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Recovering Word Forms by Context for Morphologically Rich Languages

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In this work, we focus on “sentence-level unlemmatization,” the task of generating a grammatical sentence given a lemmatized one; this task is usually easy to do for humans but may present problems for machine learning models. We treat this setting as a machine translation problem and, as a first try, apply a sequence-to-sequence model to the texts of Russian Wikipedia articles, evaluate the effect of the different training sets sizes quantitatively and achieve the BLUE score of 67, 3 using the largest training set available. We discuss preliminary results and flaws of traditional machine translation evaluation methods for this task and suggest directions for future research.

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Authors and Affiliations

  1. St.Petersburg Department of Steklov Mathematical Institute RAS, St. Petersburg, Russia

    A. M. Alekseev & S. I. Nikolenko

  2. St.Petersburg State University, St. Petersburg, Russia

    S. I. Nikolenko

Authors
  1. A. M. Alekseev
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  2. S. I. Nikolenko
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Correspondence to A. M. Alekseev.

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Published in Zapiski Nauchnykh Seminarov POMI, Vol. 499, 2021, pp. 129–136.

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Alekseev, A.M., Nikolenko, S.I. Recovering Word Forms by Context for Morphologically Rich Languages. J Math Sci 273, 527–532 (2023). https://doi.org/10.1007/s10958-023-06518-7

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  • DOI: https://doi.org/10.1007/s10958-023-06518-7

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