Mingi Kim — accepted AI-conference papers

2025

Learning to See through Sound: From VggCaps to Multi2Cap for Richer Automated Audio Captioning

Sangyeon Cho, Mingi Kim, Jinkwon Hwang, Jaehoon Go, Minuk Ma, Sunjae Yoon +1

EMNLP 2025

Automated Audio Captioning (AAC) aims to generate natural language descriptions of audio content, enabling machines to interpret and communicate complex acoustic scenes. However, current AAC datasets often suffer from short and simplistic captions, limiting model expressiveness and semantic depth. T

Cited by 0 Source ↗PDF ↗Scholar ↗

2024

KoCoSa: Korean Context-aware Sarcasm Detection Dataset

Yumin Kim, Heejae Suh, Mingi Kim, Dongyeon Won, Hwanhee Lee

COLING 2024main

Sarcasm is a way of verbal irony where someone says the opposite of what they mean, often to ridicule a person, situation, or idea. It is often difficult to detect sarcasm in the dialogue since detecting sarcasm should reflect the context (i.e., dialogue history). In this paper, we introduce a new d…

Cited by 3 Source ↗PDF ↗code ↗Scholar ↗