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Putting the Horses Before the Cart: Identifying Multiword Expressions Before Translation

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Computational and Corpus-Based Phraseology (EUROPHRAS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10596))

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Abstract

Translating multiword expressions (MWEs) is notoriously difficult. Part of the challenge stems from the analysis of non-compositional expressions in source texts, preventing literal translation. Therefore, before translating them, it is crucial to locate MWEs in the source text. We would be putting the cart before the horses if we tried to translate MWEs before ensuring that they are correctly identified in the source text. This paper discusses the current state of affairs in automatic MWE identification, covering rule-based methods and sequence taggers. While MWE identification is not a solved problem, significant advances have been made in the recent years. Hence, we can hope that MWE identification can be integrated into MT in the near future, thus avoiding clumsy translations that have often been mocked and used to motivate the urgent need for better MWE processing.

I would like to thank the chairs of MUMTTT 2017 for inviting me to the event and for giving me the oportunity to publish this invited contribution. This paper includes materials published in other venues and co-written with: Mathieu Constant, Silvio Cordeiro, Benoit Favre, Marco Idiart, Gülşen Eryiğit, Johanna Monti, Lonneke van der Plas, Michael Rosner, Manon Scholivet, Amalia Todirascu, and Aline Villavicencio. Work reported here has been partly funded by projects PARSEME (Cost Action IC1207), PARSEME-FR (ANR-14-CERA-0001), and AIM-WEST (FAPERGS-INRIA 1706-2551/13-7).

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Notes

  1. 1.

    Translations obtained using Google’s online translation service (http://translate.google.com) on September 6, 2017.

  2. 2.

    http://multiword.sf.net/.

  3. 3.

    http://dimsum16.github.io.

  4. 4.

    http://multiword.sf.net/sharedtask2017.

  5. 5.

    http://mwetoolkit.sf.net/.

  6. 6.

    In this toy example, the “lexicon” is formed by abstract POS patterns. In our implementation, lexicons can contain lemmas, surface forms, POS patterns or a mix of all these.

  7. 7.

    In the remainder of the paper, we abbreviate the POS tag NOUN as N.

  8. 8.

    The 13 most frequent non-literal particles: about, around, away, back, down, in, into, off, on, out, over, through, up.

  9. 9.

    B is used for a token that appears at the Beginning of an MWE, I is used for a token Included in the MWE, and O for tokens Outside any MWE.

  10. 10.

    http://www.chokkan.org/software/crfsuite/.

  11. 11.

    http://bach.arts.kuleuven.be/dicovalence/.

  12. 12.

    t\(_{\text {i}}\) is a shortcut denoting the group of features w\(_{\text {i}}\), l\(_{\text {i}}\) and p\(_{\text {i}}\) for a token t\(_{\text {i}}\). In other words, each token t\(_{\text {i}}\) is a tuple (w\(_{\text {i}}\),l\(_{\text {i}}\),p\(_{\text {i}}\)). The same applies to n-grams.

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  1. Aix Marseille Univ, CNRS, LIF, Marseille, France

    Carlos Ramisch

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  1. University of Wolverhampton, Wolverhampton, United Kingdom

    Ruslan Mitkov

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Ramisch, C. (2017). Putting the Horses Before the Cart: Identifying Multiword Expressions Before Translation. In: Mitkov, R. (eds) Computational and Corpus-Based Phraseology. EUROPHRAS 2017. Lecture Notes in Computer Science(), vol 10596. Springer, Cham. https://doi.org/10.1007/978-3-319-69805-2_6

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