Generative Adversarial Networks based on Mixed-Attentions for Citation Intent Classification in Scientific Publications

ROCLING 2021 · Yuh-Shyang Wang, Chao-Yi Chen, Lung-Hao Lee ·

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.

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