6 citations · 10 across the 10 of their papers we have counts for
10 papers
Phonological Representation Learning for Isolated Signs Improves Out-of-Vocabulary Generalization
Lee Kezar, Zed Sehyr, Jesse Thomason
Sign language datasets are often not representative in terms of vocabulary, underscoring the need for models that generalize to unseen signs. Vector quantization is a promising app…
The American Sign Language Knowledge Graph: Infusing ASL Models with Linguistic Knowledge
Lee Kezar, Nidhi Munikote, Zian Zeng +3
Language models for American Sign Language (ASL) could make language technologies substantially more accessible to those who sign. To train models on tasks such as isolated sign re…
Harmful Speech Detection by Language Models Exhibits Gender-Queer Dialect Bias
Rebecca Dorn, Lee Kezar, Fred Morstatter +1
Content moderation on social media platforms shapes the dynamics of online discourse, influencing whose voices are amplified and whose are suppressed. Recent studies have raised co…
Detecting Unseen Multiword Expressions in American Sign Language
Lee Kezar, Aryan Shukla
Multiword expressions present unique challenges in many translation tasks. In an attempt to ultimately apply a multiword expression detection system to the translation of American…
Finding Pragmatic Differences Between Disciplines
Lee Kezar, Jay Pujara
Scholarly documents have a great degree of variation, both in terms of content (semantics) and structure (pragmatics). Prior work in scholarly document understanding emphasizes sem…
The Sem-Lex Benchmark: Modeling ASL Signs and Their Phonemes
Lee Kezar, Elana Pontecorvo, Adele Daniels +6
Sign language recognition and translation technologies have the potential to increase access and inclusion of deaf signing communities, but research progress is bottlenecked by a l…
Exploring Strategies for Modeling Sign Language Phonology
Lee Kezar, Riley Carlin, Tejas Srinivasan +3
Like speech, signs are composed of discrete, recombinable features called phonemes. Prior work shows that models which can recognize phonemes are better at sign recognition, motiva…
Improving Sign Recognition with Phonology
Lee Kezar, Jesse Thomason, Zed Sevcikova Sehyr
We use insights from research on American Sign Language (ASL) phonology to train models for isolated sign language recognition (ISLR), a step towards automatic sign language unders…
The Role of Facial Expressions and Emotion in ASL
Lee Kezar, Pei Zhou
There is little prior work on quantifying the relationships between facial expressions and emotionality in American Sign Language. In this final report, we provide two methods for…
Evaluating Machine Common Sense via Cloze Testing
Ehsan Qasemi, Lee Kezar, Jay Pujara +1
Language models (LMs) show state of the art performance for common sense (CS) question answering, but whether this ability implies a human-level mastery of CS remains an open quest…