Papers by Lorna Quandt
Modeling Intensification for Sign Language Generation: A Computational Approach (2022.findings-acl)
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| Challenge: | End-to-end sign language generation models do not accurately represent prosody in sign language. |
| Approach: | They propose to model intensification in a data-driven manner to improve prosody in generated sign languages by modeling temporal and spatial variations. |
| Outcome: | The proposed models improve the prosody of generated sign languages by using data-driven models. |
Including Facial Expressions in Contextual Embeddings for Sign Language Generation (2023.starsem-1)
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| Challenge: | State-of-the-art sign language generation frameworks lack expressivity and naturalness . current systems focus on manual signs, neglecting affective, grammatical and semantic functions of facial expressions . communication between the Deaf and Hard of Hearing (DHH) individuals may be facilitated by emerging language technologies . |
| Approach: | They propose a Dual Encoder Transformer capable of generating manual signs and facial expressions by capturing similarities and differences found in text and sign gloss annotations. |
| Outcome: | The proposed model improves the quality of automatically generated sign language. |