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We examine a lengthy drawback in the typical use of BLEU: its confidence in a single reference. With modern neuronal paraphrasing techniques, we study whether automatic generation of additional diverse references offer better coverage of the room of valid translations and thereby improve their correlation with human judgments. Our experiments in English-speaking directions of the WMT19 metric task (at the system and record level) show that the use of paraphrased references usually improve BLEU, and if this is the case, the more the better. However, we also show that better results could be achieved if these paraphrases could specifically target the parts of the room for the MT outlets that are evaluated. In addition, the profits remain easily, even if human paraphrases are used, thereby proposing inherent restrictions of the LEU capability to properly exploit multiple references. Surprisingly, we also find that the adequacy appears less important, as shown in the high results of a strong sampling approach, which even gives human paraphrases when used with the set level BLEU.