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 | Piyush Mishra | Saliha Muradoglu | Lan Sang | Tyler Short | Sagarika Shreevastava | Elizabeth Spaulding | Testumichi Umada | Beilei Xiang | Changbing Yang | Mans Hulden
Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology
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.
- Zak Boston 1
- April Bukoski 1
- Daniel Chen 1
- Princess Dickens 1
- Andrew Gerlach 1
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