Empirical Translation Process Research: Past and Possible Future Perspectives
This work addresses a gap in translation studies by offering a mathematically rigorous framework that could enhance understanding of human translation processes and inform cognitive architecture design, though it appears incremental as it builds on existing traditions.
The paper tackles the lack of a comprehensive framework in Empirical Translation Process Research by proposing the Free Energy Principle and Active Inference as a foundation, enabling modeling of deep temporal architectures and linking relevance maximization to free energy minimization.
Over the past four decades, efforts have been made to develop and evaluate models for Empirical Translation Process Research (TPR), yet a comprehensive framework remains elusive. This article traces the evolution of empirical TPR within the CRITT TPR-DB tradition and proposes the Free Energy Principle (FEP) and Active Inference (AIF) as a framework for modeling deeply embedded translation processes. It introduces novel approaches for quantifying fundamental concepts of Relevance Theory (relevance, s-mode, i-mode), and establishes their relation to the Monitor Model, framing relevance maximization as a special case of minimizing free energy. FEP/AIF provides a mathematically rigorous foundation that enables modeling of deep temporal architectures in which embedded translation processes unfold on different timelines. This framework opens up exciting prospects for future research in predictive TPR, likely to enrich our comprehension of human translation processes, and making valuable contributions to the wider realm of translation studies and the design of cognitive architectures.