@inproceedings{wu-yarowsky-2022-robustness,
title = "On the Robustness of Cognate Generation Models",
author = "Wu, Winston and
Yarowsky, David",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.458",
pages = "4299--4305",
abstract = "We evaluate two popular neural cognate generation models{'} robustness to several types of human-plausible noise (deletion, duplication, swapping, and keyboard errors, as well as a new type of error, phonological errors). We find that duplication and phonological substitution is least harmful, while the other types of errors are harmful. We present an in-depth analysis of the models{'} results with respect to each error type to explain how and why these models perform as they do.",
}
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%0 Conference Proceedings
%T On the Robustness of Cognate Generation Models
%A Wu, Winston
%A Yarowsky, David
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Thirteenth Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F wu-yarowsky-2022-robustness
%X We evaluate two popular neural cognate generation models’ robustness to several types of human-plausible noise (deletion, duplication, swapping, and keyboard errors, as well as a new type of error, phonological errors). We find that duplication and phonological substitution is least harmful, while the other types of errors are harmful. We present an in-depth analysis of the models’ results with respect to each error type to explain how and why these models perform as they do.
%U https://aclanthology.org/2022.lrec-1.458
%P 4299-4305
Markdown (Informal)
[On the Robustness of Cognate Generation Models](https://aclanthology.org/2022.lrec-1.458) (Wu & Yarowsky, LREC 2022)
ACL
- Winston Wu and David Yarowsky. 2022. On the Robustness of Cognate Generation Models. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 4299–4305, Marseille, France. European Language Resources Association.