  We propose a simple and effective method for machine translation evaluation
which does not require reference translations. Our approach is based on (1)
grounding the entity mentions found in each source sentence and candidate
translation against a large-scale multilingual knowledge base, and (2)
measuring the recall of the grounded entities found in the candidate vs. those
found in the source. Our approach achieves the highest correlation with human
judgements on 9 out of the 18 language pairs from the WMT19 benchmark for
evaluation without references, which is the largest number of wins for a single
evaluation method on this task. On 4 language pairs, we also achieve higher
correlation with human judgements than BLEU. To foster further research, we
release a dataset containing 1.8 million grounded entity mentions across 18
language pairs from the WMT19 metrics track data.
