Cheonbok Park

Orcid: 0000-0001-7264-628X

According to our database1, Cheonbok Park authored at least 26 papers between 2019 and 2025.

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Bibliography

2025
Enhancing Hallucination Detection via Future Context.
CoRR, July, 2025

Cross-lingual Collapse: How Language-Centric Foundation Models Shape Reasoning in Large Language Models.
CoRR, June, 2025

ReGUIDE: Data Efficient GUI Grounding via Spatial Reasoning and Search.
CoRR, May, 2025

Peri-LN: Revisiting Layer Normalization in the Transformer Architecture.
CoRR, February, 2025

KMMLU: Measuring Massive Multitask Language Understanding in Korean.
Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies, 2025

Code-Switching Curriculum Learning for Multilingual Transfer in LLMs.
Proceedings of the Findings of the Association for Computational Linguistics, 2025

2024
Mitigating Semantic Leakage in Cross-lingual Embeddings via Orthogonality Constraint.
CoRR, 2024

Adaptive Contrastive Decoding in Retrieval-Augmented Generation for Handling Noisy Contexts.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2024, 2024

Aligning Language Models to Explicitly Handle Ambiguity.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

2023
A Visual Analytics System for Improving Attention-based Traffic Forecasting Models.
IEEE Trans. Vis. Comput. Graph., 2023

PePe: Personalized Post-editing Model utilizing User-generated Post-edits.
Proceedings of the Findings of the Association for Computational Linguistics: EACL 2023, 2023

Towards Accurate Translation via Semantically Appropriate Application of Lexical Constraints.
Proceedings of the Findings of the Association for Computational Linguistics: ACL 2023, 2023

2022
PASTA: PArallel Spatio-Temporal Attention with Spatial Auto-Correlation Gating for Fine-Grained Crowd Flow Prediction.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2022

Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Specializing Multi-domain NMT via Penalizing Low Mutual Information.
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

Residual Correction in Real-Time Traffic Forecasting.
Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

DaLC: Domain Adaptation Learning Curve Prediction for Neural Machine Translation.
Proceedings of the Findings of the Association for Computational Linguistics: ACL 2022, 2022

2021
VATUN: Visual Analytics for Testing and Understanding Convolutional Neural Networks.
Proceedings of the 21st Eurographics Conference on Visualization, 2021

An Empirical Experiment on Deep Learning Models for Predicting Traffic Data.
Proceedings of the 37th IEEE International Conference on Data Engineering, 2021

Unsupervised Neural Machine Translation for Low-Resource Domains via Meta-Learning.
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021

2020
Meta-Learning for Low-Resource Unsupervised Neural MachineTranslation.
CoRR, 2020

ST-GRAT: A Novel Spatio-temporal Graph Attention Networks for Accurately Forecasting Dynamically Changing Road Speed.
Proceedings of the CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, 2020

2019
STGRAT: A Spatio-Temporal Graph Attention Network for Traffic Forecasting.
CoRR, 2019

SANVis: Visual Analytics for Understanding Self-Attention Networks.
Proceedings of the 30th IEEE Visualization Conference, 2019

A Comparison of the Effects of Data Imputation Methods on Model Performance.
Proceedings of the 21st International Conference on Advanced Communication Technology, 2019

AILA: Attentive Interactive Labeling Assistant for Document Classification through Attention-Based Deep Neural Networks.
Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 2019


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