Yuh-Shyang Wang


Generative Adversarial Networks based on Mixed-Attentions for Citation Intent Classification in Scientific Publications
Yuh-Shyang Wang | Chao-Yi Chen | Lung-Hao Lee
Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)

We propose the mixed-attention-based Generative Adversarial Network (named maGAN), and apply it for citation intent classification in scientific publication. We select domain-specific training data, propose a mixed-attention mechanism, and employ generative adversarial network architecture for pre-training language model and fine-tuning to the downstream multi-class classification task. Experiments were conducted on the SciCite datasets to compare model performance. Our proposed maGAN model achieved the best Macro-F1 of 0.8532.


Scientific Writing Evaluation Using Ensemble Multi-channel Neural Networks
Yuh-Shyang Wang | Lung-Hao Lee | Bo-Lin Lin | Liang-Chih Yu
Proceedings of the 32nd Conference on Computational Linguistics and Speech Processing (ROCLING 2020)