Sarah Beemer author Zak Boston author April Bukoski author Daniel Chen author Princess Dickens author Andrew Gerlach author Torin Hopkins author Parth Anand Jawale author Chris Koski author Akanksha Malhotra author Piyush Mishra author Saliha Muradoglu author Lan Sang author Tyler Short author Sagarika Shreevastava author Elizabeth Spaulding author Testumichi Umada author Beilei Xiang author Changbing Yang author Mans Hulden author 2020-jul text Association for Computational Linguistics Online conference publication 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. beemer-etal-2020-linguist https://www.aclweb.org/anthology/2020.sigmorphon-1.18 2020-jul 162 170