Modeling Morphological Typology for Unsupervised Learning of Language Morphology

Hongzhi Xu, Jordan Kodner, Mitchell Marcus, Charles Yang


Abstract
This paper describes a language-independent model for fully unsupervised morphological analysis that exploits a universal framework leveraging morphological typology. By modeling morphological processes including suffixation, prefixation, infixation, and full and partial reduplication with constrained stem change rules, our system effectively constrains the search space and offers a wide coverage in terms of morphological typology. The system is tested on nine typologically and genetically diverse languages, and shows superior performance over leading systems. We also investigate the effect of an oracle that provides only a handful of bits per language to signal morphological type.
Anthology ID:
2020.acl-main.596
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Editors:
Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6672–6681
Language:
URL:
https://aclanthology.org/2020.acl-main.596
DOI:
10.18653/v1/2020.acl-main.596
Bibkey:
Cite (ACL):
Hongzhi Xu, Jordan Kodner, Mitchell Marcus, and Charles Yang. 2020. Modeling Morphological Typology for Unsupervised Learning of Language Morphology. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6672–6681, Online. Association for Computational Linguistics.
Cite (Informal):
Modeling Morphological Typology for Unsupervised Learning of Language Morphology (Xu et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.596.pdf
Video:
 http://slideslive.com/38929259