Automatic metrics are commonly used as the exclusive tool for declaring the superiority of one machine translation system's quality over another. The community choice of automatic metric guides research directions and industrial developments by deciding which models are deemed better. Evaluating metrics correlations has been limited to a small collection of human judgements. In this paper, we corroborate how reliable metrics are in contrast to human judgements on - to the best of our knowledge - the largest collection of human judgements. We investigate which metrics have the highest accuracy to make system-level quality rankings for pairs of systems, taking human judgement as a gold standard, which is the closest scenario to the real metric usage. Furthermore, we evaluate the performance of various metrics across different language pairs and domains. Lastly, we show that the sole use of BLEU negatively affected the past development of improved models. We release the collection of human judgements of 4380 systems, and 2.3 M annotated sentences for further analysis and replication of our work.

该研究对机器翻译中评估标准的可靠性进行了探究，发现使用自动指标作为唯一评判标准可能导致错误决策，需要依赖人工判断作为参考，并发布了收集到的大规模人工翻译质量评价数据集，以供进一步研究。

自动机器翻译评估指标的全面评估：是否需要进行交付