Learning Neural Templates for Text Generation

Sam Wiseman, Stuart Shieber, Alexander Rush


Abstract
While neural, encoder-decoder models have had significant empirical success in text generation, there remain several unaddressed problems with this style of generation. Encoder-decoder models are largely (a) uninterpretable, and (b) difficult to control in terms of their phrasing or content. This work proposes a neural generation system using a hidden semi-markov model (HSMM) decoder, which learns latent, discrete templates jointly with learning to generate. We show that this model learns useful templates, and that these templates make generation both more interpretable and controllable. Furthermore, we show that this approach scales to real data sets and achieves strong performance nearing that of encoder-decoder text generation models.
Anthology ID:
D18-1356
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3174–3187
Language:
URL:
https://aclanthology.org/D18-1356
DOI:
10.18653/v1/D18-1356
Bibkey:
Cite (ACL):
Sam Wiseman, Stuart Shieber, and Alexander Rush. 2018. Learning Neural Templates for Text Generation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3174–3187, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Learning Neural Templates for Text Generation (Wiseman et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1356.pdf
Video:
 https://vimeo.com/305928599
Code
 harvardnlp/neural-template-gen +  additional community code
Data
WikiBio