Publications

2025

1. 

   Call for Rigor in Reporting Quality of Instruction Tuning Data

   Hyeonseok Moon, Jaehyung Seo, and Heuiseok Lim

   In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics, 2025

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2. 

   Cross-Lingual Optimization for Language Transfer in Large Language Models

   Jungseob Lee, Seongtae Hong, Hyeonseok Moon, and 1 more author

   In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics, 2025

3. 

   Semantic Aware Linear Transfer by Recycling Pre-trained Language Models for Cross-lingual Transfer

   Seungyoon Lee, Seongtae Hong, Hyeonseok Moon, and 1 more author

   In Findings of the Association for Computational Linguistics: ACL 2025, 2025

4. 

   FLEX: A Benchmark for Evaluating Robustness of Fairness in Large Language Models

   Dahyun Jung, Seungyoon Lee, Hyeonseok Moon, and 2 more authors

   In Findings of the Association for Computational Linguistics: NAACL 2025, Apr 2025

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5. 

   MIRAGE: A Metric-Intensive Benchmark for Retrieval-Augmented Generation Evaluation

   Chanhee Park, Hyeonseok Moon, Chanjun Park, and 1 more author

   In Findings of the Association for Computational Linguistics: NAACL 2025, Apr 2025

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6. 

   Find the Intention of Instruction: Comprehensive Evaluation of Instruction Understanding for Large Language Models

   Hyeonseok Moon, Jaehyung Seo, Seungyoon Lee, and 2 more authors

   In Findings of the Association for Computational Linguistics: NAACL 2025, Apr 2025

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7. 

   MIGRATE: Cross-Lingual Adaptation of Domain-Specific LLMs through Code-Switching and Embedding Transfer

   Seongtae Hong, Seungyoon Lee, Hyeonseok Moon, and 1 more author

   In Proceedings of the 31st International Conference on Computational Linguistics, Jan 2025

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2024

1. 

   Leveraging Pre-existing Resources for Data-Efficient Counter-Narrative Generation in Korean

   Seungyoon Lee, Chanjun Park, DaHyun Jung, and 4 more authors

   In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), May 2024

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2. 

   Detecting Critical Errors Considering Cross-Cultural Factors in English-Korean Translation

   Sugyeong Eo, Jungwoo Lim, Chanjun Park, and 5 more authors

   In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), May 2024

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3. 

   Translation of Multifaceted Data without Re-Training of Machine Translation Systems

   Hyeonseok Moon, Seungyoon Lee, SeongTae Hong, and 3 more authors

   In Findings of the Association for Computational Linguistics: EMNLP 2024, Nov 2024

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4. 

   Length-aware Byte Pair Encoding for Mitigating Over-segmentation in Korean Machine Translation

   Jungseob Lee, Hyeonseok Moon, Seungjun Lee, and 6 more authors

   In Findings of the Association for Computational Linguistics: ACL 2024, Aug 2024

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5. 

   Generative Interpretation: Toward Human-Like Evaluation for Educational Question-Answer Pair Generation

   Hyeonseok Moon, Jaewook Lee, Sugyeong Eo, and 3 more authors

   In Findings of the Association for Computational Linguistics: EACL 2024, Mar 2024

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6. 

   Hyper-BTS Dataset: Scalability and Enhanced Analysis of Back TranScription (BTS) for ASR Post-Processing

   Chanjun Park, Jaehyung Seo, Seolhwa Lee, and 5 more authors

   In Findings of the Association for Computational Linguistics: EACL 2024, Mar 2024

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   The recent advancements in the realm of Automatic Speech Recognition (ASR) post-processing have been primarily driven by sequence-to-sequence paradigms. Despite their effectiveness, these methods often demand substantial amounts of data, necessitating the expensive recruitment of phonetic transcription experts to rectify the erroneous outputs of ASR systems, thereby creating the desired training data. Back TranScription (BTS) alleviates this issue by generating ASR inputs from clean text via a Text-to-Speech (TTS) system. While initial studies on BTS exhibited promise, they were constrained by a limited dataset of just 200,000 sentence pairs, leaving the scalability of this method in question. In this study, we delve into the potential scalability of BTS. We introduce the “Hyper-BTS” dataset, a corpus approximately five times larger than that utilized in prior research. Additionally, we present innovative criteria for categorizing error types within ASR post-processing. This not only facilitates a more comprehensive qualitative analysis, which was absent in preceding studies, but also enhances the understanding of ASR error patterns. Our empirical results, both quantitative and qualitative, suggest that the enlarged scale of the Hyper-BTS dataset sufficiently addresses a vast majority of the ASR error categories. We make the Hyper-BTS dataset publicly available.

7. 

   Exploiting hanja-based resources in processing korean historic documents written by common literati

   Hyeonseok Moon, Myunghoon Kang, Jaehyung Seo, and 4 more authors

   IEEE Access, Mar 2024

2023

1. 

   KEBAP: Korean Error Explainable Benchmark Dataset for ASR and Post-processing

   Seonmin Koo, Chanjun Park, Jinsung Kim, and 4 more authors

   In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Mar 2023

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2. 

   Post-hoc Utterance Refining Method by Entity Mining for Faithful Knowledge Grounded Conversations

   Yoonna Jang, Suhyune Son, Jeongwoo Lee, and 6 more authors

   In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Mar 2023

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3. 

   CHEF in the Language Kitchen: A Generative Data Augmentation Leveraging Korean Morpheme Ingredients

   Jaehyung Seo, Hyeonseok Moon, Jaewook Lee, and 3 more authors

   In The 2023 Conference on Empirical Methods in Natural Language Processing, Mar 2023

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4. 

   Informative Evidence-guided Prompt-based Fine-tuning for English-Korean Critical Error Detection

   Dahyun Jung, Sugyeong Eo, Chanjun Park, and 3 more authors

   In Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), Mar 2023

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5. 

   Improving formality-sensitive machine translation using data-centric approaches and prompt engineering

   Seungjun Lee, Hyeonseok Moon, Chanjun Park, and 1 more author

   In Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023), Mar 2023

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6. 

   PEEP-Talk: A Situational Dialogue-based Chatbot for English Education

   Seungjun Lee, Yoonna Jang, Chanjun Park, and 7 more authors

   In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), Mar 2023

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7. 

   Towards Diverse and Effective Question-Answer Pair Generation from Children Storybooks

   Sugyeong Eo, Hyeonseok Moon, Jinsung Kim, and 6 more authors

   In Findings of the Association for Computational Linguistics: ACL 2023, Jul 2023

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8. 

   Doubts on the reliability of parallel corpus filtering

   Hyeonseok Moon, Chanjun Park, Seonmin Koo, and 8 more authors

   Expert Systems with Applications, Jul 2023

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9. 

   Uncovering the Risks and Drawbacks Associated with the Use of Synthetic Data for Grammatical Error Correction

   Seonmin Koo, Chanjun Park, Seolhwa Lee, and 4 more authors

   IEEE Access, Jul 2023

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10. 

    A Survey on Evaluation Metrics for Machine Translation

    Seungjun Lee, Jungseob Lee, Hyeonseok Moon, and 5 more authors

    Mathematics, Jul 2023

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2022

1. 

   QUAK: A Synthetic Quality Estimation Dataset for Korean-English Neural Machine Translation

   Sugyeong Eo, Chanjun Park, Hyeonseok Moon, and 4 more authors

   In Proceedings of the 29th International Conference on Computational Linguistics, Jul 2022

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2. 

   A dog is passing over the jet? a text-generation dataset for korean commonsense reasoning and evaluation

   Jaehyung Seo, Seounghoon Lee, Chanjun Park, and 5 more authors

   In Findings of the Association for Computational Linguistics: NAACL 2022, Jul 2022

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3. 

   Empirical Analysis of Noising Scheme based Synthetic Data Generation for Automatic Post-editing

   Hyeonseok Moon, Chanjun Park, Seolhwa Lee, and 4 more authors

   In Proceedings of the Thirteenth Language Resources and Evaluation Conference, Jul 2022

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4. 

   Priming Ancient Korean Neural Machine Translation

   Chanjun Park, Seolhwa Lee, Jaehyung Seo, and 3 more authors

   In Proceedings of the Thirteenth Language Resources and Evaluation Conference, Jul 2022

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5. 

   PU-GEN: Enhancing generative commonsense reasoning for language models with human-centered knowledge

   Jaehyung Seo, Dongsuk Oh, Sugyeong Eo, and 5 more authors

   Knowledge-Based Systems, Jul 2022

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6. 

   K-nct: Korean neural grammatical error correction gold-standard test set using novel error type classification criteria

   Seonmin Koo, Chanjun Park, Jaehyung Seo, and 4 more authors

   IEEE Access, Jul 2022

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7. 

   Plain Template Insertion: Korean-Prompt-Based Engineering for Few-Shot Learners

   Jaehyung Seo, Hyeonseok Moon, Chanhee Lee, and 5 more authors

   IEEE Access, Jul 2022

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8. 

   BERTOEIC: Solving TOEIC Problems Using Simple and Efficient Data Augmentation Techniques with Pretrained Transformer Encoders

   Jeongwoo Lee, Hyeonseok Moon, Chanjun Park, and 3 more authors

   Applied Sciences, Jul 2022

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9. 

   Empirical Analysis of Parallel Corpora and In-Depth Analysis Using LIWC

   Chanjun Park, Midan Shim, Sugyeong Eo, and 4 more authors

   Applied Sciences, Jul 2022

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10. 

    AI for Patents: A Novel Yet Effective and Efficient Framework for Patent Analysis

    Junyoung Son, Hyeonseok Moon, Jeongwoo Lee, and 4 more authors

    IEEE Access, Jul 2022

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11. 

    Return on Advertising Spend Prediction with Task Decomposition-Based LSTM Model

    Hyeonseok Moon, Taemin Lee, Jaehyung Seo, and 7 more authors

    Mathematics, Jul 2022

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12. 

    Word-level quality estimation for korean-english neural machine translation

    Sugyeong Eo, Chanjun Park, Hyeonseok Moon, and 2 more authors

    IEEE Access, Jul 2022

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13. 

    Dense-to-question and sparse-to-answer: Hybrid retriever system for industrial frequently asked questions

    Jaehyung Seo, Taemin Lee, Hyeonseok Moon, and 7 more authors

    Mathematics, Jul 2022

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14. 

    Mimicking Infants’ Bilingual Language Acquisition for Domain Specialized Neural Machine Translation

    Chanjun Park, Woo-Young Go, Sugyeong Eo, and 3 more authors

    IEEE Access, Jul 2022

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15. 

    An automatic post editing with efficient and simple data generation method

    Hyeonseok Moon, Chanjun Park, Jaehyung Seo, and 2 more authors

    IEEE Access, Jul 2022

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2021

1. 

   Should we find another model?: Improving neural machine translation performance with ONE-piece tokenization method without model modification

   Chanjun Park, Sugyeong Eo, Hyeonseok Moon, and 1 more author

   In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers, Jul 2021

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2. 

   An empirical study on automatic post editing for neural machine translation

   Hyeonseok Moon, Chanjun Park, Sugyeong Eo, and 2 more authors

   IEEE Access, Jul 2021

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3. 

   Comparative analysis of current approaches to quality estimation for neural machine translation

   Sugyeong Eo, Chanjun Park, Hyeonseok Moon, and 2 more authors

   Applied Sciences, Jul 2021

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