Hyeonseok Moon

Orcid: 0000-0002-0841-4262

According to our database1, Hyeonseok Moon authored at least 40 papers between 2021 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Toward Practical Automatic Speech Recognition and Post-Processing: a Call for Explainable Error Benchmark Guideline.
CoRR, 2024

Exploiting Hanja-Based Resources in Processing Korean Historic Documents Written by Common Literati.
IEEE Access, 2024

Translation of Multifaceted Data without Re-Training of Machine Translation Systems.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2024, 2024

Hyper-BTS Dataset: Scalability and Enhanced Analysis of Back TranScription (BTS) for ASR Post-Processing.
Proceedings of the Findings of the Association for Computational Linguistics: EACL 2024, 2024

Generative Interpretation: Toward Human-Like Evaluation for Educational Question-Answer Pair Generation.
Proceedings of the Findings of the Association for Computational Linguistics: EACL 2024, 2024

Leveraging Pre-existing Resources for Data-Efficient Counter-Narrative Generation in Korean.
Proceedings of the 2024 Joint International Conference on Computational Linguistics, 2024

Detecting Critical Errors Considering Cross-Cultural Factors in English-Korean Translation.
Proceedings of the 2024 Joint International Conference on Computational Linguistics, 2024

Length-aware Byte Pair Encoding for Mitigating Over-segmentation in Korean Machine Translation.
Proceedings of the Findings of the Association for Computational Linguistics, 2024

2023
Doubts on the reliability of parallel corpus filtering.
Expert Syst. Appl., December, 2023

Data-Driven Approach for Formality-Sensitive Machine Translation: Language-Specific Handling and Synthetic Data Generation.
CoRR, 2023

Synthetic Alone: Exploring the Dark Side of Synthetic Data for Grammatical Error Correction.
CoRR, 2023

Self-Improving-Leaderboard(SIL): A Call for Real-World Centric Natural Language Processing Leaderboards.
CoRR, 2023

Uncovering the Risks and Drawbacks Associated With the Use of Synthetic Data for Grammatical Error Correction.
IEEE Access, 2023

Improving Formality-Sensitive Machine Translation Using Data-Centric Approaches and Prompt Engineering.
Proceedings of the 20th International Conference on Spoken Language Translation, 2023

Informative Evidence-guided Prompt-based Fine-tuning for English-Korean Critical Error Detection.
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, 2023

CHEF in the Language Kitchen: A Generative Data Augmentation Leveraging Korean Morpheme Ingredients.
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

KEBAP: Korean Error Explainable Benchmark Dataset for ASR and Post-processing.
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

Post-hoc Utterance Refining Method by Entity Mining for Faithful Knowledge Grounded Conversations.
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

PEEP-Talk: A Situational Dialogue-based Chatbot for English Education.
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 2023

Towards Diverse and Effective Question-Answer Pair Generation from Children Storybooks.
Proceedings of the Findings of the Association for Computational Linguistics: ACL 2023, 2023

2022
PU-GEN: Enhancing generative commonsense reasoning for language models with human-centered knowledge.
Knowl. Based Syst., 2022

AI for Patents: A Novel Yet Effective and Efficient Framework for Patent Analysis.
IEEE Access, 2022

Plain Template Insertion: Korean-Prompt-Based Engineering for Few-Shot Learners.
IEEE Access, 2022

Mimicking Infants' Bilingual Language Acquisition for Domain Specialized Neural Machine Translation.
IEEE Access, 2022

An Automatic Post Editing With Efficient and Simple Data Generation Method.
IEEE Access, 2022

K-NCT: Korean Neural Grammatical Error Correction Gold-Standard Test Set Using Novel Error Type Classification Criteria.
IEEE Access, 2022

Word-Level Quality Estimation for Korean-English Neural Machine Translation.
IEEE Access, 2022

KU X Upstage's Submission for the WMT22 Quality Estimation: Critical Error Detection Shared Task.
Proceedings of the Seventh Conference on Machine Translation, 2022

A Dog Is Passing Over The Jet? A Text-Generation Dataset for Korean Commonsense Reasoning and Evaluation.
Proceedings of the Findings of the Association for Computational Linguistics: NAACL 2022, 2022

Priming Ancient Korean Neural Machine Translation.
Proceedings of the Thirteenth Language Resources and Evaluation Conference, 2022

Empirical Analysis of Noising Scheme based Synthetic Data Generation for Automatic Post-editing.
Proceedings of the Thirteenth Language Resources and Evaluation Conference, 2022

QUAK: A Synthetic Quality Estimation Dataset for Korean-English Neural Machine Translation.
Proceedings of the 29th International Conference on Computational Linguistics, 2022

2021
A Self-Supervised Automatic Post-Editing Data Generation Tool.
CoRR, 2021

A New Tool for Efficiently Generating Quality Estimation Datasets.
CoRR, 2021

Automatic Knowledge Augmentation for Generative Commonsense Reasoning.
CoRR, 2021

How should human translation coexist with NMT? Efficient tool for building high quality parallel corpus.
CoRR, 2021

Empirical Analysis of Korean Public AI Hub Parallel Corpora and in-depth Analysis using LIWC.
CoRR, 2021

An Empirical Study on Automatic Post Editing for Neural Machine Translation.
IEEE Access, 2021

Should we find another model?: Improving Neural Machine Translation Performance with ONE-Piece Tokenization Method without Model Modification.
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers, 2021

BTS: Back TranScription for Speech-to-Text Post-Processor using Text-to-Speech-to-Text.
Proceedings of the 8th Workshop on Asian Translation, 2021


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