Papers by Gerard Sant

2 papers
SignCLIP: Connecting Text and Sign Language by Contrastive Learning (2024.emnlp-main)

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Challenge: SignCLIP is an efficient method of learning useful visual representations for sign language processing from large-scale, multilingual video-text pairs without optimizing for a specific task or sign language of limited size.
Approach: They propose a method for learning visual representations for sign language processing from large-scale video-text pairs without directly optimizing for a specific task or sign language.
Outcome: The proposed model can learn from multilingual video-text pairs without optimizing for a specific task or sign language of limited size.
Multiformer: A Head-Configurable Transformer-Based Model for Direct Speech Translation (2022.naacl-srw)

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Challenge: Existing approaches to address speech tasks with a self-attention mechanism are expensive and lead to information loss.
Approach: They propose a Transformer-based model which uses different attention mechanisms on each head to bias the self-attention towards the extraction of more diverse token interactions.
Outcome: The proposed model outperforms baseline models by 0.7 BLEU in the speech task.

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