  High-quality Machine Translation (MT) evaluation relies heavily on human
judgments. Comprehensive error classification methods, such as Multidimensional
Quality Metrics (MQM), are expensive as they are time-consuming and can only be
done by experts, whose availability may be limited especially for low-resource
languages. On the other hand, just assigning overall scores, like Direct
Assessment (DA), is simpler and faster and can be done by translators of any
level, but are less reliable. In this paper, we introduce Error Span Annotation
(ESA), a human evaluation protocol which combines the continuous rating of DA
with the high-level error severity span marking of MQM. We validate ESA by
comparing it to MQM and DA for 12 MT systems and one human reference
translation (English to German) from WMT23. The results show that ESA offers
faster and cheaper annotations than MQM at the same quality level, without the
requirement of expensive MQM experts.
