Linguist vs. Machine: Rapid Development of Finite-State Morphological Grammars
Date
2020
Authors
Beemer, Sarah
Boston, Zak
Bukoski, April
Chen, Daniel
Dickens, Princess
Gerlach, Andrew
Hopkins, Torin
Jawale, Parth Anand
Koski, Chris
Malhotra, Akanksha
Journal Title
Journal ISSN
Volume Title
Publisher
Association for Computational Linguistics
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.
Description
Keywords
Citation
Collections
Source
Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology
Type
Conference paper
Book Title
Entity type
Access Statement
Open Access
License Rights
Creative Commons Attribution 4.0 International License
Restricted until
Downloads
File
Description