Events Calendar

PhD Thesis Proposal - Lee Kezar

Title: Phonological Inductive Biases for Computationally Modeling American Sign Language  
 
Date and Time: Tuesday, December 11 - 3:00pm - 4:30pm  
 
Location: GFS 109  
 
Committee: Jesse Thomason (chair), Laurent Itti, Jonathan May, Mike Ananny, Zed Sehyr  
 
Abstract: Sign languages are used by millions of people internationally, yet language technologies commonly do not include them because there are insufficient data to train large neural models. In this presentation, I address to what extent linguistic priors, especially theories of phonology and lexical semantics, can help neural models learn American Sign Language from limited data. We show that learning to recognize phonological features (the location, movement, and configuration of the hands) in video data is a versatile and effective approach for ASL recognition and comprehension. Concretely, we show that phonological and semantic "knowledge infusion" can (a) increase sign recognition accuracy by 30%, (b) enable few- and zero-shot sign understanding, and (c) reduce sensitivity to signer demographics. Proposed work will address longstanding research questions in phonology (such as the number of movement phonemes) and apply our methods to ASL-to-English translation.

Location: Grace Ford Salvatori Hall Of Letters, Arts & Sciences (GFS) - 109

Audiences: Everyone Is Invited

Contact: Lee Kezar