A Well-Composed Text is Half Done! Composition Sampling for Diverse Conditional Generation

Shashi Narayan, Gonçalo Simões, Yao Zhao, Joshua Maynez, Dipanjan Das, Michael Collins, Mirella Lapata


Abstract
We propose Composition Sampling, a simple but effective method to generate diverse outputs for conditional generation of higher quality compared to previous stochastic decoding strategies. It builds on recently proposed plan-based neural generation models (FROST, Narayan et al, 2021) that are trained to first create a composition of the output and then generate by conditioning on it and the input. Our approach avoids text degeneration by first sampling a composition in the form of an entity chain and then using beam search to generate the best possible text grounded to this entity chain. Experiments on summarization (CNN/DailyMail and XSum) and question generation (SQuAD), using existing and newly proposed automaticmetrics together with human-based evaluation, demonstrate that Composition Sampling is currently the best available decoding strategy for generating diverse meaningful outputs.
Anthology ID:
2022.acl-long.94
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1319–1339
Language:
URL:
https://aclanthology.org/2022.acl-long.94
DOI:
10.18653/v1/2022.acl-long.94
Bibkey:
Cite (ACL):
Shashi Narayan, Gonçalo Simões, Yao Zhao, Joshua Maynez, Dipanjan Das, Michael Collins, and Mirella Lapata. 2022. A Well-Composed Text is Half Done! Composition Sampling for Diverse Conditional Generation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1319–1339, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
A Well-Composed Text is Half Done! Composition Sampling for Diverse Conditional Generation (Narayan et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.94.pdf
Video:
 https://aclanthology.org/2022.acl-long.94.mp4
Code
 google-research/language
Data
SQuAD