This paper investigates the role of tutor feedback in language learning using computational models. We compare two dominant paradigms in language learning: interactive learning and cross-situational learning - which differ primarily in the role of social feedback such as gaze or pointing. We analyze the relationship between these two paradigms and propose a new mixed paradigm that combines the two paradigms and allows to test algorithms in experiments that combine no feedback and social feedback. To deal with mixed feedback experiments, we develop new algorithms and show how they perform with respect to traditional knn and prototype approaches.
SingaKids: A Multilingual Multimodal Dialogic Tutor for Language Learning
Nanyang Technological University, Singapore（新加坡南洋理工大学）；National Institute of Education (NIE), Singapore（新加坡国家教育研究所）；Institute for Infocomm Research (I 2 R), A*STAR, Singapore（新加坡资讯与通信研究院（I2R））
Catching The Correct Answer Trap: Characterising AI Tutor Blind Spots When Analysing Student Reasoning
Department of Computer Science, University College London, UK（英国伦敦大学学院计算机科学系）；Centre for Artificial Intelligence, University College London, UK（英国伦敦大学学院人工智能中心）
