  We present a novel approach to detecting over and under translations (OT/UT)
as part of adequacy error checks in translation evaluation. We do not restrict
ourselves to machine translation (MT) outputs and specifically target
applications with human generated translation pipeline. The goal of our system
is to identify OT/UT errors from human translated video subtitles with high
error recall. We achieve this without reference translations by learning a
model on synthesized training data. We compare various classification networks
that we trained on embeddings from pre-trained language model with our best
hybrid network of GRU + CNN achieving 89.3% accuracy on high-quality
human-annotated evaluation data in 8 languages.
