The Translator’s Extended Mind

An Active Inference Agent for Modeling Human Translation Processes

This paper develops an outline for a hierarchically embedded architecture of an artificial agent that models human translation processes based on principles of active inference (AIF) and predictive processing (PP). AIF and PP posit that the mind constructs a model of the environment which guides behavior by continually generating and integrating predictions and sensory input. The proposed model of the translation agent consists of three processing strata: a sensorimotor layer, a cognitive layer, and a phenomenal layer. Each layer consists of a network of states and transitions that interact on different time scales. Following the AIF framework, states are conditioned on observations which may originate from the environment and/or the embedded processing layer, while transitions between states are conditioned on actions that implement plans to optimize goal-oriented behavior. The AIF agent aims at simulating the variation in translational behavior under various conditions and to facilitate investigating the underlying mental mechanisms. It provides a novel framework for generating and testing new hypotheses of the translating mind.

Artificial Intelligence-based Machine English-Assisted Translation in the Internet of Things Environment

With the development of Internet of Things technology, the things that machines do instead of humans are becoming more and more complicated. Machine translation has developed rapidly in the past few decades, and the translation system has also been greatly improved. People's life and work are inseparable from machine translation, which brings a lot of convenience to people. But machine translation also has many flaws. Although machine translation can translate long texts in a very short time, its translation quality is quite poor, especially in the face of advanced English such as professional English, terminology, abbreviations, etc. To this end, machine English-assisted translation systems have been developed in recent years. Different from the working principle of machine English translation, machine English-assisted translation is a method of artificial intelligence + human-computer interaction. It uses convolutional neural networks and deep learning to translate words efficiently. The translator puts the original text and the translation into the machine database each time, and the machine can process some English terms, complex sentences, technical English, and other advanced English after continuous learning. Machine English-assisted translation can reduce repeated translations and greatly improve translation quality and translation efficiency. In this paper, the combination of artificial intelligence and machine English-assisted translation is compared with machine English translation, and comparative experiments are carried out by setting different matching degrees. Experiments show that the translation efficiency of machine English-assisted translation is much better than that of machine English translation. As the matching rate increases, the translation efficiency of machine English-assisted translation is higher. When the matching rate is greater than 80%, the translation efficiency is three times that of machine English translation. However, it is slightly insufficient in processing pure, simple statements. It highlights the advantages of machine English-assisted translation in terms of term translation and long complex sentences.