Author name cluster

Kyohoon Jin

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

EAAI Journal 2025 Journal Article

Korean football in-game conversation state tracking dataset for dialogue and turn level evaluation

Sangmin Song
Juhyoung Park
Juhwan Choi
Junho Lee
Kyohoon Jin
YoungBin Kim

Recent research in dialogue state tracking has made significant progress in tracking user goals through dialogue-level and turn-level approaches, but existing research primarily focused on predicting dialogue-level belief states. In this study, we present the KICK: Korean football In-game Conversation state tracKing dataset, which introduces a conversation-based approach. This approach leverages the roles of casters and commentators within the self-contained context of sports broadcasting to examine how utterances impact the belief state at both the dialogue-level and turn-level. Towards this end, we propose a task that aims to track the states of a specific time turn and understand conversations during the entire game. The proposed dataset comprises 228 games and 2463 events over one season, with a larger number of tokens per dialogue and turn, making it more challenging than existing datasets. Experiments revealed that the roles and interactions of casters and commentators are important for improving the zero-shot state tracking performance. By better understanding role-based utterances, we identify distinct approaches to the overall game process and events at specific turns.

Details DOI

AAAI Conference 2025 System Paper

SummPilot: Bridging Efficiency and Customization for Interactive Summarization System

JungMin Yun
Juhwan Choi
Kyohoon Jin
Soojin Jang
Jinhee Jang
YoungBin Kim

This paper incorporates the efficiency of automatic summarization and addresses the challenge of generating personalized summaries tailored to individual users' interests and requirements. To tackle this challenge, we introduce SummPilot, an interaction-based customizable summarization system. SummPilot leverages a large language model to facilitate both automatic and interactive summarization. Users can engage with the system to understand document content and personalize summaries through interactive components such as semantic graphs, entity clustering, and explainable evaluation. Our demo and user studies demonstrate SummPilot's adaptability and usefulness for customizable summarization.

PDF Details DOI

EAAI Journal 2023 Journal Article

Weakly supervised semantic segmentation via Graph RecalibratiOn with Scaling Weight uNit

Soojin Jang
JuneHyoung Kwon
Kyohoon Jin
YoungBin Kim

Weakly supervised semantic segmentation (WSSS) has garnered considerable attention for its efficacy in generating pixel-level labels using weak labels. Class activation maps (CAMs) are utilized by WSSS to generate pseudo-masks from image-level labels. However, these CAMs primarily focus on the most discriminative features of an object, while less discriminative features may be ignored or unidentified. Due to co-occurring pixels, it may also be impossible to distinguish between the foreground and background. In this paper, we propose a method referred to as Graph RecalibratiOn with Scaling Weight uNit (GROWN) to address these challenges. It illustrates the relation between local and global features by utilizing graph structure. Adaptively representing the image’s semantic features is possible by scaling weights that aggregate contextual features. The proposed method successfully captures long-range dependencies and extracts contextual features to improve the pseudo-mask quality. As a result, the proposed method can predict pixel-level labels effectively. The datasets PASCAL VOC 2012 and MS COCO were utilized in the experiments. GROWN outperforms state-of-the-art WSSS methods that employ image-level labels, as demonstrated by the results.

Details DOI