Experience is the Best Teacher: Personalized Vocabulary Building Within the Context of Instagram Posts and Sentences from GPT-3
Kanta Yamaoka, Ko Watanabe, Koichi Kise, Andreas Dengel, Shoya Ishimaru
Abstract
Although language learners have different contexts and motivations, sensing personal backgrounds to optimize learning materials has still been challenging. By focusing on the huge movement of Social Networking Services (SNS) such as Instagram, we came up with the idea of utilizing social posts, in particular images, as learning materials. This paper presents our working prototype of the proposed system that extracts keywords from these images and leverages GPT-3 to generate sentences for acquiring new vocabulary around the keywords. By conducting a pilot study involving three users, we found that 2.2 words appeared as unknown words for the user in one generated sentence on average, and there is room for improvement in the proposed system. These findings can be utilized in a large-scale evaluation designed in the future.