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Hybrid Approaches to Machine Translation pp 57–75Cite as

Hybrid Word Alignment

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Abstract

This paper proposes a hybrid word alignment model for Phrase-Based Statistical Machine Translation (PB-SMT). The proposed hybrid word alignment model provides most informative alignment links, which are offered by both unsupervised and semi-supervised word alignment models. Two unsupervised word alignment models, namely GIZA++ and Berkeley aligner, and a rule based word alignment technique are combined together. The unsupervised alignment models are trained on the surface form as well as the root form of the training data and provide alignment tables for the corresponding training data. The rule-based aligner is aimed towards aligning named entities (NEs) and syntactically motivated chunks. NEs are aligned through transliteration using a joint source-channel model. Chunks are aligned employing a bootstrapping approach by translating the source chunks into the target language using a baseline PB-SMT model and subsequently validating the chunk hypotheses using a fuzzy matching technique against the target corpus. Experiments are carried out after single-tokenizing the multiword NEs. The effectiveness of the proposed hybrid alignment model was extrinsically evaluated on the MT quality by using well-known automatic MT evaluation metrics, such as BLUE and NIST. Our best system provided significant improvements over the baseline as measured by BLEU.

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Notes

  1. 1.

    The EILMT project is funded by the Department of Electronics and Information Technology (DEITY), Ministry of Communications and Information Technology (MCIT), Government of India.

  2. 2.

    http://nlp.stanford.edu/software/lex-parser.shtml

  3. 3.

    http://crfchunker.sourceforge.net/

  4. 4.

    The IL-ILMT project is funded by the Department of Electronics and Information Technology (DEITY), Ministry of Communications and Information Technology (MCIT), Government of India.

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Acknowledgements

The research leading to these results has received funding from the EU project EXPERT –the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007-2013<tel:2007-2013>/ under REA grant agreement no. [317471].

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Authors and Affiliations

  1. Universität Des Saarlandes, Saarbrücken, Germany

    Santanu Pal

  2. Jadavpur University, Kolkata, India

    Sudip Kumar Naskar

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  1. Santanu Pal
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Correspondence to Santanu Pal .

Editor information

Editors and Affiliations

  1. Universitat politècnica de catalunya , Barcelona, Spain

    Marta R. Costa-jussà

  2. University of Aix-Marseille and University of Mainz, Marseille, France

    Reinhard Rapp

  3. Pompeu Fabra University, Barcelona, Barcelona, Spain

    Patrik Lambert

  4. Lingenio GmbH, Heidelberg, Baden-Württemberg, Germany

    Kurt Eberle

  5. Institute for Infocomm Research, Singapore, Singapur, Singapore

    Rafael E. Banchs

  6. Centre for Translation Studies, University of Leeds School of Modern Languages&Cultures, Leeds, United Kingdom

    Bogdan Babych

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Pal, S., Naskar, S.K. (2016). Hybrid Word Alignment. In: Costa-jussà, M., Rapp, R., Lambert, P., Eberle, K., Banchs, R., Babych, B. (eds) Hybrid Approaches to Machine Translation. Theory and Applications of Natural Language Processing. Springer, Cham. https://doi.org/10.1007/978-3-319-21311-8_3

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  • DOI: https://doi.org/10.1007/978-3-319-21311-8_3

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  • Online ISBN: 978-3-319-21311-8

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