@inproceedings{goldman-etal-2023-sigmorphon,
title = "{SIGMORPHON}{--}{U}ni{M}orph 2023 Shared Task 0: Typologically Diverse Morphological Inflection",
author = "Goldman, Omer and
Batsuren, Khuyagbaatar and
Khalifa, Salam and
Arora, Aryaman and
Nicolai, Garrett and
Tsarfaty, Reut and
Vylomova, Ekaterina",
editor = {Nicolai, Garrett and
Chodroff, Eleanor and
Mailhot, Frederic and
{\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i}},
booktitle = "Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.sigmorphon-1.13",
doi = "10.18653/v1/2023.sigmorphon-1.13",
pages = "117--125",
abstract = "The 2023 SIGMORPHON{--}UniMorph shared task on typologically diverse morphological inflection included a wide range of languages: 26 languages from 9 primary language families. The data this year was all lemma-split, to allow testing models{'} generalization ability, and structured along the new hierarchical schema presented in (Batsuren et al., 2022). The systems submitted this year, 9 in number, showed ingenuity and innovativeness, including hard attention for explainability and bidirectional decoding. Special treatment was also given by many participants to the newly-introduced data in Japanese, due to the high abundance of unseen Kanji characters in its test set.",
}
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<abstract>The 2023 SIGMORPHON–UniMorph shared task on typologically diverse morphological inflection included a wide range of languages: 26 languages from 9 primary language families. The data this year was all lemma-split, to allow testing models’ generalization ability, and structured along the new hierarchical schema presented in (Batsuren et al., 2022). The systems submitted this year, 9 in number, showed ingenuity and innovativeness, including hard attention for explainability and bidirectional decoding. Special treatment was also given by many participants to the newly-introduced data in Japanese, due to the high abundance of unseen Kanji characters in its test set.</abstract>
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%0 Conference Proceedings
%T SIGMORPHON–UniMorph 2023 Shared Task 0: Typologically Diverse Morphological Inflection
%A Goldman, Omer
%A Batsuren, Khuyagbaatar
%A Khalifa, Salam
%A Arora, Aryaman
%A Nicolai, Garrett
%A Tsarfaty, Reut
%A Vylomova, Ekaterina
%Y Nicolai, Garrett
%Y Chodroff, Eleanor
%Y Mailhot, Frederic
%Y Çöltekin, Çağrı
%S Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F goldman-etal-2023-sigmorphon
%X The 2023 SIGMORPHON–UniMorph shared task on typologically diverse morphological inflection included a wide range of languages: 26 languages from 9 primary language families. The data this year was all lemma-split, to allow testing models’ generalization ability, and structured along the new hierarchical schema presented in (Batsuren et al., 2022). The systems submitted this year, 9 in number, showed ingenuity and innovativeness, including hard attention for explainability and bidirectional decoding. Special treatment was also given by many participants to the newly-introduced data in Japanese, due to the high abundance of unseen Kanji characters in its test set.
%R 10.18653/v1/2023.sigmorphon-1.13
%U https://aclanthology.org/2023.sigmorphon-1.13
%U https://doi.org/10.18653/v1/2023.sigmorphon-1.13
%P 117-125
Markdown (Informal)
[SIGMORPHON–UniMorph 2023 Shared Task 0: Typologically Diverse Morphological Inflection](https://aclanthology.org/2023.sigmorphon-1.13) (Goldman et al., SIGMORPHON 2023)
ACL
- Omer Goldman, Khuyagbaatar Batsuren, Salam Khalifa, Aryaman Arora, Garrett Nicolai, Reut Tsarfaty, and Ekaterina Vylomova. 2023. SIGMORPHON–UniMorph 2023 Shared Task 0: Typologically Diverse Morphological Inflection. In Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 117–125, Toronto, Canada. Association for Computational Linguistics.