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

Yuh Shyang Wang, Chao Yi Chen, Lung Hao Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing
EditorsLung-Hao Lee, Chia-Hui Chang, Kuan-Yu Chen
PublisherThe Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
Pages280-285
Number of pages6
ISBN (Electronic)9789869576949
StatePublished - 2021
Event33rd Conference on Computational Linguistics and Speech Processing, ROCLING 2021 - Taoyuan, Taiwan
Duration: 15 Oct 202116 Oct 2021

Publication series

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

Conference

Conference33rd Conference on Computational Linguistics and Speech Processing, ROCLING 2021
Country/TerritoryTaiwan
CityTaoyuan
Period15/10/2116/10/21

Keywords

  • Attentions
  • Citation intents
  • Pretrained language models
  • Scientific publications

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