This paper offers a fine-grained analysis of the machine translation outputs in the context of the Shared Task at the 8th Conference of Machine Translation (WMT23). Building on the foundation of previous test suite efforts, our analysis includes Large Language Models and an updated test set featuring new linguistic phenomena. To our knowledge, this is the first fine-grained linguistic analysis for the GPT-4 translation outputs. Our evaluation spans German-English, English-German, and English-Russian language directions. Some of the phenomena with the lowest accuracies for German-English are idioms and resultative predicates. For English-German, these include mediopassive voice, and noun formation(er). As for English-Russian, these included idioms and semantic roles. GPT-4 performs equally or comparably to the best systems in German-English and English-German but falls in the second significance cluster for English-Russian.

This document describes a fine-grained linguistically motivated analysis of 29 machine translation systems submitted at the Shared Task of the 7th Conference of Machine Translation (WMT22). This submission expands the test suite work of previous years by adding the language direction of English–Russian. As a result, evaluation takes place for the language directions of German–English, English–German, and English–Russian. We find that the German–English systems suffer in translating idioms, some tenses of modal verbs, and resultative predicates, the English–German ones in idioms, transitive-past progressive, and middle voice, whereas the English–Russian ones in pseudogapping and idioms.