  We introduce the submissions of the NJUNLP team to the WMT 2023 Quality
Estimation (QE) shared task. Our team submitted predictions for the
English-German language pair on all two sub-tasks: (i) sentence- and word-level
quality prediction; and (ii) fine-grained error span detection. This year, we
further explore pseudo data methods for QE based on NJUQE framework
(https://github.com/NJUNLP/njuqe). We generate pseudo MQM data using parallel
data from the WMT translation task. We pre-train the XLMR large model on pseudo
QE data, then fine-tune it on real QE data. At both stages, we jointly learn
sentence-level scores and word-level tags. Empirically, we conduct experiments
to find the key hyper-parameters that improve the performance. Technically, we
propose a simple method that covert the word-level outputs to fine-grained
error span results. Overall, our models achieved the best results in
English-German for both word-level and fine-grained error span detection
sub-tasks by a considerable margin.
