@inproceedings{beemer-etal-2020-linguist,
title = "Linguist vs. Machine: Rapid Development of Finite-State Morphological Grammars",
author = "Beemer, Sarah and
Boston, Zak and
Bukoski, April and
Chen, Daniel and
Dickens, Princess and
Gerlach, Andrew and
Hopkins, Torin and
Anand Jawale, Parth and
Koski, Chris and
Malhotra, Akanksha and
Mishra, Piyush and
Muradoglu, Saliha and
Sang, Lan and
Short, Tyler and
Shreevastava, Sagarika and
Spaulding, Elizabeth and
Umada, Testumichi and
Xiang, Beilei and
Yang, Changbing and
Hulden, Mans",
editor = "Nicolai, Garrett and
Gorman, Kyle and
Cotterell, Ryan",
booktitle = "Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.sigmorphon-1.18",
doi = "10.18653/v1/2020.sigmorphon-1.18",
pages = "162--170",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Linguist vs. Machine: Rapid Development of Finite-State Morphological Grammars
%A Beemer, Sarah
%A Boston, Zak
%A Bukoski, April
%A Chen, Daniel
%A Dickens, Princess
%A Gerlach, Andrew
%A Hopkins, Torin
%A Anand Jawale, Parth
%A Koski, Chris
%A Malhotra, Akanksha
%A Mishra, Piyush
%A Muradoglu, Saliha
%A Sang, Lan
%A Short, Tyler
%A Shreevastava, Sagarika
%A Spaulding, Elizabeth
%A Umada, Testumichi
%A Xiang, Beilei
%A Yang, Changbing
%A Hulden, Mans
%Y Nicolai, Garrett
%Y Gorman, Kyle
%Y Cotterell, Ryan
%S Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F beemer-etal-2020-linguist
%X 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.
%R 10.18653/v1/2020.sigmorphon-1.18
%U https://aclanthology.org/2020.sigmorphon-1.18
%U https://doi.org/10.18653/v1/2020.sigmorphon-1.18
%P 162-170
Markdown (Informal)
[Linguist vs. Machine: Rapid Development of Finite-State Morphological Grammars](https://aclanthology.org/2020.sigmorphon-1.18) (Beemer et al., SIGMORPHON 2020)
ACL
- 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, et al.. 2020. Linguist vs. Machine: Rapid Development of Finite-State Morphological Grammars. In Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 162–170, Online. Association for Computational Linguistics.