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Zero-shot neural machine translation is an attractive goal because of the high cost of obtaining data and building translation systems for new translation directions. However, previous papers have reported mixed success in zero-shot translation. It is hard to predict in which settings it will be effective, and what limits performance compared to a fully supervised system. In this paper, we investigate zero-shot performance of a multilingual EN↔FR,CS,DE,FI system trained on WMT data. We findarXiv:2011.01703v1 fatcat:qoy243alazfp3nq7z2vtrikcfm