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A fuzzy model for NMT word alignment using quasi-perfect matching

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Abstract

In this article, first, the concept of quasi-perfect matching in a fuzzy graph is introduced. In addition to using these types of matching in expressing our main application goal, this introduction provides a complete classification on all matching that are known as “maximum matching” in classical graph theory. A useful set called conductive set has been obtained to be able to customize any of the introduced categories for matchings as desired or according to the practical necessity. Extensions with different powers are made from a fuzzy graph and useful meters are generated on each of them. These extensions and related concepts have been used in a different approach for words alignment in machine translation. The mismatch between the number of sentence words in the source and target languages has always been a challenge for the designers of machine translation systems in word-based alignment. To solve this, we introduce a different approach for aligning words, based on a fuzzy graph extracted from a parallel corpus.

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Data Availability

The authors declare that the data supporting the findings of this study are available within the paper, its supplementary information files

Notes

  1. The number of elements in the set \(E_{s}(M)\)

  2. \(G^*\setminus S\) means subtracting S from \(G^*\).

  3. [ ] represents the smallest integer operation.

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Authors and Affiliations

  1. Department of Mathematics, Chabahar Branch, Islamic Azad University, Chabahar, Iran

    M. Khalili

  2. Department of Mathematics, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran

    R. A. Borzooei

  3. Department of Mathematics, Faculty of Engineering and Natural Sciences, Istinye University, Istanbul, Turkey

    R. A. Borzooei

  4. Department of Mathematics, Tehran Central Branch, Islamic Azad University, Tehran, Iran

    D. Ebrahimibagha

Authors
  1. M. Khalili
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  2. R. A. Borzooei
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  3. D. Ebrahimibagha
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Correspondence to R. A. Borzooei.

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Communicated by Marcos Eduardo Valle.

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Khalili, M., Borzooei, R.A. & Ebrahimibagha, D. A fuzzy model for NMT word alignment using quasi-perfect matching. Comp. Appl. Math. 42, 368 (2023). https://doi.org/10.1007/s40314-023-02498-1

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  • DOI: https://doi.org/10.1007/s40314-023-02498-1

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