Multi-Dimensional Machine Translation Evaluation: Model Evaluation and
  Resource for Korean
Dojun Park Sebastian Padó
Almost all frameworks for the manual or automatic evaluation of machine
translation characterize the quality of an MT output with a single number. An
exception is the Multidimensional Quality Metrics (MQM) framework which offers
a fine-grained ontology of quality dimensions for scoring (such as style,
fluency, accuracy, and terminology). Previous studies have demonstrated the
feasibility of MQM annotation but there are, to our knowledge, no computational
models that predict MQM scores for novel texts, due to a lack of resources. In
this paper, we address these shortcomings by (a) providing a 1200-sentence MQM
evaluation benchmark for the language pair English-Korean and (b) reframing MT
evaluation as the multi-task problem of simultaneously predicting several MQM
scores using SOTA language models, both in a reference-based MT evaluation
setup and a reference-free quality estimation (QE) setup. We find that
reference-free setup outperforms its counterpart in the style dimension while
reference-based models retain an edge regarding accuracy. Overall, RemBERT
emerges as the most promising model. Through our evaluation, we offer an
insight into the translation quality in a more fine-grained, interpretable
manner.