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

Yuh Shyang Wang, Chao Yi Chen, Lung Hao Lee

研究成果: 書貢獻/報告類型會議論文篇章同行評審

摘要

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.

原文???core.languages.en_GB???
主出版物標題ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing
編輯Lung-Hao Lee, Chia-Hui Chang, Kuan-Yu Chen
發行者The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
頁面280-285
頁數6
ISBN(電子)9789869576949
出版狀態已出版 - 2021
事件33rd Conference on Computational Linguistics and Speech Processing, ROCLING 2021 - Taoyuan, Taiwan
持續時間: 15 10月 202116 10月 2021

出版系列

名字ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing

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???event.eventtypes.event.conference???33rd Conference on Computational Linguistics and Speech Processing, ROCLING 2021
國家/地區Taiwan
城市Taoyuan
期間15/10/2116/10/21

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