most citedA Property Induction Framework for Neural Language Models

10 citations · 10 across the 8 of their papers we have counts for

collaborators

9 papers

cs.CL202210 cited

A Property Induction Framework for Neural Language Models

Kanishka Misra, Julia Taylor Rayz, Allyson Ettinger

To what extent can experience from language contribute to our conceptual knowledge? Computational explorations of this question have shed light on the ability of powerful neural la…

cs.CL2022

On Information Hiding in Natural Language Systems

Geetanjali Bihani, Julia Taylor Rayz

With data privacy becoming more of a necessity than a luxury in today's digital world, research on more robust models of privacy preservation and information security is on the ris…

cs.CL2021

Do language models learn typicality judgments from text?

Kanishka Misra, Allyson Ettinger, Julia Taylor Rayz

Building on research arguing for the possibility of conceptual and categorical knowledge acquisition through statistics contained in language, we evaluate predictive language model…

cs.CL2021

Low Anisotropy Sense Retrofitting (LASeR) : Towards Isotropic and Sense Enriched Representations

Geetanjali Bihani, Julia Taylor Rayz

Contextual word representation models have shown massive improvements on a multitude of NLP tasks, yet their word sense disambiguation capabilities remain poorly explained. To addr…

cs.CL2021

Fuzzy Classification of Multi-intent Utterances

Geetanjali Bihani, Julia Taylor Rayz

Current intent classification approaches assign binary intent class memberships to natural language utterances while disregarding the inherent vagueness in language and the corresp…

cs.CL2021

Finding Fuzziness in Neural Network Models of Language Processing

Kanishka Misra, Julia Taylor Rayz

Humans often communicate by using imprecise language, suggesting that fuzzy concepts with unclear boundaries are prevalent in language use. In this paper, we test the extent to whi…