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For open-ended language generation tasks such as storytelling and dialogue,choosing the right decoding algorithm is critical to controlling the tradeoffbetween generation quality and diversity. However, there presently exists noconsensus on which decoding procedure is best or even the criteria by which tocompare them. We address these issues by casting decoding as a multi-objectiveoptimization problem aiming to simultaneously maximize both response qualityand diversity. Our framework enables us to perform the first large-scaleevaluation of decoding methods along the entire quality-diversity spectrum. Wefind that when diversity is a priority, all methods perform similarly, but whenquality is viewed as more important, the recently proposed nucleus sampling(Holtzman et al. 2019) outperforms all other evaluated decoding algorithms. Ourexperiments also confirm the existence of the `likelihood trap', thecounter-intuitive observation that high likelihood sequences are oftensurprisingly low quality. We leverage our findings to create and evaluate analgorithm called \emph{selective sampling} which tractably approximatesglobally-normalized temperature sampling.

Trading Off Diversity and Quality in Natural Language Generation