@inproceedings{narayan-etal-2022-well,
title = "A Well-Composed Text is Half Done! Composition Sampling for Diverse Conditional Generation",
author = "Narayan, Shashi and
Sim{\~o}es, Gon{\c{c}}alo and
Zhao, Yao and
Maynez, Joshua and
Das, Dipanjan and
Collins, Michael and
Lapata, Mirella",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.94",
doi = "10.18653/v1/2022.acl-long.94",
pages = "1319--1339",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T A Well-Composed Text is Half Done! Composition Sampling for Diverse Conditional Generation
%A Narayan, Shashi
%A Simões, Gonçalo
%A Zhao, Yao
%A Maynez, Joshua
%A Das, Dipanjan
%A Collins, Michael
%A Lapata, Mirella
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F narayan-etal-2022-well
%X 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.
%R 10.18653/v1/2022.acl-long.94
%U https://aclanthology.org/2022.acl-long.94
%U https://doi.org/10.18653/v1/2022.acl-long.94
%P 1319-1339
Markdown (Informal)
[A Well-Composed Text is Half Done! Composition Sampling for Diverse Conditional Generation](https://aclanthology.org/2022.acl-long.94) (Narayan et al., ACL 2022)
ACL