Unpaired abstract-to-conclusion text style transfer using CycleGANs

Haotong Wang, Yves Lepage, Chooi Ling Goh

研究成果: Conference contribution

抄録

The availability of paired examples greatly facilitates the task of style transfer by allowing the use of supervised learning. However, our scenario does not enjoy such a condition. We focus on style transfer for academic writing, and examine the possibility of performing style transfer between sentences from the abstract and conclusion sections of a scientific article in the Natural Language Processing field, in both directions. We assume a latent correlation between the abstract and conclusion styles, and construct an unpaired data set. We propose the use of a version of CydeGAN based on transformers to perform the task. Our approach is shown to realize differences in tense or word usage which are characteristic of the different sections.

本文言語English
ホスト出版物のタイトル2020 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020
出版社Institute of Electrical and Electronics Engineers Inc.
ページ435-440
ページ数6
ISBN(電子版)9781728192796
DOI
出版ステータスPublished - 2020 10 17
イベント12th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020 - Virtual, Depok, Indonesia
継続期間: 2020 10 172020 10 18

出版物シリーズ

名前2020 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020

Conference

Conference12th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020
国/地域Indonesia
CityVirtual, Depok
Period20/10/1720/10/18

ASJC Scopus subject areas

  • コンピュータ サイエンス(その他)
  • 人工知能
  • コンピュータ サイエンスの応用
  • コンピュータ ビジョンおよびパターン認識
  • 情報システム
  • 信号処理

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