  Current methods for automatically evaluating grammatical error correction
(GEC) systems rely on gold-standard references. However, these methods suffer
from penalizing grammatical edits that are correct but not in the gold
standard. We show that reference-less grammaticality metrics correlate very
strongly with human judgments and are competitive with the leading
reference-based evaluation metrics. By interpolating both methods, we achieve
state-of-the-art correlation with human judgments. Finally, we show that GEC
metrics are much more reliable when they are calculated at the sentence level
instead of the corpus level. We have set up a CodaLab site for benchmarking GEC
output using a common dataset and different evaluation metrics.
