Skip to main content
SpringerLink
Log in

A Comparison of Character-Based Neural Machine Translations Techniques Applied to Spelling Normalization

  • Conference paper
  • First Online:
Pattern Recognition. ICPR International Workshops and Challenges (ICPR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12667))

Included in the following conference series:

  • 1841 Accesses

Abstract

The lack of spelling conventions and the natural evolution of human language create a linguistic barrier inherent in historical documents. This barrier has always been a concern for scholars in humanities. In order to tackle this problem, spelling normalization aims to adapt a document’s orthography to modern standards. In this work, we evaluate several character-based neural machine translation normalization approaches—using modern documents to enrich the neural models. We evaluated these approaches on several datasets from different languages and time periods, reaching the conclusion that each approach is better suited for a different set of documents.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

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

  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. Domingo, M., Casacuberta, F.: 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.) ICIAP 2019. LNCS, vol. 11808, pp. 59–69. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30754-7_7

    Chapter  Google Scholar 

  12. Domingo, M., et al.: A user study of the incremental learning in NMT. In: Proceedings of the European Association for Machine Translation, pp. 319–328 (2020)

    Google Scholar 

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

    Google Scholar 

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

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

    Article  Google Scholar 

  16. 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 

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

  18. 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 

  19. 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 

  20. 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 

  21. 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 

  22. 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 

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

  24. 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, pp. 923–929 (2016)

    Google Scholar 

  25. 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

  26. 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 

  27. 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 

  28. 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 

  29. 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 

  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. 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 

  32. 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 

  33. 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 

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

    Article  MathSciNet  Google Scholar 

  35. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986)

    Article  Google Scholar 

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

    Google Scholar 

  37. Sennrich, R., Haddow, B., Birch, A.: Neural machine translation of rare words with subword units. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 1715–1725 (2016)

    Google Scholar 

  38. 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 

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

    Google Scholar 

  40. Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Proceedings of the Advances in Neural Information Processing Systems, vol. 27, pp. 3104–3112 (2014)

    Google Scholar 

  41. 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 

  42. 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 

  43. 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 

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

    Google Scholar 

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

  46. 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 

Download references

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 Valenciana (2014–2020) under project IDIFEDER/2018/025; from Ministerio de Economía y Competitividad under project PGC2018-096212-B-C31; and from Generalitat Valenciana (GVA) under project PROMETEO/2019/121.We gratefully acknowledge the support of NVIDIA Corporation with the donation of a GPU used for part of this research.

Author information

Authors and Affiliations

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

    Miguel Domingo & Francisco Casacuberta

Authors
  1. Miguel Domingo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Francisco Casacuberta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Miguel Domingo .

Editor information

Editors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy

    Alberto Del Bimbo

  2. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Rita Cucchiara

  3. Department of Computer Science, Boston University, Boston, MA, USA

    Stan Sclaroff

  4. Dipartimento di Matematica e Informatica, University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Cloud & AI, JD.COM, Beijing, China

    Tao Mei

  6. Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy

    Marco Bertini

  7. Computational Sciences Department, National Institute of Astrophysics, Optics and Electronics (INAOE), Tonantzintla, Puebla, Mexico

    Hugo Jair Escalante

  8. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Roberto Vezzani

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Domingo, M., Casacuberta, F. (2021). A Comparison of Character-Based Neural Machine Translations Techniques Applied to Spelling Normalization. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12667. Springer, Cham. https://doi.org/10.1007/978-3-030-68787-8_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-68787-8_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-68786-1

  • Online ISBN: 978-3-030-68787-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation