Papers by Guangyong Chen
G-MAP: General Memory-Augmented Pre-trained Language Model for Domain Tasks (2022.emnlp-main)
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| Challenge: | Existing domain-adaptive pre-training (DAPT) models tend to forget the general knowledge acquired by general PLMs, leading to catastrophic forgetting and sub-optimal performance. |
| Approach: | They propose a framework which augments the domain-specific PLM by a memory built from the frozen general PLM without losing the general knowledge. |
| Outcome: | The proposed framework augments the domain-specific PLM by a memory built from the frozen general PLM without losing the general knowledge. |
Explore More Guidance: A Task-aware Instruction Network for Sign Language Translation Enhanced with Data Augmentation (2022.findings-naacl)
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| Challenge: | Existing studies focus on the recognition step, while paying less attention to sign language translation. |
| Approach: | They propose a task-aware instruction network, namely TIN-SLT, for sign language translation, by introducing the isntruction module and the learning-based feature fuse strategy into a Transformer network. |
| Outcome: | The proposed system outperforms existing solutions on two benchmark datasets, PHOENIX-2014-T and ASLG-PC12, and outperformed previous best solutions by 1.65 and 1.42 in terms of BLEU-4. |