Papers by Jinhyeon Kim
Analysis of Multi-Source Language Training in Cross-Lingual Transfer (2024.acl-long)
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| Challenge: | Existing studies on cross-lingual transfer (XLT) methods address data scarcity problem . cross-linguistic transfer (xLT) techniques are effective at fine-tuning multilingual LMs . |
| Approach: | They propose to use multiple source languages to improve XLT by fine-tuning multilingual models . they propose to employ arbitrary combinations of source languages for XL to improve performance . |
| Outcome: | The proposed technique improves performance on language-agnostic or task-specific features by using multiple source languages. |
X-SNS: Cross-Lingual Transfer Prediction through Sub-Network Similarity (2023.findings-emnlp)
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| Challenge: | Cross-lingual transfer (XLT) is an emergent ability of multilingual language models that preserves their performance when evaluated in non-English languages. |
| Approach: | They propose to use sub-network similarity between two languages as a proxy for XLT prediction. |
| Outcome: | The proposed method shows proficiency in ranking candidates for zero-shot XLT, achieving an improvement of 4.6% on average in terms of NDCG@3. |
Learning to Embed Multi-Modal Contexts for Situated Conversational Agents (2022.findings-naacl)
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Haeju Lee, Oh Joon Kwon, Yunseon Choi, Minho Park, Ran Han, Yoonhyung Kim, Jinhyeon Kim, Youngjune Lee, Haebin Shin, Kangwook Lee, Kee-Eung Kim
| Challenge: | Situated Interactive Multi-Modal Conversations 2.0 aims to create virtual shopping assistants that can accept complex multi-modal inputs. |
| Approach: | They propose a joint learning approach that integrates visual inputs and performs all four subtasks at once for efficiency. |
| Outcome: | The proposed approach won the 10th Dialog Systems Technology Challenge (DSTC10) . it incorporates visual inputs and performs all four subtasks at once for efficiency . |