@inproceedings{vylomova-etal-2020-sigmorphon,
title = "{SIGMORPHON} 2020 Shared Task 0: Typologically Diverse Morphological Inflection",
author = "Vylomova, Ekaterina and
White, Jennifer and
Salesky, Elizabeth and
Mielke, Sabrina J. and
Wu, Shijie and
Ponti, Edoardo Maria and
Maudslay, Rowan Hall and
Zmigrod, Ran and
Valvoda, Josef and
Toldova, Svetlana and
Tyers, Francis and
Klyachko, Elena and
Yegorov, Ilya and
Krizhanovsky, Natalia and
Czarnowska, Paula and
Nikkarinen, Irene and
Krizhanovsky, Andrew and
Pimentel, Tiago and
Torroba Hennigen, Lucas and
Kirov, Christo and
Nicolai, Garrett and
Williams, Adina and
Anastasopoulos, Antonios and
Cruz, Hilaria and
Chodroff, Eleanor and
Cotterell, Ryan and
Silfverberg, Miikka 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-1.1",
doi = "10.18653/v1/2020.sigmorphon-1.1",
pages = "1--39",
abstract = "A broad goal in natural language processing (NLP) is to develop a system that has the capacity to process any natural language. Most systems, however, are developed using data from just one language such as English. The SIGMORPHON 2020 shared task on morphological reinflection aims to investigate systems{'} ability to generalize across typologically distinct languages, many of which are low resource. Systems were developed using data from 45 languages and just 5 language families, fine-tuned with data from an additional 45 languages and 10 language families (13 in total), and evaluated on all 90 languages. A total of 22 systems (19 neural) from 10 teams were submitted to the task. All four winning systems were neural (two monolingual transformers and two massively multilingual RNN-based models with gated attention). Most teams demonstrate utility of data hallucination and augmentation, ensembles, and multilingual training for low-resource languages. Non-neural learners and manually designed grammars showed competitive and even superior performance on some languages (such as Ingrian, Tajik, Tagalog, Zarma, Lingala), especially with very limited data. Some language families (Afro-Asiatic, Niger-Congo, Turkic) were relatively easy for most systems and achieved over 90{\%} mean accuracy while others were more challenging.",
}
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<abstract>A broad goal in natural language processing (NLP) is to develop a system that has the capacity to process any natural language. Most systems, however, are developed using data from just one language such as English. The SIGMORPHON 2020 shared task on morphological reinflection aims to investigate systems’ ability to generalize across typologically distinct languages, many of which are low resource. Systems were developed using data from 45 languages and just 5 language families, fine-tuned with data from an additional 45 languages and 10 language families (13 in total), and evaluated on all 90 languages. A total of 22 systems (19 neural) from 10 teams were submitted to the task. All four winning systems were neural (two monolingual transformers and two massively multilingual RNN-based models with gated attention). Most teams demonstrate utility of data hallucination and augmentation, ensembles, and multilingual training for low-resource languages. Non-neural learners and manually designed grammars showed competitive and even superior performance on some languages (such as Ingrian, Tajik, Tagalog, Zarma, Lingala), especially with very limited data. Some language families (Afro-Asiatic, Niger-Congo, Turkic) were relatively easy for most systems and achieved over 90% mean accuracy while others were more challenging.</abstract>
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%0 Conference Proceedings
%T SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection
%A Vylomova, Ekaterina
%A White, Jennifer
%A Salesky, Elizabeth
%A Mielke, Sabrina J.
%A Wu, Shijie
%A Ponti, Edoardo Maria
%A Maudslay, Rowan Hall
%A Zmigrod, Ran
%A Valvoda, Josef
%A Toldova, Svetlana
%A Tyers, Francis
%A Klyachko, Elena
%A Yegorov, Ilya
%A Krizhanovsky, Natalia
%A Czarnowska, Paula
%A Nikkarinen, Irene
%A Krizhanovsky, Andrew
%A Pimentel, Tiago
%A Torroba Hennigen, Lucas
%A Kirov, Christo
%A Nicolai, Garrett
%A Williams, Adina
%A Anastasopoulos, Antonios
%A Cruz, Hilaria
%A Chodroff, Eleanor
%A Cotterell, Ryan
%A Silfverberg, Miikka
%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 vylomova-etal-2020-sigmorphon
%X A broad goal in natural language processing (NLP) is to develop a system that has the capacity to process any natural language. Most systems, however, are developed using data from just one language such as English. The SIGMORPHON 2020 shared task on morphological reinflection aims to investigate systems’ ability to generalize across typologically distinct languages, many of which are low resource. Systems were developed using data from 45 languages and just 5 language families, fine-tuned with data from an additional 45 languages and 10 language families (13 in total), and evaluated on all 90 languages. A total of 22 systems (19 neural) from 10 teams were submitted to the task. All four winning systems were neural (two monolingual transformers and two massively multilingual RNN-based models with gated attention). Most teams demonstrate utility of data hallucination and augmentation, ensembles, and multilingual training for low-resource languages. Non-neural learners and manually designed grammars showed competitive and even superior performance on some languages (such as Ingrian, Tajik, Tagalog, Zarma, Lingala), especially with very limited data. Some language families (Afro-Asiatic, Niger-Congo, Turkic) were relatively easy for most systems and achieved over 90% mean accuracy while others were more challenging.
%R 10.18653/v1/2020.sigmorphon-1.1
%U https://aclanthology.org/2020.sigmorphon-1.1
%U https://doi.org/10.18653/v1/2020.sigmorphon-1.1
%P 1-39
Markdown (Informal)
[SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection](https://aclanthology.org/2020.sigmorphon-1.1) (Vylomova et al., SIGMORPHON 2020)
ACL
- Ekaterina Vylomova, Jennifer White, Elizabeth Salesky, Sabrina J. Mielke, Shijie Wu, Edoardo Maria Ponti, Rowan Hall Maudslay, Ran Zmigrod, Josef Valvoda, Svetlana Toldova, Francis Tyers, Elena Klyachko, Ilya Yegorov, Natalia Krizhanovsky, Paula Czarnowska, Irene Nikkarinen, Andrew Krizhanovsky, Tiago Pimentel, Lucas Torroba Hennigen, et al.. 2020. SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection. In Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 1–39, Online. Association for Computational Linguistics.