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

, , , , , , and . IEEE Access, (2023)

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Priming Ancient Korean Neural Machine Translation., , , , , and . LREC, page 22-28. European Language Resources Association, (2022)Uncovering the Risks and Drawbacks Associated With the Use of Synthetic Data for Grammatical Error Correction., , , , , , and . IEEE Access, (2023)BTS: Back TranScription for Speech-to-Text Post-Processor using Text-to-Speech-to-Text., , , , , , and . WAT@ACL/IJCNLP, page 106-116. Association for Computational Linguistics, (2021)PU-GEN: Enhancing generative commonsense reasoning for language models with human-centered knowledge., , , , , , , and . Knowl. Based Syst., (2022)KEBAP: Korean Error Explainable Benchmark Dataset for ASR and Post-processing., , , , , , and . EMNLP, page 4798-4815. Association for Computational Linguistics, (2023)Length-aware Byte Pair Encoding for Mitigating Over-segmentation in Korean Machine Translation., , , , , , , , and . ACL (Findings), page 2287-2303. Association for Computational Linguistics, (2024)KU X Upstage's Submission for the WMT22 Quality Estimation: Critical Error Detection Shared Task., , , , and . WMT, page 606-614. Association for Computational Linguistics, (2022)Should we find another model?: Improving Neural Machine Translation Performance with ONE-Piece Tokenization Method without Model Modification., , , and . NAACL-HLT (Industry Papers), page 97-104. Association for Computational Linguistics, (2021)Generative Interpretation: Toward Human-Like Evaluation for Educational Question-Answer Pair Generation., , , , , and . EACL (Findings), page 2185-2196. Association for Computational Linguistics, (2024)Towards Diverse and Effective Question-Answer Pair Generation from Children Storybooks., , , , , , , , and . ACL (Findings), page 6100-6115. Association for Computational Linguistics, (2023)