Papers by Junyoung Jang
DaCoM: Strategies to Construct Domain-specific Low-resource Language Machine Translation Dataset (2025.coling-industry)
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| Challenge: | Existing models for low-resource languages struggle with domain-specific terms and lack of expert annotators for dataset creation. |
| Approach: | They propose a method for collecting low-resource language pairs from industrial domains using a large language model and neural machine translation framework. |
| Outcome: | The proposed model performs poorly on DaCoM-created datasets with up to 53.7 BLEURT points difference depending on domain inclusion. |
Post-hoc Utterance Refining Method by Entity Mining for Faithful Knowledge Grounded Conversations (2023.emnlp-main)
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Yoonna Jang, Suhyune Son, Jeongwoo Lee, Junyoung Son, Yuna Hur, Jungwoo Lim, Hyeonseok Moon, Kisu Yang, Heuiseok Lim
| Challenge: | Despite advances in language generation, models suffer from hallucinations that are either untrue or unfaithful to a given source. |
| Approach: | They propose a method to refine hallucinated utterances based on source knowledge . REM implicitly uses key entities in the knowledge to refine the utterant . |
| Outcome: | The proposed method reduces entity hallucination in the generated utterance and improves the quality of the model. |
From Ambiguity to Accuracy: The Transformative Effect of Coreference Resolution on Retrieval-Augmented Generation systems (2025.acl-srw)
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| Challenge: | Retrieval-augmented generation (RAG) is a key framework in natural language processing . however, the effectiveness of RAG is often hindered by coreferential complexity in retrieved documents . |
| Approach: | They investigate how entity coreference affects document retrieval and generative performance in RAG-based systems. |
| Outcome: | The proposed model improves QA performance and retrieval relevance and contextual understanding. |
Explore the Way: Exploring Reasoning Path by Bridging Entities for Effective Cross-Document Relation Extraction (2023.findings-emnlp)
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| Challenge: | Existing studies focus on capturing implicit relations between entities, but humans use explicit information chains to find the relations between two entities. |
| Approach: | They propose a cross-document relation extraction task that captures the relation between two entities mentioned in different documents. |
| Outcome: | The proposed model outperforms baseline models in the CodRED task. |