@inproceedings{kodner-etal-2023-morphological,
title = "Morphological Inflection: A Reality Check",
author = "Kodner, Jordan and
Payne, Sarah and
Khalifa, Salam and
Liu, Zoey",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.335",
doi = "10.18653/v1/2023.acl-long.335",
pages = "6082--6101",
abstract = "Morphological inflection is a popular task in sub-word NLP with both practical and cognitive applications. For years now, state-of-the-art systems have reported high, but also highly variable, performance across data sets and languages. We investigate the causes of this high performance and high variability; we find several aspects of data set creation and evaluation which systematically inflate performance and obfuscate differences between languages. To improve generalizability and reliability of results, we propose new data sampling and evaluation strategies that better reflect likely use-cases. Using these new strategies, we make new observations on the generalization abilities of current inflection systems.",
}
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<abstract>Morphological inflection is a popular task in sub-word NLP with both practical and cognitive applications. For years now, state-of-the-art systems have reported high, but also highly variable, performance across data sets and languages. We investigate the causes of this high performance and high variability; we find several aspects of data set creation and evaluation which systematically inflate performance and obfuscate differences between languages. To improve generalizability and reliability of results, we propose new data sampling and evaluation strategies that better reflect likely use-cases. Using these new strategies, we make new observations on the generalization abilities of current inflection systems.</abstract>
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%0 Conference Proceedings
%T Morphological Inflection: A Reality Check
%A Kodner, Jordan
%A Payne, Sarah
%A Khalifa, Salam
%A Liu, Zoey
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F kodner-etal-2023-morphological
%X Morphological inflection is a popular task in sub-word NLP with both practical and cognitive applications. For years now, state-of-the-art systems have reported high, but also highly variable, performance across data sets and languages. We investigate the causes of this high performance and high variability; we find several aspects of data set creation and evaluation which systematically inflate performance and obfuscate differences between languages. To improve generalizability and reliability of results, we propose new data sampling and evaluation strategies that better reflect likely use-cases. Using these new strategies, we make new observations on the generalization abilities of current inflection systems.
%R 10.18653/v1/2023.acl-long.335
%U https://aclanthology.org/2023.acl-long.335
%U https://doi.org/10.18653/v1/2023.acl-long.335
%P 6082-6101
Markdown (Informal)
[Morphological Inflection: A Reality Check](https://aclanthology.org/2023.acl-long.335) (Kodner et al., ACL 2023)
ACL
- Jordan Kodner, Sarah Payne, Salam Khalifa, and Zoey Liu. 2023. Morphological Inflection: A Reality Check. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6082–6101, Toronto, Canada. Association for Computational Linguistics.