Papers by Jihyeon Lee
PePe: Personalized Post-editing Model utilizing User-generated Post-edits (2023.findings-eacl)
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| Challenge: | Existing neural machine translation models ignore personal style in their translations, but in these studies the definition of personal style is over-simplified. |
| Approach: | They propose a personalized automatic post-editing framework that generates sentences considering distinct personal behaviors by collecting post-edited data from a live machine translation system and combining a discriminator module and user-specific parameters. |
| Outcome: | The proposed model outperforms baseline models on four different metrics including BLEU, TER, YiSi-1, and human evaluation. |
Diagnosis of Dysarthria Severity and Explanation Generation Using XAI-Enhanced CLINIC-GENIE on Diadochokinetic Tasks (2026.findings-eacl)
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| Challenge: | Recent deep learning approaches for dysarthria impairment severity lack interpretability essential for clinical applications. |
| Approach: | They propose a deep neural network classifier that integrates acoustic and speech embeddings with Clinically Explainable Acoustic Features (CEAFs) and a module that transforms CEAFs and their Shapley values into intuitive natural language explanations. |
| Outcome: | The proposed model achieves a balanced accuracy of 0.952 (17.3% improvement over using CEAFs alone) and certified speech-language pathologists rated explanations with an average fidelity score of 4.94, confirming enhanced clinical utility. |