Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model

Jun Yen Leung, Guy Emerson, Ryan Cotterell


Abstract
Across languages, multiple consecutive adjectives modifying a noun (e.g. “the big red dog”) follow certain unmarked ordering rules. While explanatory accounts have been put forward, much of the work done in this area has relied primarily on the intuitive judgment of native speakers, rather than on corpus data. We present the first purely corpus-driven model of multi-lingual adjective ordering in the form of a latent-variable model that can accurately order adjectives across 24 different languages, even when the training and testing languages are different. We utilize this novel statistical model to provide strong converging evidence for the existence of universal, cross-linguistic, hierarchical adjective ordering tendencies.
Anthology ID:
2020.emnlp-main.329
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Editors:
Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4016–4028
Language:
URL:
https://aclanthology.org/2020.emnlp-main.329
DOI:
10.18653/v1/2020.emnlp-main.329
Bibkey:
Cite (ACL):
Jun Yen Leung, Guy Emerson, and Ryan Cotterell. 2020. Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4016–4028, Online. Association for Computational Linguistics.
Cite (Informal):
Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model (Leung et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.329.pdf
Video:
 https://slideslive.com/38938880
Data
Universal Dependencies