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This research presents a fine-grained human evaluation to compare theTransformer and recurrent approaches to neural machine translation (MT), on thetranslation direction English-to-Chinese. To this end, we develop an errortaxonomy compliant with the Multidimensional Quality Metrics (MQM) frameworkthat is customised to the relevant phenomena of this translation direction. Wethen conduct an error annotation using this customised error taxonomy on theoutput of state-of-the-art recurrent- and Transformer-based MT systems on asubset of WMT2019's news test set. The resulting annotation shows that,compared to the best recurrent system, the best Transformer system results in a31% reduction of the total number of errors and it produced significantly lesserrors in 10 out of 22 error categories. We also note that two of the systemsevaluated do not produce any error for a category that was relevant for thistranslation direction prior to the advent of NMT systems: Chinese classifiers.

Fine-grained Human Evaluation of Transformer and Recurrent Approaches to Neural Machine Translation for English-to-Chinese