Learning Implicit Text Generation via Feature Matching
Inkit Padhi, Pierre Dognin, Ke Bai, Cícero Nogueira dos Santos, Vijil Chenthamarakshan, Youssef Mroueh, Payel Das
Abstract
Generative feature matching network (GFMN) is an approach for training state-of-the-art implicit generative models for images by performing moment matching on features from pre-trained neural networks. In this paper, we present new GFMN formulations that are effective for sequential data. Our experimental results show the effectiveness of the proposed method, SeqGFMN, for three distinct generation tasks in English: unconditional text generation, class-conditional text generation, and unsupervised text style transfer. SeqGFMN is stable to train and outperforms various adversarial approaches for text generation and text style transfer.- Anthology ID:
- 2020.acl-main.354
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3855–3863
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.354
- DOI:
- 10.18653/v1/2020.acl-main.354
- Cite (ACL):
- Inkit Padhi, Pierre Dognin, Ke Bai, Cícero Nogueira dos Santos, Vijil Chenthamarakshan, Youssef Mroueh, and Payel Das. 2020. Learning Implicit Text Generation via Feature Matching. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3855–3863, Online. Association for Computational Linguistics.
- Cite (Informal):
- Learning Implicit Text Generation via Feature Matching (Padhi et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/2020.acl-main.354.pdf
- Data
- MS COCO