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

• URL: http://arxiv.org/abs/2203.15108v1
• Date: Mon, 28 Mar 2022 21:24:03 GMT
• Title: A Well-Composed Text is Half Done! Composition Sampling for Diverse Conditional Generation
• Authors: Shashi Narayan, Gon\c{c}alo Sim\~oes, Yao Zhao, Joshua Maynez, Dipanjan Das, Michael Collins and Mirella Lapata
• Abstract summary: We propose Composition Sampling, a simple but effective method to generate diverse outputs for conditional generation of higher quality. It builds on recently proposed plan-based neural generation models that are trained to first create a composition of the output and then generate by conditioning on it and the input.
• Score: 79.98319703471596
• License: http://creativecommons.org/licenses/by/4.0/
• 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 (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 automatic metrics 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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