Empirical Translation Process Research: Past and Possible Future Perspectives

TL;DR

This paper reviews the evolution of empirical translation process research and introduces a novel framework based on the Free Energy Principle and Active Inference to model deep, embedded translation processes.

Contribution

It proposes a new theoretical framework using FEP and AIF for modeling complex translation processes, connecting relevance theory and the Monitor Model.

Findings

Relevance, s-mode, i-mode quantified using new approaches

Relevance maximization linked to free energy minimization

Framework enables modeling of deep temporal translation architectures

Abstract

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.