Previous studies have shown automatic evaluation metrics to be more reliable when compared against many human translations. However, multiple human references may not always be available. It is more common to have only a single human reference (extracted from parallel texts) or no reference at all. Our earlier work suggested that one way to address this problem is to train a metric to evaluate a sentence by comparing it against pseudo references, or imperfect “references ” produced by off-the-shelf MT systems. In this paper, we further examine the approach both in terms of the training methodology and in terms of the role of the human and pseudo references. Our expanded experiments show that the approach generalizes well across multiple years and different source languages.
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