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A hybrid machine translation architecture guided by syntax

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Machine Translation

Abstract

This article presents a hybrid architecture which combines rule-based machine translation (RBMT) with phrase-based statistical machine translation (SMT). The hybrid translation system is guided by the rule-based engine. Before the transfer step, a varied set of partial candidate translations is calculated with the SMT system and used to enrich the tree-based representation with more translation alternatives. The final translation is constructed by choosing the most probable combination among the available fragments using monotone statistical decoding following the order provided by the rule-based system. We apply the hybrid model to a pair of distantly related languages, Spanish and Basque, and perform extensive experimentation on two different corpora. According to our empirical evaluation, the hybrid approach outperforms the best individual system across a varied set of automatic translation evaluation metrics. Following some output analysis to better understand the behaviour of the hybrid system, we explore the possibility of adding alternative parse trees and extra features to the hybrid decoder. Finally, we present a twofold manual evaluation of the translation systems studied in this paper, consisting of (i) a pairwise output comparison and (ii) a individual task-oriented evaluation using HTER. Interestingly, the manual evaluation shows some contradictory results with respect to the automatic evaluation; humans tend to prefer the translations from the RBMT system over the statistical and hybrid translations.

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Notes

  1. See Groves and Way (2005) for a similar, early effort to integrate SMT and Example-Based MT.

  2. A complete SMT translation of the source sentence is always available in the hybrid decoder.

  3. http://www.elhuyar.org.

  4. Note that due to the log-linear approach used in Moses, the features should be stored in the phrase-table as exponential of \(e\). The features are presented in the way the decoder will see them.

  5. http://revista.consumer.es.

  6. http://www.elhuyar.org/hizkuntza-zerbitzuak/EN/Services.

  7. http://statmt.org/europarl/.

  8. Labaka et al. (2007) observe that with respect to agglutinative languages such as Basque, “even if a morpheme-based translation is more appropriate than a word-based translation, n-gram based metrics are not suited to the comparison between sequences of morphemes”.

  9. http://nlp.lsi.upc.edu/asiya/.

  10. http://translate.google.com/, translations obtained on the 29th of April, 2013.

  11. Larger \(n\)-best lists did not improve significantly the BLEU score.

  12. Results on the out-of-domain NEWS corpus are similar and are excluded for the sake of brevity.

  13. In every run of MERT the development set is translated by a system which generates an n-best list of translations. In our case we have two systems that generate two n-best lists. These two lists are joined and sorted at every run so that the minimisation process proceeds as usual but using the translations of both systems.

  14. The same happens with the hybrids: SMatxinT\(_{(F)}\) is consistently better than the hybrid version constructed with Matxin\(_{(M)}\) (results not included in the table for brevity and clarity reasons).

  15. Note that as shown in Hearne and Way (2006), the most probable target parse tree is not necessarily the best indicator of translation quality.

  16. For the hybrid system we used the SMatxinT\(_{(F+M)}\) variant presented in Table 5.

  17. http://www.itzultzailea.euskadi.net/traductor/portalExterno/text.do.

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Acknowledgments

The authors are grateful to the anonymous reviewers of the initial version of this article for their insightful and detailed comments, which contributed significantly to improving the paper. This work has been partially funded by the Spanish Ministry of Science and Innovation (OpenMT-2 fundamental research project, TIN2009-14675-C03-01) and the European Community’s Seventh Framework Programme (FP7/2007-2013) under Grant agreement number 247914 (MOLTO project, FP7-ICT-2009-4-247914) and the European project QTLeap (FP7-ICT-2013.4.1-610516).

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Authors and Affiliations

  1. IXA Research Group, Department of Computer Languages and Systems, University of the Basque Country (UPV/EHU), Manuel de Lardizabal 1, 20018 , Donostia, Spain

    Gorka Labaka & Kepa Sarasola

  2. TALP Research Center, Department of Computer Science, Technical University of Catalonia – Barcelona Tech, Jordi Girona 1-3, 08034 , Barcelona, Spain

    Cristina España-Bonet

  3. Qatar Computing Research Institute, Qatar Foundation, Tornado Tower, Floor 10, P.O. Box 5825 , Doha, Qatar

    Lluís Màrquez

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  1. Gorka Labaka
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  2. Cristina España-Bonet
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  3. Lluís Màrquez
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  4. Kepa Sarasola
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Correspondence to Cristina España-Bonet.

Additional information

L. Màrquez: During the research period of this article, he was a member of the TALP Research Center, Department of Computer Science, Technical University of Catalonia–Barcelona Tech.

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Labaka, G., España-Bonet, C., Màrquez, L. et al. A hybrid machine translation architecture guided by syntax. Machine Translation 28, 91–125 (2014). https://doi.org/10.1007/s10590-014-9153-0

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