Abstract
Developing conventional natural language generation systems requires extensive attention from human experts in order to craft complex sets of sentence planning rules. We propose a Bayesian nonparametric approach to learn sentence planning rules by inducing synchronous tree substitution grammars for pairs of text plans and morphosyntactically-specified dependency trees. Our system is able to learn rules which can be used to generate novel texts after training on small datasets.- Anthology ID:
- W18-6546
- Volume:
- Proceedings of the 11th International Conference on Natural Language Generation
- Month:
- November
- Year:
- 2018
- Address:
- Tilburg University, The Netherlands
- Editors:
- Emiel Krahmer, Albert Gatt, Martijn Goudbeek
- Venue:
- INLG
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 391–396
- Language:
- URL:
- https://aclanthology.org/W18-6546
- DOI:
- 10.18653/v1/W18-6546
- Cite (ACL):
- David M. Howcroft, Dietrich Klakow, and Vera Demberg. 2018. Toward Bayesian Synchronous Tree Substitution Grammars for Sentence Planning. In Proceedings of the 11th International Conference on Natural Language Generation, pages 391–396, Tilburg University, The Netherlands. Association for Computational Linguistics.
- Cite (Informal):
- Toward Bayesian Synchronous Tree Substitution Grammars for Sentence Planning (Howcroft et al., INLG 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/W18-6546.pdf