  While traditional corpus-level evaluation metrics for machine translation
(MT) correlate well with fluency, they struggle to reflect adequacy.
Model-based MT metrics trained on segment-level human judgments have emerged as
an attractive replacement due to strong correlation results. These models,
however, require potentially expensive re-training for new domains and
languages. Furthermore, their decisions are inherently non-transparent and
appear to reflect unwelcome biases. We explore the simple type-based classifier
metric, MacroF1, and study its applicability to MT evaluation. We find that
MacroF1 is competitive on direct assessment, and outperforms others in
indicating downstream cross-lingual information retrieval task performance.
Further, we show that MacroF1 can be used to effectively compare supervised and
unsupervised neural machine translation, and reveal significant qualitative
differences in the methods' outputs.
