Speaker Biography
Hong Huaqing holds a Ph.D. and is a Professor, Doctoral Supervisor, and Researcher at the Institute of Language Sciences, Shanghai International Studies University. He is also a Researcher at the Learning Research and Development Center, Nanyang Technological University, Singapore, an International Expert at the Center for Teaching and Learning Development, Peking University, and an Honorary Board Member of the China Association for Corpus Linguistics. Dr. Hong has long been based in Singapore for his academic work. Since 2000, he has conducted research in machine translation, information extraction, and artificial intelligence at the Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), Singapore. In 2005, he began leading corpus-based large-scale educational research at the National Institute of Education, Nanyang Technological University. Since 2013, he has been responsible for the development and application of the e-learning system at the Lee Kong Chian School of Medicine, while also serving as a Researcher at the Learning Research and Development Center, Nanyang Technological University, focusing on advancing smart education driven by big data learning analytics and the development of innovative learning ecosystems. His research interests include learning sciences, computer-assisted instruction, machine translation, multimodal discourse analysis, big data learning analytics, and innovative education.
Lecture Time & Venue
Lecture Time: December 29, 19:00–20:30
Meeting Platform: Tencent Meeting ID: 818-341-106; Passcode: 111111
Lecture Title
AI-Driven and Human-Machine Collaboration: The Construction and Application of a Multimodal Transcreation Corpus
Lecture Abstract
Against the backdrop of global brand communication, transcreation—as a cross-cultural communication strategy that goes beyond traditional translation—has become increasingly important. However, current research on multimodal transcreation faces the significant challenge of insufficient data infrastructure. This lecture introduces a pioneering effort: the construction and application of the Multimodal Transcreation Corpus (MTC). It systematically elaborates on how an AI-driven, human-machine collaborative approach is employed to integrate multimodal resources, including text, images, and video, into a comprehensive transcreation corpus, and presents an intelligent teaching and research platform based on the IDEF0 functional modeling framework. Using specific case studies, the lecture will demonstrate how this corpus supports data-driven learning (DDL), drives innovation in translation teaching models, and opens new pathways for cutting-edge research directions such as quantitative analysis of transcreation strategies and exploration of multimodal semiotic interaction mechanisms.
