Papers by Lee Kezar

3 papers
The American Sign Language Knowledge Graph: Infusing ASL Models with Linguistic Knowledge (2025.findings-naacl)

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Challenge: Sign language models could make language technologies more accessible to deaf and hard-of-hearing signers, but the supply of accurately labeled data struggles to meet the demand associated with training large, end-to-end architectures.
Approach: They construct an American Sign Language Knowledge Graph from 11 sources of linguistic knowledge and use it to train neuro-symbolic models on ASL video input tasks.
Outcome: The proposed model achieves 91% accuracies for isolated sign recognition, 14% for predicting the semantic features of unseen signs, and 36% for classifying the topic of Youtube-ASL videos.
Improving Sign Recognition with Phonology (2023.eacl-main)

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Challenge: Existing work does not consider sign language phonology, but none leverages it . a recent study has shown that sign language recognition models lack structure .
Approach: They explicitly recognize the role of phonology in sign production to train models for isolated sign language recognition . they train models that take in pose estimations of a signer producing a single sign to predict its phonological characteristics .
Outcome: The proposed model improves sign recognition accuracy by 9% on the WLASL benchmark . the study could accelerate linguistic research in the domain of signed languages .
Mixed Feelings: Natural Text Generation with Variable, Coexistent Affective Categories (P18-3)

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Challenge: a recent study has shown that language models which can generate emotional sentences are limited to one affective category out of a few.
Approach: a new research proposal proposes a language model which can produce multiple emotions simultaneously. authors propose to use a long-term memory language model to allow for variation in multiple emotions.
Outcome: a new language model allows for variation in multiple emotions simultaneously . the proposed model is based on a model of long-term memory .

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