Papers by Jungyeul Park
An Untold Story of Preprocessing Task Evaluation: An Alignment-based Joint Evaluation Approach (2024.lrec-main)
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Eunkyul Leah Jo, Angela Yoonseo Park, Grace Tianjiao Zhang, Izia Xiaoxiao Wang, Junrui Wang, MingJia Mao, Jungyeul Park
| Challenge: | a preprocessing task such as tokenization and sentence boundary detection (SBD) has been considered as a solution to many NLP challenges . however, the low error rates of current methods are mainly specific to certain tasks and rule-based tokenization can be difficult to use across different systems. |
| Approach: | They propose an evaluation algorithm that combines both tokenization and SBD results to improve evaluation reliability. |
| Outcome: | The proposed evaluation algorithm improves the reliability of evaluations by reevaluating the counts of true positive cases for F1 measures in both preprocessing tasks jointly. |
K-UniMorph: Korean Universal Morphology and its Feature Schema (2023.findings-acl)
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| Challenge: | Previously, the Korean language has been underrepresented in the field of morphological paradigms amongst hundreds of diverse world languages. |
| Approach: | They propose a new Universal Morphology dataset for Korean that preserves its distinct characteristics. |
| Outcome: | The proposed dataset extracts inflected Korean verb forms from the largest annotated corpus for Korean. |
TReX: Tokenizer Regression for Optimal Data Mixture (2026.eacl-long)
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| Challenge: | Existing approaches to train and inference tokenizers rely on heuristics or large-scale searches to determine optimal data mixtures. |
| Approach: | They propose a regression-based framework that efficiently predicts the optimal data mixture for tokenizer training. |
| Outcome: | The proposed model outperforms mixtures based on LLaMA3 and uniform distributions by up to 12% in both in- and out-of-distribution compression efficiency. |
Improving Automatic Grammatical Error Annotation for Chinese Through Linguistically-Informed Error Typology (2025.coling-main)
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| Challenge: | In educational settings, GEC systems provide immediate and consistent feedback to both native (L1) and non-native (L2) language learners. |
| Approach: | They propose a framework that provides detailed feedback on 12-16% of all errors by identifying them under a new error typology, specific enough to uncover subtle differences in error patterns between L1 and L2 writings. |
| Outcome: | The proposed framework can provide detailed feedback on 12-16% of all errors, revealing subtle differences in error patterns between L1 and L2 writings. |
A Linguistically-Informed Annotation Strategy for Korean Semantic Role Labeling (2024.lrec-main)
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| Challenge: | Semantic role labeling is an essential component of semantic and syntactic processing of natural languages. |
| Approach: | They propose an annotation strategy for Korean semantic role labeling that is in line with the previously proposed linguistic theories as well as the distinct properties of the Korean language. |
| Outcome: | The proposed annotation strategy is consistent with the proposed linguistic theories and the distinct properties of the Korean language. |
Refined Evaluation for End-to-End Grammatical Error Correction Using an Alignment-Based Approach (2025.coling-main)
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| Challenge: | errant is a new evaluation tool that can be used to evaluate end-to-end grammatical error correction systems. |
| Approach: | They propose a method to assess end-to-end grammatical error correction systems using alignment-based alignment methods that reproduce and improve results from existing evaluation tools. |
| Outcome: | The proposed method reproduces and improves results from existing evaluation tools, such as errant, even when applied to raw text input. |
Yet Another Format of Universal Dependencies for Korean (2022.coling-1)
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Yige Chen, Eunkyul Leah Jo, Yundong Yao, KyungTae Lim, Miikka Silfverberg, Francis M. Tyers, Jungyeul Park
| Challenge: | Existing dependency parsers for Korean do not perform as well as their English counterparts due to the complexity of Korean's linguistic features. |
| Approach: | They propose a morpheme-based Korean dependency parsing format and propose to adopt it to Universal Dependencies. |
| Outcome: | The proposed format outperforms parsing results for Korean UD treebanks and detailed error analysis. |
TELLME: Test-Enhanced Learning for Language Model Enrichment (2026.findings-eacl)
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Minjun Kim, Inho Won, HyeonSeok Lim, MinKyu Kim, Junghun Yuk, Wooyoung Go, Jongyoul Park, Jungyeul Park, KyungTae Lim
| Challenge: | Continual pre-training (CPT) has been widely adopted as a method for domain expansion in large language models, but has faced challenges such as acquiring large-scale domain-specific datasets and high computational costs. |
| Approach: | They propose a method that integrates the Test-Enhanced Learning principle with CPT to promote efficient domain-specific knowledge acquisition and long-term memory retention. |
| Outcome: | The proposed method outperforms existing methods by 23.6% in the financial domain and achieves 9.8% improvement in long-term memory retention. |
jp-evalb: Robust Alignment-based PARSEVAL Measures (2024.naacl-demo)
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| Challenge: | evalb is used for constituency parsing evaluation, but imposes constraints and requires consistent tokenization and sentence boundary outcomes. |
| Approach: | They propose an evaluation system designed to compute PARSEVAL measures, offering a viable alternative to evalb commonly used for constituency parsing evaluation. |
| Outcome: | The proposed evaluation system is based on an alignment method that aligns sentences and words when discrepancies arise. |
Evaluating Prompting Strategies for Grammatical Error Correction Based on Language Proficiency (2024.lrec-main)
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| Challenge: | generative LLMs have been known for overcorrection where results obtain higher recall measures than precision measures. |
| Approach: | They propose to use generative LLMs to prompt grammatical error correction using a model based on language proficiency to examine the interaction between LLM's performance and L2 language proficiency. |
| Outcome: | The proposed model improves on zero-shot and few-shot prompting and fine-tuning models for grammatical error correction for learners of English as a foreign language based on the different proficiency levels. |
Towards Standardized Annotation and Parsing for Korean FrameNet (2024.lrec-main)
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| Challenge: | Existing studies on Korean FrameNet have focused on English, but annotations are not optimally designed for Korean. |
| Approach: | They propose a morphologically enhanced annotation strategy for Korean FrameNet datasets and parsing by leveraging the CoNLL-U format. |
| Outcome: | The proposed method improves the annotation accuracy of Korean FrameNet datasets and their parsers. |
When the Misidentified Adverbial Phrase Functions as a Complement (2024.findings-emnlp)
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| Challenge: | Existing studies on the predicate-argument structure of natural languages have been limited . despite the importance of distinguishing mandatory arguments and optional modifiers, there is no dataset with token-level annotations for Korean . |
| Approach: | They propose a dataset which labels mandatory and optional elements as complements and adjuncts in Korean . they reclassify Korean phrases as complement and develop an automatic labeling technique for complements . |
| Outcome: | The proposed dataset is trainable and reliable, with satisfying results . Korean is an adverbial language, and its predicate-argument structure is well understood . |
Unified Automated Essay Scoring and Grammatical Error Correction (2025.findings-naacl)
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| Challenge: | a new study explores the integration of automated writing evaluation and grammatical error correction through multitask learning. |
| Approach: | They propose a system that integrates automated writing evaluation and grammatical error correction through multitask learning by leveraging a shared learning framework. |
| Outcome: | The proposed system outperforms models trained on AWE and GEC, the authors show . their study demonstrates that the proposed system improves writing assessment accuracy and accuracy . |
Data Anonymization for Requirements Quality Analysis: a Reproducible Automatic Error Detection Task (L18-1)
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| Challenge: | a recent study focuses on identifying potential problems of ambiguity, completeness, conformity, singularity and readability in requirements specifications. |
| Approach: | They propose to identify potential problems of ambiguity, completeness, conformity, singularity and readability in system and software requirements specifications. |
| Outcome: | The proposed system achieves 79.47% for the F1 score on proposed evaluation data. |