We investigate the impact of the Plain English Movement (PEM) on the complexity of legal language in UK law reports from the 1950s-2010s, contrasting it with the evolution of scientific language. The PEM, emerging in the late 20th century, advocated for clear and understandable legal language. We define complexity through the concept of surprisal - an information-theoretic measure correlating with cognitive processing difficulty. Our research contrasts surprisal with traditional readability measures, which often overlook content. We hypothesize that, if the PEM has influenced legal language, there would be a reduction in complexity over time and a shift from a nominal to a more verbal style. We analyze text complexity and lexico-grammatical changes in line with PEM recommendations. Results indicate minimal impact of the PEM on both legal and scientific domains. This finding suggests future research should consider processing effort when advocating for linguistic norms to enhance accessibility.

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.