Papers by Junyoung Lim
Hyper-BTS Dataset: Scalability and Enhanced Analysis of Back TranScription (BTS) for ASR Post-Processing (2024.findings-eacl)
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Chanjun Park, Jaehyung Seo, Seolhwa Lee, Junyoung Son, Hyeonseok Moon, Sugyeong Eo, Chanhee Lee, Heuiseok Lim
| Challenge: | Automatic Speech Recognition (ASR) post-processing requires substantial amounts of data, requiring expensive phonetic transcription experts. |
| Approach: | They propose a "Hyper-BTS" dataset that is five times larger than prior studies . they propose criteria for categorizing error types within ASR post-processing . |
| Outcome: | The proposed method can generate ASR inputs from clean text using a text-to-speech system. |
TimeChara: Evaluating Point-in-Time Character Hallucination of Role-Playing Large Language Models (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) can be used to simulate human behaviors, but point-in-time role-playing is a key component of fandom role-players. |
| Approach: | They propose a benchmark to evaluate point-in-time character hallucination in role-playing LLMs. |
| Outcome: | The proposed method reduces point-in-time character hallucinations effectively by decomposing reasoning steps and using narrative experts. |
KoCHET: A Korean Cultural Heritage Corpus for Entity-related Tasks (2022.coling-1)
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| Challenge: | Existing corpus for entity-related tasks is limited in terms of application and cannot be used for entity recognition. |
| Approach: | They propose to use a Korean cultural heritage corpus for the typical entity-related tasks named entity recognition (NER), relation extraction (RE) and entity typing (ET) . |
| Outcome: | The proposed corpus makes it more useful in terms of cultural heritage and provides practical insights in terms linguistic analysis. |
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. |
GRASP: Guiding Model with RelAtional Semantics Using Prompt for Dialogue Relation Extraction (2022.coling-1)
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| Challenge: | Existing studies utilize pre-trained language models with extensive features to supplement the low information density of the dialogue by multiple speakers. |
| Approach: | They propose a dialogue-based relation extraction task that leverages pre-trained language models to capture relational semantic clues of a given dialogue using an argument-aware prompt marker strategy and a relational clue detection task. |
| Outcome: | The proposed model achieves state-of-the-art on a DialogRE dataset even though it only leverages pre-trained language models without adding any extra layers. |
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. |