@inproceedings{kodner-etal-2022-sigmorphon,
title = "{SIGMORPHON}{--}{U}ni{M}orph 2022 Shared Task 0: Generalization and Typologically Diverse Morphological Inflection",
author = "Kodner, Jordan and
Khalifa, Salam and
Batsuren, Khuyagbaatar and
Dolatian, Hossep and
Cotterell, Ryan and
Akkus, Faruk and
Anastasopoulos, Antonios and
Andrushko, Taras and
Arora, Aryaman and
Atanalov, Nona and
Bella, G{\'a}bor and
Budianskaya, Elena and
Ghanggo Ate, Yustinus and
Goldman, Omer and
Guriel, David and
Guriel, Simon and
Guriel-Agiashvili, Silvia and
Kiera{\'s}, Witold and
Krizhanovsky, Andrew and
Krizhanovsky, Natalia and
Marchenko, Igor and
Markowska, Magdalena and
Mashkovtseva, Polina and
Nepomniashchaya, Maria and
Rodionova, Daria and
Scheifer, Karina and
Sorova, Alexandra and
Yemelina, Anastasia and
Young, Jeremiah and
Vylomova, Ekaterina",
editor = "Nicolai, Garrett and
Chodroff, Eleanor",
booktitle = "Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology",
month = jul,
year = "2022",
address = "Seattle, Washington",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.sigmorphon-1.19/",
doi = "10.18653/v1/2022.sigmorphon-1.19",
pages = "176--203",
abstract = "The 2022 SIGMORPHON{--}UniMorph shared task on large scale morphological inflection generation included a wide range of typologically diverse languages: 33 languages from 11 top-level language families: Arabic (Modern Standard), Assamese, Braj, Chukchi, Eastern Armenian, Evenki, Georgian, Gothic, Gujarati, Hebrew, Hungarian, Itelmen, Karelian, Kazakh, Ket, Khalkha Mongolian, Kholosi, Korean, Lamahalot, Low German, Ludic, Magahi, Middle Low German, Old English, Old High German, Old Norse, Polish, Pomak, Slovak, Turkish, Upper Sorbian, Veps, and Xibe. We emphasize generalization along different dimensions this year by evaluating test items with unseen lemmas and unseen features separately under small and large training conditions. Across the five submitted systems and two baselines, the prediction of inflections with unseen features proved challenging, with average performance decreased substantially from last year. This was true even for languages for which the forms were in principle predictable, which suggests that further work is needed in designing systems that capture the various types of generalization required for the world`s languages."
}
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<namePart type="given">Elena</namePart>
<namePart type="family">Budianskaya</namePart>
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<namePart type="given">Yustinus</namePart>
<namePart type="family">Ghanggo Ate</namePart>
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<namePart type="given">Omer</namePart>
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<abstract>The 2022 SIGMORPHON–UniMorph shared task on large scale morphological inflection generation included a wide range of typologically diverse languages: 33 languages from 11 top-level language families: Arabic (Modern Standard), Assamese, Braj, Chukchi, Eastern Armenian, Evenki, Georgian, Gothic, Gujarati, Hebrew, Hungarian, Itelmen, Karelian, Kazakh, Ket, Khalkha Mongolian, Kholosi, Korean, Lamahalot, Low German, Ludic, Magahi, Middle Low German, Old English, Old High German, Old Norse, Polish, Pomak, Slovak, Turkish, Upper Sorbian, Veps, and Xibe. We emphasize generalization along different dimensions this year by evaluating test items with unseen lemmas and unseen features separately under small and large training conditions. Across the five submitted systems and two baselines, the prediction of inflections with unseen features proved challenging, with average performance decreased substantially from last year. This was true even for languages for which the forms were in principle predictable, which suggests that further work is needed in designing systems that capture the various types of generalization required for the world‘s languages.</abstract>
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%0 Conference Proceedings
%T SIGMORPHON–UniMorph 2022 Shared Task 0: Generalization and Typologically Diverse Morphological Inflection
%A Kodner, Jordan
%A Khalifa, Salam
%A Batsuren, Khuyagbaatar
%A Dolatian, Hossep
%A Cotterell, Ryan
%A Akkus, Faruk
%A Anastasopoulos, Antonios
%A Andrushko, Taras
%A Arora, Aryaman
%A Atanalov, Nona
%A Bella, Gábor
%A Budianskaya, Elena
%A Ghanggo Ate, Yustinus
%A Goldman, Omer
%A Guriel, David
%A Guriel, Simon
%A Guriel-Agiashvili, Silvia
%A Kieraś, Witold
%A Krizhanovsky, Andrew
%A Krizhanovsky, Natalia
%A Marchenko, Igor
%A Markowska, Magdalena
%A Mashkovtseva, Polina
%A Nepomniashchaya, Maria
%A Rodionova, Daria
%A Scheifer, Karina
%A Sorova, Alexandra
%A Yemelina, Anastasia
%A Young, Jeremiah
%A Vylomova, Ekaterina
%Y Nicolai, Garrett
%Y Chodroff, Eleanor
%S Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, Washington
%F kodner-etal-2022-sigmorphon
%X The 2022 SIGMORPHON–UniMorph shared task on large scale morphological inflection generation included a wide range of typologically diverse languages: 33 languages from 11 top-level language families: Arabic (Modern Standard), Assamese, Braj, Chukchi, Eastern Armenian, Evenki, Georgian, Gothic, Gujarati, Hebrew, Hungarian, Itelmen, Karelian, Kazakh, Ket, Khalkha Mongolian, Kholosi, Korean, Lamahalot, Low German, Ludic, Magahi, Middle Low German, Old English, Old High German, Old Norse, Polish, Pomak, Slovak, Turkish, Upper Sorbian, Veps, and Xibe. We emphasize generalization along different dimensions this year by evaluating test items with unseen lemmas and unseen features separately under small and large training conditions. Across the five submitted systems and two baselines, the prediction of inflections with unseen features proved challenging, with average performance decreased substantially from last year. This was true even for languages for which the forms were in principle predictable, which suggests that further work is needed in designing systems that capture the various types of generalization required for the world‘s languages.
%R 10.18653/v1/2022.sigmorphon-1.19
%U https://aclanthology.org/2022.sigmorphon-1.19/
%U https://doi.org/10.18653/v1/2022.sigmorphon-1.19
%P 176-203
Markdown (Informal)
[SIGMORPHON–UniMorph 2022 Shared Task 0: Generalization and Typologically Diverse Morphological Inflection](https://aclanthology.org/2022.sigmorphon-1.19/) (Kodner et al., SIGMORPHON 2022)
ACL
- Jordan Kodner, Salam Khalifa, Khuyagbaatar Batsuren, Hossep Dolatian, Ryan Cotterell, Faruk Akkus, Antonios Anastasopoulos, Taras Andrushko, Aryaman Arora, Nona Atanalov, Gábor Bella, Elena Budianskaya, Yustinus Ghanggo Ate, Omer Goldman, David Guriel, Simon Guriel, Silvia Guriel-Agiashvili, Witold Kieraś, Andrew Krizhanovsky, Natalia Krizhanovsky, Igor Marchenko, Magdalena Markowska, Polina Mashkovtseva, Maria Nepomniashchaya, Daria Rodionova, Karina Scheifer, Alexandra Sorova, Anastasia Yemelina, Jeremiah Young, and Ekaterina Vylomova. 2022. SIGMORPHON–UniMorph 2022 Shared Task 0: Generalization and Typologically Diverse Morphological Inflection. In Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 176–203, Seattle, Washington. Association for Computational Linguistics.