Linguist vs. Machine: Rapid Development of Finite-State Morphological Grammars

Sarah Beemer, Zak Boston, April Bukoski, Daniel Chen, Princess Dickens, Andrew Gerlach, Torin Hopkins, Parth Anand Jawale, Chris Koski, Akanksha Malhotra, Saliha Muradoglu

    Research output: Contribution to conferencePaper

    Abstract

    Sequence-to-sequence models have proven to be highly successful in learning morphological inflection from examples as the series of SIGMORPHON/CoNLL shared tasks have shown. It is usually assumed, however, that a linguist working with inflectional examples could in principle develop a gold standard-level morphological analyzer and generator that would surpass a trained neural network model in accuracy of predictions, but that it may require significant amounts of human labor. In this paper, we discuss an experiment where a group of people with some linguistic training develop 25+ grammars as part of the shared task and weigh the cost/benefit ratio of developing grammars by hand. We also present tools that can help linguists triage difficult complex morphophonological phenomena within a language and hypothesize inflectional class membership. We conclude that a significant development effort by trained linguists to analyze and model morphophonological patterns are required in order to surpass the accuracy of neural models.
    Original languageEnglish
    Pages162-170
    DOIs
    Publication statusPublished - 2020
    Event17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology - Seattle, USA, Online
    Duration: 1 Jan 2020 → …

    Conference

    Conference17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology
    Period1/01/20 → …

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