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Found 8 papers, 3 papers with code
Detecting Unseen Multiword Expressions in American Sign Language
Multiword expressions present unique challenges in many translation tasks.
Finding Pragmatic Differences Between Disciplines
Using a corpus of scholarly documents across 19 disciplines and state-of-the-art language modeling techniques, we learn a fixed set of domain-agnostic descriptors for document sections and "retrofit" the corpus to these descriptors (also referred to as "normalization").
The Sem-Lex Benchmark: Modeling ASL Signs and Their Phonemes
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 lack of representative data.
Exploring Strategies for Modeling Sign Language Phonology
Specifically, we explore how learning strategies like multi-task and curriculum learning can leverage mutually useful information between phoneme types to facilitate better modeling of sign language phonemes.
Improving Sign Recognition with Phonology
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 understanding.
Evaluating Machine Common Sense via Cloze Testing
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 question.
The Role of Facial Expressions and Emotion in ASL
There is little prior work on quantifying the relationships between facial expressions and emotionality in American Sign Language.
Mixed Feelings: Natural Text Generation with Variable, Coexistent Affective Categories
Conversational agents, having the goal of natural language generation, must rely on language models which can integrate emotion into their responses.
