@inproceedings{04aaf979562d407d8f9e26c4adb2d749,
title = "Generative Adversarial Networks based on Mixed-Attentions for Citation Intent Classification in Scientific Publications",
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.",
keywords = "Attentions, Citation intents, Pretrained language models, Scientific publications",
author = "Wang, {Yuh Shyang} and Chen, {Chao Yi} and Lee, {Lung Hao}",
note = "Publisher Copyright: {\textcopyright} 2021 ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing. All rights reserved.; 33rd Conference on Computational Linguistics and Speech Processing, ROCLING 2021 ; Conference date: 15-10-2021 Through 16-10-2021",
year = "2021",
language = "English",
series = "ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing",
publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
pages = "280--285",
editor = "Lung-Hao Lee and Chia-Hui Chang and Kuan-Yu Chen",
booktitle = "ROCLING 2021 - Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing",
}