HUME: Human UCCA-Based Evaluation of Machine Translation
This provides a more fine-grained analysis of translation quality for MT researchers, though it is incremental as it builds on existing semantic representation schemes.
The paper tackles the problem of evaluating machine translation by introducing HUME, a semantics-based human evaluation measure that assesses which meaning components are retained in translations, and reports good inter-annotator agreement and correlation with human adequacy scores across four language pairs.
Human evaluation of machine translation normally uses sentence-level measures such as relative ranking or adequacy scales. However, these provide no insight into possible errors, and do not scale well with sentence length. We argue for a semantics-based evaluation, which captures what meaning components are retained in the MT output, thus providing a more fine-grained analysis of translation quality, and enabling the construction and tuning of semantics-based MT. We present a novel human semantic evaluation measure, Human UCCA-based MT Evaluation (HUME), building on the UCCA semantic representation scheme. HUME covers a wider range of semantic phenomena than previous methods and does not rely on semantic annotation of the potentially garbled MT output. We experiment with four language pairs, demonstrating HUME's broad applicability, and report good inter-annotator agreement rates and correlation with human adequacy scores.