Presented by , Florence Harkness Professor of Religion, with Justin Barber and Michael Hemenway, AI Institute at Iliff School of Theology
Abstract: The broad aim of this project is to explore new possibilities for translation within a post-print digital media environment. Working with emerging technologies of neural machine translation (NMT) and natural language processing (NLP) in the programming language of Python, we are exploring new models and methods for translating Hebrew biblical and other ancient texts. Whereas print translation pushes the translator toward closure, deciding on a single translation and relegating alternatives to footnotes or parentheses, how might new media technologies make it possible to provide readers/users access to the processes of translation, hosting an encounter that attends to the rich ambiguities and polyvocalities of the other text in translation? How might we deploy new media technologies in ways that radically alter translation, not only transforming the processes of translation but also involving users in those processes? Our interests as humanities scholars in ambiguity, polyvocality, and the irreducible otherness of the text in translation fly in the face of the burgeoning industry of NMT (e.g., Google Translate). Whereas the consumer-oriented goal of NMT is to erase ambiguity and make the processes of translation invisible and immediate (so users barely realize translation is taking place), we aim to build models using NMT and other NLP tools perversely, to slow down and make visible the complex processes of translation, in order to invite users to participate in those processes.