Follow the Wisdom of the Crowd: Effective Text Generation via Minimum Bayes Risk Decoding

• URL: http://arxiv.org/abs/2211.07634v1
• Date: Mon, 14 Nov 2022 18:57:37 GMT
• Title: Follow the Wisdom of the Crowd: Effective Text Generation via Minimum Bayes Risk Decoding
• Authors: Mirac Suzgun, Luke Melas-Kyriazi, Dan Jurafsky
• Abstract summary: We present crowd sampling, a family of decoding methods based on Bayesian risk minimization. Crowd sampling seeks to select a candidate from a pool of candidates that has the least expected risk. Experiments show that crowd sampling delivers improvements of 3-7 ROUGE and BLEU points across a wide range of tasks.
• Score: 27.454582992694974
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In open-ended natural-language generation, existing text decoding methods typically struggle to produce text which is both diverse and high-quality. Greedy and beam search are known to suffer from text degeneration and linguistic diversity issues, while temperature, top-k, and nucleus sampling often yield diverse but low-quality outputs. In this work, we present crowd sampling, a family of decoding methods based on Bayesian risk minimization, to address this diversity-quality trade-off. Inspired by the principle of "the wisdom of the crowd," crowd sampling seeks to select a candidate from a pool of candidates that has the least expected risk (i.e., highest expected reward) under a generative model according to a given utility function. Crowd sampling can be seen as a generalization of numerous existing methods, including majority voting, and in practice, it can be used as a drop-in replacement for existing sampling methods. Extensive experiments show that crowd sampling delivers improvements of 3-7 ROUGE and BLEU points across a wide range of tasks, including summarization, data-to-text, translation, and textual style transfer, while achieving new state-of-the-art results on WebNLG and WMT'16.

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