Papers by Tae-Sun Chung
Multi-Channel Spatio-Temporal Transformer for Sign Language Production (2024.lrec-main)
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| Challenge: | Sign language production models ignore structural correlations between channels and use multi-channel spatial attention to capture correlations across channels. |
| Approach: | They propose a novel approach to transform sign language into a unified feature representation using multi-channel spatial attention and temporal attention to learn sequential dependencies for each channel over time. |
| Outcome: | The proposed model outperforms state-of-the-art models on two sign language datasets from diverse cultures. |
PCEE-BERT: Accelerating BERT Inference via Patient and Confident Early Exiting (2022.findings-naacl)
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| Challenge: | Pre-trained language models (PLMs) are the state-of-the-art (SOTA) models for natural language processing (NLP). |
| Approach: | They propose a patient and confident early exiting BERT (PCEE-BERT) that can work with different PLMs and popular model compression methods. |
| Outcome: | The proposed method outperforms existing models on the GLUE benchmarks and achieves different speed-up ratios. |