The CoNLL--SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection
Abstract
The CoNLL--SIGMORPHON 2018 shared task on supervised learning of morphological generation featured data sets from 103 typologically diverse languages. Apart from extending the number of languages involved in earlier supervised tasks of generating inflected forms, this year the shared task also featured a new second task which asked participants to inflect words in sentential context, similar to a cloze task. This second task featured seven languages. Task 1 received 27 submissions and task 2 received 6 submissions. Both tasks featured a low, medium, and high data condition. Nearly all submissions featured a neural component and built on highly-ranked systems from the earlier 2017 shared task. In the inflection task (task 1), 41 of the 52 languages present in last year's inflection task showed improvement by the best systems in the low-resource setting. The cloze task (task 2) proved to be difficult, and few submissions managed to consistently improve upon both a simple neural baseline system and a lemma-repeating baseline.
Cite
@article{arxiv.1810.07125,
title = {The CoNLL--SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection},
author = {Ryan Cotterell and Christo Kirov and John Sylak-Glassman and Géraldine Walther and Ekaterina Vylomova and Arya D. McCarthy and Katharina Kann and Sabrina J. Mielke and Garrett Nicolai and Miikka Silfverberg and David Yarowsky and Jason Eisner and Mans Hulden},
journal= {arXiv preprint arXiv:1810.07125},
year = {2020}
}
Comments
CoNLL 2018. arXiv admin note: text overlap with arXiv:1706.09031