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Literary translation is a culturally significant task, but it is bottleneckedby the small number of qualified literary translators relative to the manyuntranslated works published around the world. Machine translation (MT) holdspotential to complement the work of human translators by improving bothtraining procedures and their overall efficiency. Literary translation is lessconstrained than more traditional MT settings since translators must balancemeaning equivalence, readability, and critical interpretability in the targetlanguage. This property, along with the complex discourse-level context presentin literary texts, also makes literary MT more challenging to computationallymodel and evaluate. To explore this task, we collect a dataset (Par3) ofnon-English language novels in the public domain, each aligned at the paragraphlevel to both human and automatic English translations. Using Par3, we discoverthat expert literary translators prefer reference human translations overmachine-translated paragraphs at a rate of 84%, while state-of-the-artautomatic MT metrics do not correlate with those preferences. The experts notethat MT outputs contain not only mistranslations, but also discourse-disruptingerrors and stylistic inconsistencies. To address these problems, we train apost-editing model whose output is preferred over normal MT output at a rate of69% by experts. We publicly release Par3 athttps://github.com/katherinethai/par3/ to spur future research into literaryMT.

Exploring Document-Level Literary Machine Translation with Parallel Paragraphs from World Literature