Papers by Changki Lee
KNU-HYUNDAI’s NMT system for Scientific Paper and Patent Tasks onWAT 2019 (D19-52)
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Cheoneum Park, Young-Jun Jung, Kihoon Kim, Geonyeong Kim, Jae-Won Jeon, Seongmin Lee, Junseok Kim, Changki Lee
| Challenge: | We submitted our transformer-based neural machine translation system to the translation tasks of the 6th workshop on Asian Translation (WAT 2019). |
| Approach: | They propose a transformer-based neural machine translation system for Chinese-Japanese, English-Japanese, and Korean->Japanoise translation tasks. |
| Outcome: | The proposed system performed well on the two translation tasks and was ranked first in terms of the BLEU scores in all the JPC2 subtasks. |
Fast End-to-end Coreference Resolution for Korean (2020.findings-emnlp)
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| Challenge: | Recent advances in coreference resolution have come at a cost of computational complexity and have not been addressed. |
| Approach: | They propose a pointer network that leverages the linguistic property of head-final languages to reduce coreference linking search space and achieve 2x speedup in document processing time. |
| Outcome: | The proposed model maintains state-of-the-art performance 66.9% of CoNLL F1 on ETRI test set while achieving 2x speedup (30 doc/sec) in document processing time. |
Rule Discovery for Natural Language Inference Data Generation Using Out-of-Distribution Detection (2025.emnlp-main)
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| Challenge: | Existing training rules for natural language inference do not cover the diversity of natural language. |
| Approach: | They propose a framework that combines out-of-distribution detection and clustering to identify new premise–hypothesis pairs in a dataset that are not covered by existing rules. |
| Outcome: | The proposed framework achieves +0.85%p accuracy on 2k and +0.15%p on 550k samples. |