most citedTechnology-Enabled Disinformation: Summary, Lessons, and Recommendations

17 citations · 17 across the 1 of their papers we have counts for

collaborators

5 papers

cs.CL2020

Grounded Compositional Outputs for Adaptive Language Modeling

Nikolaos Pappas, Phoebe Mulcaire, Noah A. Smith

Language models have emerged as a central component across NLP, and a great deal of progress depends on the ability to cheaply adapt them (e.g., through finetuning) to new domains…

cs.CL2020

Evaluating Models' Local Decision Boundaries via Contrast Sets

Matt Gardner, Yoav Artzi, Victoria Basmova +23

Standard test sets for supervised learning evaluate in-distribution generalization. Unfortunately, when a dataset has systematic gaps (e.g., annotation artifacts), these evaluation…

cs.CL2019

Low-Resource Parsing with Crosslingual Contextualized Representations

Phoebe Mulcaire, Jungo Kasai, Noah A. Smith

Despite advances in dependency parsing, languages with small treebanks still present challenges. We assess recent approaches to multilingual contextual word representations (CWRs),…

cs.CL2019

Polyglot Contextual Representations Improve Crosslingual Transfer

Phoebe Mulcaire, Jungo Kasai, Noah A. Smith

We introduce Rosita, a method to produce multilingual contextual word representations by training a single language model on text from multiple languages. Our method combines the a…

cs.CY201917 cited

Technology-Enabled Disinformation: Summary, Lessons, and Recommendations

John Akers, Gagan Bansal, Gabriel Cadamuro +12

Technology is increasingly used -- unintentionally (misinformation) or intentionally (disinformation) -- to spread false information at scale, with potentially broad-reaching socie…