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arXiv · 2203.15108

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
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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 (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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Shashi Narayan, Gonçalo Simões, Yao Zhao, Joshua Maynez, Dipanjan Das, Michael Collins, Mirella Lapata. 2022-03-28. A Well-Composed Text is Half Done! Composition Sampling for Diverse Conditional Generation. https://arxiv.org/abs/2203.15108

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