Jung In Koh

Junyoung Koh, Jaeyun Lee, Soo Yong Kim et al.

Recent work on music question answering (Music-QA) has primarily focused on single-track understanding, where models answer questions about an individual audio clip using its tags, captions, or metadata. However, listeners often describe music in comparative terms, and existing benchmarks do not systematically evaluate reasoning across multiple tracks. Building on the Jamendo-QA dataset, we introduce Jamendo-MT-QA, a dataset and benchmark for multi-track comparative question answering. From Creative Commons-licensed tracks on Jamendo, we construct 36,519 comparative QA items over 12,173 track pairs, with each pair yielding three question types: yes/no, short-answer, and sentence-level questions. We describe an LLM-assisted pipeline for generating and filtering comparative questions, and benchmark representative audio-language models using both automatic metrics and LLM-as-a-Judge evaluation.

Paul Taele, Jung In Koh, Tracy Hammond

Kanji script writing is a skill that is often introduced to novice Japanese foreign language students for achieving Japanese writing mastery, but often poses difficulties to students with primarily English fluency due to their its vast differences with written English. Instructors often introduce various pedagogical methods -- such as visual structure and written techniques -- to assist students in kanji study, but may lack availability providing direct feedback on students' writing outside of class. Current educational applications are also limited due to lacking richer instructor-emulated feedback. We introduce Kanji Workbook, a writing-based intelligent tutoring system for students to receive intelligent assessment that emulates human instructor feedback. Our interface not only leverages students' computing devices for allowing them to learn, practice, and review the writing of prompted characters from their course's kanji script lessons, but also provides a diverse set of writing assessment metrics -- derived from instructor interviews and classroom observation insights -- through intelligent scoring and visual animations. We deployed our interface onto novice- and intermediate-level university courses over an entire academic year, and observed that interface users on average achieved higher course grades than their peers and also reacted positively to our interface's various features.