Adversarial Generation of Natural Language
Sandeep Subramanian, Sai Rajeswar, Francis Dutil, Chris Pal, Aaron Courville
Abstract
Generative Adversarial Networks (GANs) have gathered a lot of attention from the computer vision community, yielding impressive results for image generation. Advances in the adversarial generation of natural language from noise however are not commensurate with the progress made in generating images, and still lag far behind likelihood based methods. In this paper, we take a step towards generating natural language with a GAN objective alone. We introduce a simple baseline that addresses the discrete output space problem without relying on gradient estimators and show that it is able to achieve state-of-the-art results on a Chinese poem generation dataset. We present quantitative results on generating sentences from context-free and probabilistic context-free grammars, and qualitative language modeling results. A conditional version is also described that can generate sequences conditioned on sentence characteristics.- Anthology ID:
- W17-2629
- Volume:
- Proceedings of the 2nd Workshop on Representation Learning for NLP
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Phil Blunsom, Antoine Bordes, Kyunghyun Cho, Shay Cohen, Chris Dyer, Edward Grefenstette, Karl Moritz Hermann, Laura Rimell, Jason Weston, Scott Yih
- Venue:
- RepL4NLP
- SIG:
- SIGREP
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 241–251
- Language:
- URL:
- https://aclanthology.org/W17-2629
- DOI:
- 10.18653/v1/W17-2629
- Cite (ACL):
- Sandeep Subramanian, Sai Rajeswar, Francis Dutil, Chris Pal, and Aaron Courville. 2017. Adversarial Generation of Natural Language. In Proceedings of the 2nd Workshop on Representation Learning for NLP, pages 241–251, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Adversarial Generation of Natural Language (Subramanian et al., RepL4NLP 2017)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/W17-2629.pdf
- Data
- Penn Treebank