Papers by Youjin Kang
“Why do I feel offended?” - Korean Dataset for Offensive Language Identification (2023.findings-eacl)
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| Challenge: | Existing methods for detecting offensive content rely on labeled datasets, but few consider low-resource languages with relatively less data available for training. |
| Approach: | They propose to use Korean as a dataset for offensive language identification . they propose to perform abusive language detection and sentiment analysis to help identify offensive languages. |
| Outcome: | The proposed datasets improve the performance of offensive language identification in Korean, while the existing methods are limited. |
DIVE: Towards Descriptive and Diverse Visual Commonsense Generation (2023.emnlp-main)
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| Challenge: | Towards human-level visual understanding, visual commonsense generation has been introduced . but current research on visual commonense generation ignores an important human cognitive ability . |
| Approach: | They propose a visual commonsense generation framework to improve inferences by visual common sense generation. |
| Outcome: | The proposed framework outperforms state-of-the-art models in descriptiveness and diversity . human evaluations confirm that the framework aligns closely with human judgments on descriptiveness . |
Break it Down into BTS: Basic, Tiniest Subword Units for Korean (2022.emnlp-main)
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| Challenge: | Existing word embeddings for Korean use the internal structure of words with subword information to improve the quality of word representations. |
| Approach: | They introduce Basic, Tiniest Subword (BTS) units for Korean language that are inspired by Hangeul, the Korean writing system. |
| Outcome: | The proposed framework outperforms the state-of-the-art Korean word embedding by 11.8% on all intrinsic and extrinsic tasks. |
DaCoM: Strategies to Construct Domain-specific Low-resource Language Machine Translation Dataset (2025.coling-industry)
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| Challenge: | Existing models for low-resource languages struggle with domain-specific terms and lack of expert annotators for dataset creation. |
| Approach: | They propose a method for collecting low-resource language pairs from industrial domains using a large language model and neural machine translation framework. |
| Outcome: | The proposed model performs poorly on DaCoM-created datasets with up to 53.7 BLEURT points difference depending on domain inclusion. |
Distilling Cross-Modal Knowledge into Domain-Specific Retrievers for Enhanced Industrial Document Understanding (2025.emnlp-industry)
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Jinhyeong Lim, Jeongwan Shin, Seeun Lee, Seongdeok Kim, Joungsu Choi, Jongbae Kim, Chun Hwan Jung, Youjin Kang
| Challenge: | Retrieval-Augmented Generation (RAG) has shown strong performance in open-domain tasks, but its effectiveness in industrial domains is limited by a lack of domain understanding and document structural elements (DSE) such as tables, figures, charts, and formula. |
| Approach: | They propose a knowledge distillation framework that transfers complementary knowledge from Large Language Models and Vision-Language Models into a compact domain-specific retriever. |
| Outcome: | The proposed framework outperforms larger baselines while requiring significantly less computational complexity. |