98 citations · 562 across the 50 of their papers we have counts for
23 papers · 1 filter
Morphologically Aware Word-Level Translation
Paula Czarnowska, Sebastian Ruder, Ryan Cotterell +1
We propose a novel morphologically aware probability model for bilingual lexicon induction, which jointly models lexeme translation and inflectional morphology in a structured way.…
Multimodal Pretraining Unmasked: A Meta-Analysis and a Unified Framework of Vision-and-Language BERTs
Emanuele Bugliarello, Ryan Cotterell, Naoaki Okazaki +1
Large-scale pretraining and task-specific fine-tuning is now the standard methodology for many tasks in computer vision and natural language processing. Recently, a multitude of me…
SIGTYP 2020 Shared Task: Prediction of Typological Features
Johannes Bjerva, Elizabeth Salesky, Sabrina J. Mielke +6
Typological knowledge bases (KBs) such as WALS (Dryer and Haspelmath, 2013) contain information about linguistic properties of the world's languages. They have been shown to be use…
Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model
Jun Yen Leung, Guy Emerson, Ryan Cotterell
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…
Please Mind the Root: Decoding Arborescences for Dependency Parsing
Ran Zmigrod, Tim Vieira, Ryan Cotterell
The connection between dependency trees and spanning trees is exploited by the NLP community to train and to decode graph-based dependency parsers. However, the NLP literature has…
Intrinsic Probing through Dimension Selection
Lucas Torroba Hennigen, Adina Williams, Ryan Cotterell
Most modern NLP systems make use of pre-trained contextual representations that attain astonishingly high performance on a variety of tasks. Such high performance should not be pos…