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Ensemble methods, which combine multiple models at decoding time, are now widely known to be effective for text-generation tasks. However, they generally increase computational costs, and thus, there have been many studies on compressing or distilling ensemble models. In this paper, we propose an alternative, simple but effective unsupervised ensemble method, post-ensemble, that combines multiple models by selecting a majority-like output in post-processing. We theoretically prove that ourdoi:10.18653/v1/d18-1449 dblp:conf/emnlp/Kobayashi18 fatcat:xioycfexuver3m2quz2n3azgli