16/11/2020

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

Jun Yen Leung, Guy Emerson, Ryan Cotterell

Keywords: multi-lingual ordering, corpus-driven model, latent-variable model, statistical model

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.

 0
 0
 0
 0
This is an embedded video. Talk and the respective paper are published at EMNLP 2020 virtual conference. If you are one of the authors of the paper and want to manage your upload, see the question "My papertalk has been externally embedded..." in the FAQ section.

Comments

Post Comment
no comments yet
code of conduct: tbd

Similar Papers