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        <title>Morphological reinflection with weighted finite-state transducers</title>
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        <namePart type="given">Alice</namePart>
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        <namePart type="given">Michael</namePart>
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    <name type="personal">
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            <title>Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology</title>
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            <namePart type="given">Garrett</namePart>
            <namePart type="family">Nicolai</namePart>
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        <name type="personal">
            <namePart type="given">Eleanor</namePart>
            <namePart type="family">Chodroff</namePart>
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            <namePart type="given">Frederic</namePart>
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    <abstract>This paper describes the submission by the University of Arizona to the SIGMORPHON 2023 Shared Task on typologically diverse morphological (re-)infection. In our submission, we investigate the role of frequency, length, and weighted transducers in addressing the challenge of morphological reinflection. We start with the non-neural baseline provided for the task and show how some improvement can be gained by integrating length and frequency in prefix selection. We also investigate using weighted finite-state transducers, jump-started from edit distance and directly augmented with frequency. Our specific technique is promising and quite simple, but we see only modest improvements for some languages here.</abstract>
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    <identifier type="doi">10.18653/v1/2023.sigmorphon-1.15</identifier>
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        <date>2023-07</date>
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            <start>132</start>
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%0 Conference Proceedings
%T Morphological reinflection with weighted finite-state transducers
%A Kwak, Alice
%A Hammond, Michael
%A Wing, Cheyenne
%Y Nicolai, Garrett
%Y Chodroff, Eleanor
%Y Mailhot, Frederic
%Y Çöltekin, Çağrı
%S Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F kwak-etal-2023-morphological
%X This paper describes the submission by the University of Arizona to the SIGMORPHON 2023 Shared Task on typologically diverse morphological (re-)infection. In our submission, we investigate the role of frequency, length, and weighted transducers in addressing the challenge of morphological reinflection. We start with the non-neural baseline provided for the task and show how some improvement can be gained by integrating length and frequency in prefix selection. We also investigate using weighted finite-state transducers, jump-started from edit distance and directly augmented with frequency. Our specific technique is promising and quite simple, but we see only modest improvements for some languages here.
%R 10.18653/v1/2023.sigmorphon-1.15
%U https://aclanthology.org/2023.sigmorphon-1.15
%U https://doi.org/10.18653/v1/2023.sigmorphon-1.15
%P 132-137

Markdown (Informal)

[Morphological reinflection with weighted finite-state transducers](https://aclanthology.org/2023.sigmorphon-1.15) (Kwak et al., SIGMORPHON 2023)

• Morphological reinflection with weighted finite-state transducers (Kwak et al., SIGMORPHON 2023)

ACL

• Alice Kwak, Michael Hammond, and Cheyenne Wing. 2023. Morphological reinflection with weighted finite-state transducers. In Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 132–137, Toronto, Canada. Association for Computational Linguistics.