Design of Intelligent Educational Mobile Apps with an Original Dataset for Chinese-Portuguese Translators

Abstract

Translation remains a vital process in many culturally diverse countries. Despite significant advances in artificial intelligence (AI) technology, machine translation currently lacks the ability to fully replace human expertise, requiring continued human intervention and review in translation workflows. This article introduces an innovative mobile education application (app) designed to train translators, with a particular focus on Chinese-Portuguese translation. This app uses a set of practice data, Chinese-Portuguese translation exercise corpus (CPTEC), developed by our corpus team to autonomously assess and identify translation quality defects, thereby promoting skill improvement. We also propose a novel hybrid grade system based on different translation quality assessment (TQA) dimensions to automatically evaluate translations by imitating humans. In addition, it demonstrates the design of challenging exercises within a mobile app to reinforce translation proficiency. To optimize the functionality of the mobile app, we use a large language model (LLM) to validate the solution, ensure that it learns the training material provided and track its performance. Subsequent experimental results show that the fine-tuned LLM improves on multiple dimensions (including accuracy, fidelity, fluency, readability, acceptability, and usability) compared to the initial state, confirming the effectiveness of the developed practice data in improving translation performance. To promote access to research, the practice data (CPTEC) will be distributed within the relevant AI community, to inspire people to create innovative software applications to support translators.