ABSTRACT
This volume champions sustainability of our world’s cultures and languages while exploring the role of translation in that sustenance. My contribution will focus on conservation of a culture through preservation of its literature, and more specifically on use of machine translation (MT) and artificial intelligence (AI) for literary translation. Such translation strives to somehow preserve the essence of a work while carrying it over to a different language and culture and giving it rebirth there. To recognize that essence, the translator must accurately capture the meaning of the original; appreciate its metaphors, connotations, register, references, and other abstract or associative factors; and choose among available target language expressions by exercising esthetic judgments. Computers, however, presently remain incapable of such accuracy, abstraction, and judgment. This chapter revisits these shortfalls in light of developments in MT and AI. We tease apart several separable aspects of literary translation – literal meaning, meter, rhyme, and the abovementioned associative elements, with reference to arguments about Vladimir Nabokov’s hyper-literal translation of Pushkin’s poem Eugene Onegin. Then we discuss several avenues for improvement in MT which may help to extract these aspects of a text’s essence – first, those which may enhance textually grounded MT (i.e., MT trained on text only), leading to delivery of high-quality literal translations; and second, those related to future perceptually grounded MT (i.e., MT trained on simulated perception, e.g., of audiovisual input, as well as text), which might extract more abstract or associative elements of a text. We suggest that recognition of perceptually grounded categories will prove central to the essence extraction sought by translators. As this categorization improves, MT should increasingly support literary, and thus cultural, preservation. However, artificial esthetic judgments will await artificial emotion.
