Knowledge Generation through TMS’s; Phrase as a case study

Abstract

This study aims to examine Translation Management Systems (TMS) and their role in knowledge generation by exploring the mechanisms for organizing and automating the process of machine-based knowledge generation on the Phrase platform, as well as evaluating the effectiveness of artificial intelligence integration in this context. The study also seeks to assess the reliability and security of data on the Phrase platform and to identify the core values and principles it upholds in knowledge generation and the management of scientific terminology. The researchers employed a content analysis methodology to investigate the platform’s role in knowledge generation. The study concluded with several findings, most notably that Phrase supports the four stages of knowledge creation as outlined in the SECI model. The Socialization stage was evident in the exchange of experiences among translators through forums and workshops within the platform. Externalization appeared through the documentation of translators’ experiences in the form of articles, guides, and manuals, as well as through the use of artificial intelligence in translation and analysis. Combination was observed through the integration of linguistic databases, terminology resources, and translation tools with various sources to develop more accurate translation models, alongside the use of machine learning techniques to build an interactive translation database. Finally, Internalization was reflected in the training of translators through educational courses based on stored explicit knowledge, aimed at enhancing performance. The researchers recommend that institutions involved in knowledge generation should leverage translation management systems such as Phrase, capitalizing on its data reliability, security features, and its foundational values and principles in knowledge generation and terminology management. Additionally, they recommend the inclusion of dedicated courses on translation management systems within university-level translation programs.