  1. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate (2015). arXiv:1409.0473

  2. Baron, A., Rayson, P.: VARD2: a tool for dealing with spelling variation in historical corpora. In: Postgraduate Conference in Corpus Linguistics (2008)

    Google Scholar 

  3. Bollmann, M.: Normalization of historical texts with neural network models. Ph.D. thesis, Sprachwissenschaftliches Institut, Ruhr-Universität (2018)

    Google Scholar 

  4. Bollmann, M., Søgaard, A.: Improving historical spelling normalization with bi-directional LSTMs and multi-task learning. In: Proceedings of the International Conference on the Computational Linguistics, pp. 131–139 (2016)

    Google Scholar 

  5. Brown, P.F., Pietra, V.J.D., Pietra, S.A.D., Mercer, R.L.: The mathematics of statistical machine translation: parameter estimation. Comput. Linguist. 19(2), 263–311 (1993)

    Google Scholar 

  6. Chatterjee, R., Farajian, M.A., Negri, M., Turchi, M., Srivastava, A., Pal, S.: Multi-source neural automatic post-editing: FBK’s participation in the WMT 2017 ape shared task. In: Proceedings of the Second Conference on Machine Translation, pp. 630–638 (2017)

    Google Scholar 

  7. Chung, J., Cho, K., Bengio, Y.: A character-level decoder without explicit segmentation for neural machine translation. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 1693–1703 (2016)

    Google Scholar 

  8. Costa-Jussà, M.R., Aldón, D., Fonollosa, J.A.: Chinese-Spanish neural machine translation enhanced with character and word bitmap fonts. Mach. Transl. 31, 35–47 (2017)

    Article  Google Scholar 

  9. Costa-Jussà, M.R., Fonollosa, J.A.: Character-based neural machine translation. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 357–361 (2016)

    Google Scholar 

  10. Domingo, M., Casacuberta, F.: Spelling normalization of historical documents by using a machine translation approach. In: Proceedings of the Annual Conference of the European Association for Machine Translation, pp. 129–137 (2018)

    Google Scholar 

  11. Jehle, F.: Works of Miguel de Cervantes in Old- and Modern-Spelling. Indiana University Purdue University Fort Wayne (2001)

    Google Scholar 

  12. Gao, Q., Vogel, S.: Parallel implementations of word alignment tool. In: Proceedings of the Association for Computational Linguistics Software Engineering, Testing, and Quality Assurance Workshop, pp. 49–57 (2008)

    Google Scholar 

  13. Gehring, J., Auli, M., Grangier, D., Yarats, D., Dauphin, Y.N.: Convolutional sequence to sequence learning (2017). arXiv:1705.03122

  14. Gers, F.A., Schmidhuber, J., Cummins, F.: Learning to forget: continual prediction with LSTM. Neural Comput. 12(10), 2451–2471 (2000)

    Article  Google Scholar 

  15. Hämäläinen, M., Säily, T., Rueter, J., Tiedemann, J., Mäkelä, E.: Normalizing early English letters to present-day English spelling. In: Proceedings of the Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pp. 87–96 (2018)

    Google Scholar 

  16. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  17. Klein, G., Kim, Y., Deng, Y., Senellart, J., Rush, A.M.: OpenNMT: open-source toolkit for neural machine translation. In: Proceedings of the Association for Computational Linguistics: System Demonstration, pp. 67–72 (2017)

    Google Scholar 

  18. Koehn, P., et al.: Moses: open source toolkit for statistical machine translation. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 177–180 (2007)

    Google Scholar 

  19. Koehn, P., Och, F.J., Marcu, D.: Statistical phrase-based translation. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, pp. 48–54 (2003)

    Google Scholar 

  20. Korchagina, N.: Normalizing medieval German texts: from rules to deep learning. In: Proceedings of the Nordic Conference on Computational Linguistics Workshop on Processing Historical Language, pp. 12–17 (2017)

    Google Scholar 

  21. Laing, M.: The linguistic analysis of medieval vernacular texts: Two projects at Edinburgh’. In: Rissanen, M., Kytd, M., Wright, S. (eds.) Corpora across the Centuries: Proceedings of the First International Colloquium on English Diachronic Corpora, vol. 25427, pp. 121–141. St Catharine’s College Cambridge (1993)

    Google Scholar 

  22. Ling, W., Trancoso, I., Dyer, C., Black, A.W.: Character-based neural machine translation. arXiv preprint arXiv:1511.04586 (2015)

  23. Lison, P., Tiedemann, J.: OpenSubtitles 2016: extracting large parallel corpora from movie and TV subtitles. In: Proceedings of the International Conference on Language Resources Association (2016)

    Google Scholar 

  24. Ljubešić, N., Zupan, K., Fišer, D., Erjavec, T.: Dataset of normalised Slovene text KonvNormSl 1.0. Slovenian language resource repository CLARIN.SI (2016). http://hdl.handle.net/11356/1068

  25. Ljubešic, N., Zupan, K., Fišer, D., Erjavec, T.: Normalising slovene data: historical texts vs. user-generated content. In: Proceedings of the Conference on Natural Language Processing, pp. 146–155 (2016)

    Google Scholar 

  26. Nakov, P., Tiedemann, J.: Combining word-level and character-level models for machine translation between closely-related languages. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 301–305 (2012)

    Google Scholar 

  27. Och, F.J.: Minimum error rate training in statistical machine translation. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 160–167 (2003)

    Google Scholar 

  28. Och, F.J., Ney, H.: Discriminative training and maximum entropy models for statistical machine translation. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 295–302 (2002)

    Google Scholar 

  29. Och, F.J., Ney, H.: A systematic comparison of various statistical alignment models. Comput. Linguist. 29(1), 19–51 (2003)

    Article  Google Scholar 

  30. Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002)

    Google Scholar 

  31. Poncelas, A., Shterionov, D., Way, A., Maillette de Buy Wenniger, G., Passban, P.: Investigation backtranslation in neural machine translation. In: Proceedings of the Annual Conference of the European Association for Machine Translation, pp. 249–258 (2018)

    Google Scholar 

  32. Porta, J., Sancho, J.L., Gómez, J.: Edit transducers for spelling variation in old Spanish. In: Proceedings of the Workshop on Computational Historical Linguistics, pp. 70–79 (2013)

    Google Scholar 

  33. Post, M.: A call for clarity in reporting BLEU scores. In: Proceedings of the Third Conference on Machine Translation, pp. 186–191 (2018)

    Google Scholar 

  34. Riezler, S., Maxwell, J.T.: On some pitfalls in automatic evaluation and significance testing for MT. In: Proceedings of the Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization, pp. 57–64 (2005)

    Google Scholar 

  35. Robbins, H., Monro, S.: A stochastic approximation method. Ann. Math. Stat. 22, 400–407 (1951)

    Article  MathSciNet  Google Scholar 

  36. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533 (1986)

    Article  Google Scholar 

  37. Scherrer, Y., Erjavec, T.: Modernizing historical Slovene words with character-based SMT. In: Proceedings of the Biennial International Workshop on Balto-Slavic Natural Language Processing, pp. 58–62 (2013)

    Google Scholar 

  38. Sennrich, R., Haddow, B., Birch, A.: Improving neural machine translation models with monolingual data. arXiv preprint arXiv:1511.06709 (2015)

  39. Snover, M., Dorr, B., Schwartz, R., Micciulla, L., Makhoul, J.: A study of translation edit rate with targeted human annotation. In: Proceedings of the Association for Machine Translation in the Americas, pp. 223–231 (2006)

    Google Scholar 

  40. Stolcke, A.: SRILM - an extensible language modeling toolkit. In: Proceedings of the International Conference on Spoken Language Processing, pp. 257–286 (2002)

    Google Scholar 

  41. Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Proc. Adv. Neural Inf. Process. Syst. 27, 3104–3112 (2014)

    Google Scholar 

  42. Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)

    Google Scholar 

  43. Tang, G., Cap, F., Pettersson, E., Nivre, J.: An evaluation of neural machine translation models on historical spelling normalization. In: Proceedings of the International Conference on Computational Linguistics, pp. 1320–1331 (2018)

    Google Scholar 

  44. Tiedemann, J.: Character-based PSMT for closely related languages. In: Proceedings of the Annual Conference of the European Association for Machine Translation, pp. 12–19 (2009)

    Google Scholar 

  45. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)

    Google Scholar 

  46. Wu, Y., et al.: Google’s neural machine translation system: bridging the gap between human and machine translation (2016). arXiv:1609.08144

  47. Zens, R., Och, F.J., Ney, H.: Phrase-based statistical machine translation. In: Jarke, M., Lakemeyer, G., Koehler, J. (eds.) KI 2002. LNCS (LNAI), vol. 2479, pp. 18–32. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45751-8_2

    Chapter  Google Scholar 

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Acknowledgments

The research leading to these results has received funding from the European Union through Programa Operativo del Fondo Europeo de Desarrollo Regional (FEDER) from Comunitat Valencia (2014–2020) under project Sistemas de frabricación inteligentes para la indústria 4.0 (grant agreement IDIFEDER/2018/025); and from Ministerio de Economía y Competitividad (MINECO) under project MISMIS-FAKEnHATE (grant agreement PGC2018-096212-B-C31). We gratefully acknowledge the support of NVIDIA Corporation with the donation of a GPU used for part of this research.

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Authors and Affiliations

  1. PRHLT Research Center, Universitat Politècnica de València, Valencia, Spain

    Miguel Domingo & Francisco Casacuberta

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  1. Miguel Domingo
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  2. Francisco Casacuberta
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Correspondence to Miguel Domingo .

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Editors and Affiliations

  1. University of Verona, Verona, Italy

    Marco Cristani

  2. University of Parma, Parma, Italy

    Andrea Prati

  3. Fondazione Bruno Kessler, Povo, Italy

    Oswald Lanz

  4. Fondazione Bruno Kessler, Povo, Italy

    Stefano Messelodi

  5. University of Trento, Povo, Italy

    Nicu Sebe

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Domingo, M., Casacuberta, F. (2019). Enriching Character-Based Neural Machine Translation with Modern Documents for Achieving an Orthography Consistency in Historical Documents. In: Cristani, M., Prati, A., Lanz, O., Messelodi, S., Sebe, N. (eds) New Trends in Image Analysis and Processing – ICIAP 2019. ICIAP 2019. Lecture Notes in Computer Science(), vol 11808. Springer, Cham. https://doi.org/10.1007/978-3-030-30754-7_7

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  • DOI: https://doi.org/10.1007/978-3-030-30754-7_7

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  • Online ISBN: 978-3-030-30754-7

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