Papers by Seoyeon Park
Can Code-Switched Texts Activate a Knowledge Switch in LLMs? A Case Study on English-Korean Code-Switching (2025.findings-emnlp)
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| Challenge: | Recent large language models (LLMs) demonstrate multilingual abilities, yet they are English-centric due to dominance of English in training corpora. |
| Approach: | They propose to use a synthetic English-korean CS question-answering dataset to investigate this potential. |
| Outcome: | The proposed model can activate, identify and leverage knowledge for reasoning in low-resource languages. |
Extending CLIP’s Image-Text Alignment to Referring Image Segmentation (2024.naacl-long)
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| Challenge: | Referring Image Segmentation (RIS) is a cross-modal task that aims to segment an instance described by a natural language expression. |
| Approach: | They propose a framework that leverages the cross-modal nature of CLIP for RIS by leveraging image-text alignment knowledge in CLIP's image-embedding space. |
| Outcome: | The proposed framework outperforms CLIP-based methods on all three major RIS benchmarks and outperformed previous CLIP methods. |
Scientific Keyphrase Identification and Classification by Pre-Trained Language Models Intermediate Task Transfer Learning (2020.coling-main)
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| Challenge: | Scientific keyphrase identification and classification is challenging due to lack of labeled data for training deep neural networks. |
| Approach: | They propose to use pre-trained language models BERT and SciBERT to train scientific keyphrase identification and classification using intermediate task transfer learning. |
| Outcome: | The proposed model achieves competitive performance in scientific keyphrase identification and classification compared to both previous studies and strong baselines. |