Learning Rhyming Constraints using Structured Adversaries

Harsh Jhamtani, Sanket Vaibhav Mehta, Jaime Carbonell, Taylor Berg-Kirkpatrick


Abstract
Existing recurrent neural language models often fail to capture higher-level structure present in text: for example, rhyming patterns present in poetry. Much prior work on poetry generation uses manually defined constraints which are satisfied during decoding using either specialized decoding procedures or rejection sampling. The rhyming constraints themselves are typically not learned by the generator. We propose an alternate approach that uses a structured discriminator to learn a poetry generator that directly captures rhyming constraints in a generative adversarial setup. By causing the discriminator to compare poems based only on a learned similarity matrix of pairs of line ending words, the proposed approach is able to successfully learn rhyming patterns in two different English poetry datasets (Sonnet and Limerick) without explicitly being provided with any phonetic information
Anthology ID:
D19-1621
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
6025–6031
Language:
URL:
https://aclanthology.org/D19-1621
DOI:
10.18653/v1/D19-1621
Bibkey:
Cite (ACL):
Harsh Jhamtani, Sanket Vaibhav Mehta, Jaime Carbonell, and Taylor Berg-Kirkpatrick. 2019. Learning Rhyming Constraints using Structured Adversaries. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6025–6031, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Learning Rhyming Constraints using Structured Adversaries (Jhamtani et al., EMNLP-IJCNLP 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingest-bitext-workshop/D19-1621.pdf
Code
 harsh19/Structured-Adversary