@inproceedings{murikinati-anastasopoulos-2020-cmu,
title = "The {CMU}-{LTI} submission to the {SIGMORPHON} 2020 Shared Task 0: Language-Specific Cross-Lingual Transfer",
author = "Murikinati, Nikitha and
Anastasopoulos, Antonios",
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-1.6",
doi = "10.18653/v1/2020.sigmorphon-1.6",
pages = "79--84",
abstract = "This paper describes the CMU-LTI submission to the SIGMORPHON 2020 Shared Task 0 on typologically diverse morphological inflection. The (unrestricted) submission uses the cross-lingual approach of our last year{'}s winning submission (Anastasopoulos and Neubig, 2019), but adapted to use specific transfer languages for each test language. Our system, with fixed non-tuned hyperparameters, achieved a macro-averaged accuracy of 80.65 ranking 20th among 31 systems, but it was still tied for best system in 25 of the 90 total languages.",
}
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%0 Conference Proceedings
%T The CMU-LTI submission to the SIGMORPHON 2020 Shared Task 0: Language-Specific Cross-Lingual Transfer
%A Murikinati, Nikitha
%A Anastasopoulos, Antonios
%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 murikinati-anastasopoulos-2020-cmu
%X This paper describes the CMU-LTI submission to the SIGMORPHON 2020 Shared Task 0 on typologically diverse morphological inflection. The (unrestricted) submission uses the cross-lingual approach of our last year’s winning submission (Anastasopoulos and Neubig, 2019), but adapted to use specific transfer languages for each test language. Our system, with fixed non-tuned hyperparameters, achieved a macro-averaged accuracy of 80.65 ranking 20th among 31 systems, but it was still tied for best system in 25 of the 90 total languages.
%R 10.18653/v1/2020.sigmorphon-1.6
%U https://aclanthology.org/2020.sigmorphon-1.6
%U https://doi.org/10.18653/v1/2020.sigmorphon-1.6
%P 79-84
Markdown (Informal)
[The CMU-LTI submission to the SIGMORPHON 2020 Shared Task 0: Language-Specific Cross-Lingual Transfer](https://aclanthology.org/2020.sigmorphon-1.6) (Murikinati & Anastasopoulos, SIGMORPHON 2020)
ACL