Papers by Dongsuk Jang
I Know What You Asked: Graph Path Learning using AMR for Commonsense Reasoning (2020.coling-main)
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| Challenge: | a large amount of pre-defined commonsense knowledge is available for commonsensense reasoning . humans acquire commonsence in their lives, but machines cannot learn commonseense without assistance. |
| Approach: | They propose an AMR-ConceptNet-Pruned (ACP) graph that is pruned from a full integrated graph . they show that the ACP graph interprets the reasoning path and predicts the correct answer . |
| Outcome: | The proposed graph outperforms baseline models in the commonsenseQA task . it shows that the reasoning path can be interpreted with the relations and concepts provided by the graph . |
MedTutor: A Retrieval-Augmented LLM System for Case-Based Medical Education (2025.emnlp-demos)
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Dongsuk Jang, Ziyao Shangguan, Kyle Tegtmeyer, Anurag Gupta, Jan T Czerminski, Sophie Chheang, Arman Cohan
| Challenge: | Existing educational tools for medical residents are time-consuming and inconsistent. |
| Approach: | They propose a system that generates educational content and multiple-choice questions from clinical case reports and a pipeline that takes clinical case report input and produces targeted educational materials. |
| Outcome: | The system generates educational content and multiple-choice questions from clinical case reports and synergizes with local knowledge base to ensure it is foundationally sound and current. |
CLEAR: Cross-Lingual Enhancement in Retrieval via Reverse-training (2026.acl-long)
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| Challenge: | Existing multilingual embedding models often struggle to capture cross-lingual alignment during training. |
| Approach: | They propose a novel loss function that leverages an English passage as a bridge to strengthen alignments between target language and English. |
| Outcome: | The proposed model improves retrieval performance across cross-lingual scenarios while minimizing performance degradation in English. |