Using Global Constraints and Reranking to Improve Cognates Detection

Michael Bloodgood, Benjamin Strauss


Abstract
Global constraints and reranking have not been used in cognates detection research to date. We propose methods for using global constraints by performing rescoring of the score matrices produced by state of the art cognates detection systems. Using global constraints to perform rescoring is complementary to state of the art methods for performing cognates detection and results in significant performance improvements beyond current state of the art performance on publicly available datasets with different language pairs and various conditions such as different levels of baseline state of the art performance and different data size conditions, including with more realistic large data size conditions than have been evaluated with in the past.
Anthology ID:
P17-1181
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1983–1992
Language:
URL:
https://www.aclweb.org/anthology/P17-1181
DOI:
10.18653/v1/P17-1181
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PDF:
http://aclanthology.lst.uni-saarland.de/P17-1181.pdf
Poster:
 P17-1181.Poster.pdf