Skip to main content
Springer Nature Link
Log in

Context based machine translation with recurrent neural network for English–Amharic translation

  • Published:
Machine Translation

Abstract

Context-aware machine translation approaches improve the quality of translation by incorporating the context of the surrounding phrases in the translation of a phrase. So far, for the low-resource language pair English-Amharic, context-aware machine translation approaches have not been investigated in depth. Moreover, the current approaches for machine translation of the low-resource language pair English-Amharic usually require a large set of parallel corpus to achieve fluency. This research investigates a new approach that translates English text to Amharic text using a combination of context based machine translation (CBMT) and a recurrent neural network machine translation (RNNMT). We built a bilingual dictionary for the CBMT to use along with a target corpus. The RNNMT model is then provided with the output of the CBMT and a parallel corpus for training. The approach is evaluated using the New Testament Bible as a corpus. Our combinational approach on English–Amharic language pair yields a performance improvement over the simple neural machine translation (NMT), while no improvement is seen over CBMT for a small dataset. We have also assessed the impact of the dictionary used by CBMT on the overall performance of the approach. The result shows that the dictionary accuracy, and hence, the CBMT output is found to affect the combinational approach.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Output 1
Output 2
Output 3
Output 4
Output 5
Fig. 3

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. www.bible.com.

References

  • Abate S, Woldeyohannis M, Tachbelie M, Meshesha M, Atnafu S, Gewe W, Assabie Y, Abera H, Seyoum B, Abebe T, Tsegaye W, Lemma A, Andargie T, Shifaw S (2018) Parallel corpora for bi-lingual English–Ethiopian languages statistical machine translation. In: Proceedings of the 27th international conference on computational linguistics, Santa Fe, New Mexico, USA, pp 3102–3111

  • Artetxe M, Labaka G, Agirre E, Cho K (2018) Unsupervised neural machine translation. In: International conference on learning representations

  • Brown PF, Cocke J, Della Pietra SA, Della Pietra VJ, Jelinek F, Lafferty JD, Mercer RL, Roossin PS (1990) A statistical approach to machine translation. Comput Linguist 16(2):79–85

    Google Scholar 

  • Carbonell J, Klein S, Miller DF, Steinbaum M, Grassiany T, Frey J (2006) Context-based machine translation

  • Clay C (2018) Comparing the gospels: Matthew, Mark, Luke, and John. https://owlcation.com/humanities/Comparing-the-Gospels-Matthew-Mark-Luke-and-John

  • Dietterich TG (1998) Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput 10(7):1895–1923

    Article  Google Scholar 

  • Eberhard D, Simons G, Fennig C (2020) Ethnologue: languages of the world. http://www.ethnologue.com, online version

  • Gangadharaiah R (2011) Coping with data-sparsity in example-based machine translation. PhD thesis, USA

  • Gasser M (2012) Toward a rule-based system for English–Amharic translation

  • Gu J, Hassan H, Devlin J, Li V (2018) Universal neural machine translation for extremely low resource languages. pp 344–354. https://doi.org/10.18653/v1/N18-1032

  • Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–80. https://doi.org/10.1162/neco.1997.9.8.1735

    Article  Google Scholar 

  • Kane TL (1974) Arabic translations into Amharic. Bulletin of the School of Oriental and African Studies, University of London 37(3):608–627. http://www.jstor.org/stable/613803

  • Kishore P, Salim R, Todd W, Wei-Jing Z (2002) Bleu: a method for automatic evaluation of machine translation. pp 311–318

  • Labaka G, España-Bonet C, Màrquez L, Sarasola K (2014) A hybrid machine translation architecture guided by syntax. Mach Transl 28(2):91–125

    Article  Google Scholar 

  • Luong MT, Pham H, Manning C (2015) Effective approaches to attention-based neural machine translation. pp 1412–1421. https://doi.org/10.18653/v1/D15-1166

  • Niehues J, Cho E, Ha TL, Waibel AH (2016) Pre-translation for neural machine translation. In: COLING

  • Oladosu J, Esan A, Adeyanju I, Adegoke B, Olaniyan O, Omodunbi B (2016) Approaches to machine translation: a review. FUOYE J Eng Technol 1:120–126

    Article  Google Scholar 

  • Popovic M (2017) Comparing language related issues for nmt and pbmt between German and English. The Prague Bulletin of Mathematical Linguistics 108. https://doi.org/10.1515/pralin-2017-0021

  • Raschka S (2018) Model evaluation, model selection, and algorithm selection in machine learning

  • Sennrich R, Haddow B, Birch A (2016) Neural machine translation of rare words with subword units. In: Proceedings of the 54th annual meeting of the association for computational linguistics (Volume 1: Long Papers), Association for Computational Linguistics, Berlin, Germany, pp 1715–1725. https://doi.org/10.18653/v1/P16-1162, https://www.aclweb.org/anthology/P16-1162

  • Soergel D (1998) WordNet. An electronic lexical database. MIT, Cambridge

    MATH  Google Scholar 

  • Tadesse A, Mekuria Y (2000) English to Amharic machine translation using smt. Master’s thesis, Addis Ababa University

  • Teshome E (2000) Bidirectional English–Amharic machine translation: an experiment using constrained corpus. Master’s thesis, Addis Ababa University

  • Teshome M, Besacier L, Taye G, Teferi D (2015) Phoneme-based English–Amharic statistical machine translation. pp 1–5. https://doi.org/10.1109/AFRCON.2015.7331921

  • Woldeyohannis M, Besacier L, Meshesha M (2016) Amharic speech recognition for speech translation

  • Wu Y, Schuster M, Chen Z, Le QV, Norouzi M, Macherey W, Krikun M, Cao Y, Gao Q, Macherey K, Klingner J, Shah A, Johnson M, Liu X, Kaiser L, Gouws S, Kato Y, Kudo T, Kazawa H, Stevens K, Kurian G, Patil N, Wang W, Young C, Smith J, Riesa J, Rudnick A, Vinyals O, Corrado GS, Hughes M, Dean J (2016) Google’s neural machine translation system: bridging the gap between human and machine translation. ArXiv abs/1609.08144

  • Yulianti E, Budi I, Hidayanto A, Manurung R, Adriani M (2011) Developing Indonesian–English hybrid machine translation system. pp 265–270

  • Zewgneh S (2017) English–Amharic document translation using hybrid approach. Master’s thesis, Addis Ababa University

  • Zhou L, hu W, Zhang J, Zong C (2017) Neural system combination for machine translation. In: Proceedings of the 55th annual meeting of the association for computational linguistics (Volume 2: Short Papers), Association for Computational Linguistics, pp 378–384. https://doi.org/10.18653/v1/P17-2060, https://www.aclweb.org/anthology/P17-2060

  • Zoph B, Knight K (2016) Multi-source neural translation. In: Proceedings of the 2016 conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics, San Diego, California, pp 30–34. https://doi.org/10.18653/v1/N16-1004, https://www.aclweb.org/anthology/N16-1004

Download references

Author information

Authors and Affiliations

  1. Addis Ababa Institute of Technology, Addis Ababa University, Addis Ababa, Ethiopia

    Yeabsira Asefa Ashengo & Surafel Lemma Abebe

  2. Armauer Hansen Research Institute, Addis Ababa, Ethiopia

    Rosa Tsegaye Aga

Authors
  1. Yeabsira Asefa Ashengo
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Rosa Tsegaye Aga
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Surafel Lemma Abebe
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Yeabsira Asefa Ashengo.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ashengo, Y.A., Aga, R.T. & Abebe, S.L. Context based machine translation with recurrent neural network for English–Amharic translation. Machine Translation 35, 19–36 (2021). https://doi.org/10.1007/s10590-021-09262-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10590-021-09262-4

Keywords