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Training, Enhancing, Evaluating and Using MT Systems with Comparable Data

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Using Comparable Corpora for Under-Resourced Areas of Machine Translation

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Abstract

This chapter describes how semi-parallel and parallel data extracted from comparable corpora can be used in enhancing machine translation (MT) systems: what are the methods used for this task in statistical and rule-based machine translation systems; what kinds of showcases exist that illustrate the usage of such enhanced MT systems. The impact of data extracted from comparable corpora on MT quality is evaluated for 17 language pairs, and detailed studies involving human evaluation are carried out for 11 language pairs. At first, baseline statistical machine translation (SMT) systems were built using traditional SMT techniques. Then they were improved by the integration of additional data extracted from the comparable corpora. Comparative evaluation was performed to measure improvements. Comparable corpora were also used to enrich the linguistic knowledge of rule-based machine translation (RBMT) systems by applying terminology extraction technology. Finally, SMT systems were adjusted for a narrow domain and included domain-specific knowledge such as terminology, named entities (NEs), domain-specific language models (LMs), etc.

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Notes

  1. 1.

    http://www.setimes.com

  2. 2.

    The News Commentary corpus is from the training data released for the shared tasks of the last few workshops for statistical machine translation (SMT).

  3. 3.

    Apache OpenNLP (available at: http://opennlp.apache.org/).

  4. 4.

    EuroTermBank (http://www.eurotermbank.com/).

  5. 5.

    WordPress: http:www.wordpress.com

  6. 6.

    Blogger: http://www.blogger.com/

  7. 7.

    Twitter: http://twitter.com

  8. 8.

    Tumblr: http://www.tumblr.com

  9. 9.

    MediaWiki: http://www.mediawiki.org/

  10. 10.

    https://www.tumblr.com/about, accessed in January, 2016.

  11. 11.

    https://about.twitter.com/company, all numbers approximate as of September 30, 2015.

  12. 12.

    http://stats.wikimedia.org/EN/ReportCardTopWikis.htm, accessed in January, 2016.

  13. 13.

    Translatewiki project: http://translatewiki.net/wiki/

  14. 14.

    Translate Toolkit & Pootle: http://translate.sourceforge.net/wiki/

  15. 15.

    Yandex Translate: http://company.yandex.com/technologies/translation.xml

  16. 16.

    https://meta.wikimedia.org/wiki/Machine_translation

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Author information

Authors and Affiliations

  1. University of Leeds, Leeds, UK

    Bogdan Babych

  2. Deutsche Forschungszentrum für Künstliche Intelligenz GmbH (DFKI), Saarbrücken, Germany

    Yu Chen, Andreas Eisele, Sabine Hunsicker & Xiaojun Zhang

  3. Tilde, Riga, Latvia

    Mārcis Pinnis, Inguna Skadiņa, Raivis Skadiņš & Andrejs Vasiļjevs

  4. Linguatec, München, Germany

    Gregor Thurmair

  5. Zemanta, Ljubljana, Slovenia

    Mateja Verlic

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  1. Bogdan Babych
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Correspondence to Inguna Skadiņa .

Editor information

Editors and Affiliations

  1. Tilde, Riga, Latvia

    Inguna Skadiņa

  2. Department of Computer Science, University of Sheffield, Sheffield, UK

    Robert Gaizauskas

  3. School of Modern Languages & Cultures, University of Leeds, Leeds, UK

    Bogdan Babych

  4. Faculty of Humanities & Social Sciences, University of Zagreb, Zagreb, Croatia

    Nikola Ljubešić

  5. Institute for Artificial Intelligence, Romanian Academy, Bucharest, Romania

    Dan Tufiş

  6. Tilde , Riga, Latvia

    Andrejs Vasiļjevs

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Babych, B. et al. (2019). Training, Enhancing, Evaluating and Using MT Systems with Comparable Data. In: Skadiņa, I., Gaizauskas, R., Babych, B., Ljubešić, N., Tufiş, D., Vasiļjevs, A. (eds) Using Comparable Corpora for Under-Resourced Areas of Machine Translation. Theory and Applications of Natural Language Processing. Springer, Cham. https://doi.org/10.1007/978-3-319-99004-0_6

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