13 citations · 26 across the 6 of their papers we have counts for
6 papers
When transformers learn "impossible" languages, what do they learn?
Ram Janarthan, Coleman Haley, Sharon Goldwater
Recent work suggests that transformer language models show a bias towards human languages over unnatural ("impossible") languages argued to be unacquirable by humans. However, this…
Is Information Density Uniform when Utterances are Grounded on Perception and Discourse?
Matteo Gay, Coleman Haley, Mario Giulianelli +1
The Uniform Information Density (UID) hypothesis posits that speakers are subject to a communicative pressure to distribute information evenly within utterances, minimising surpris…
A Grounded Typology of Word Classes
Coleman Haley, Sharon Goldwater, Edoardo Ponti
We propose a grounded approach to meaning in language typology. We treat data from perceptual modalities, such as images, as a language-agnostic representation of meaning. Hence, w…
Structural Biases for Improving Transformers on Translation into Morphologically Rich Languages
Paul Soulos, Sudha Rao, Caitlin Smith +9
Machine translation has seen rapid progress with the advent of Transformer-based models. These models have no explicit linguistic structure built into them, yet they may still impl…
Morphology Matters: A Multilingual Language Modeling Analysis
Hyunji Hayley Park, Katherine J. Zhang, Coleman Haley +3
Prior studies in multilingual language modeling (e.g., Cotterell et al., 2018; Mielke et al., 2019) disagree on whether or not inflectional morphology makes languages harder to mod…
Neural Polysynthetic Language Modelling
Lane Schwartz, Francis Tyers, Lori Levin +18
Research in natural language processing commonly assumes that approaches that work well for English and and other widely-used languages are "language agnostic". In high-resource la…