TechXplore: “Recent advancements in the field of machine learning (ML) have greatly improved the quality of automatic translation tools. At present, these tools are primarily used to translate basic sentences, as well as short texts or unofficial documents. Literary texts, such as novels or short stories, are still fully translated by expert human translators, who are experienced in grasping abstract and complex meanings and translating them in another language. While a few studies have investigated the potential of computational models for translating literary texts, findings in this area are still limited. Researchers at UMass Amherst have recently carried out a study exploring the quality of literary text translations produced by machines, by comparing them with same text-translations created by humans. Their findings, pre-published on arXiv, highlight some of the shortcomings of existing computational models to translate foreign texts into English.
Machine translation (MT) holds potential to complement the work of human translators by improving both training procedures and their overall efficiency,” Katherine Thai and her colleagues wrote in their paper. “Literary translation is less constrained than more traditional MT settings since translators must balance meaning equivalence, readability, and critical interpretability in the target language. This property, along with the complex discourse-level context present in literary texts, also makes literary MT more challenging to computationally model and evaluate.”