Papers by Yeeun Kang
Statement-Tuning Enables Efficient Cross-lingual Generalization in Encoder-only Models (2025.findings-acl)
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Ahmed Elshabrawy, Thanh-Nhi Nguyen, Yeeun Kang, Lihan Feng, Annant Jain, Faadil Abdullah Shaikh, Jonibek Mansurov, Mohamed Fazli Mohamed Imam, Jesus-German Ortiz-Barajas, Rendi Chevi, Alham Fikri Aji
| Challenge: | Large Language Models excel in zero-shot and few-shot tasks, but their architecture makes them difficult to use. |
| Approach: | They adapt Large Language Models (LLMs) for zero-shot generalization using Statement Tuning . they find encoders can achieve zero- shot cross-lingual generalization . |
| Outcome: | The proposed model generalizes well across languages while being more efficient. |
CoVoSwitch: Machine Translation of Synthetic Code-Switched Text Based on Intonation Units (2024.acl-srw)
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| Challenge: | Multilingual code-switching research is often hindered by the lack and linguistically biased status of available datasets. |
| Approach: | They synthesize code-switching data by replacing intonation units detected through PSST, a speech segmentation model fine-tuned from OpenAI’s Whisper, using a language-to-text translation dataset, CoVoST 2. |
| Outcome: | The proposed model outperforms two monolingual models and is better at code-switching translation into English than non-English. |