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論文タイトル：Dialogue Attributes’ Zero-Shot Classification Based Anime Scene’s Matching for Japanese Listening Test
著者：Junjie Shan, Yangdi Ni and Yoko Nishihara
概要：
This paper proposes a method to provide Anime dialogue scenes for Japanese listening test training through Zero-shot classification. In this study, listening test dialogues and anime dialogue scenes were categorized by three attributes: speakers’ relationship, dialogue location, and dialogue style. We collected 90 listening dialogues from each level of the past Japanese Language Proficiency Test (JLPT) and manually labeled the attributes of each dialogue. After testing the effect of the zero-shot model on listening dialogues’ classification with 2,143 different sets of label keywords, nine sets with the highest accuracy were identified. We classified 247,645 anime dialogue scenes’ attributes using the nine sets of label keywords and counted the number of anime scenes, word cover rate, and text similarity between anime dialogues and 90 listening tests when different kinds of attributes were matched. The results show that as the number of matched attributes increases, the range of selected anime scenes continues narrowing and being precise while keeping basically the same word cover rate and text similarity. The average number of anime scenes when matching single, double, and triple attributes were 140,988, 70,834, and 29,362, while the word cover rate of matched anime scenes to the input listening dialogues were 96.18%, 95.22%, and 94.51%.
書誌情報：The 12th International Symposium on Information and Communication Technology, pp.966–971
発表日：2023年12月8日