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Posts by Collection
portfolio
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publications
Recurrent Neural Network-Based Semantic Variational Autoencoder for Sequence to Sequence Learning
Myeongjun Jang, Seungwan Seo and Pilsung Kang.
Information Sciences, Volume 490. 59-73 (SCI), 2019
Unusual customer response identification and visualization based on text mining and anomaly detection
Seungwan Seo, Deokseong Seo, Myeongjun Jang, Jaeyun Jung and Pilsung Kang.
Experts Systems with Applications, Volume 144, 113111 (SCIE), 2020
Paraphrase Thought: Sentence Embedding Module Imitating Human Language Recognition
Myeongjun Jang and Pilsung Kang.
Information Sciences, Volumn 541, 123-145 (SCI), 2020
Intrusion Detection based on Sequential Information preserving Log Embedding Methods and Anomaly Detection Algorithms
Czangyeob Kim, Myeongjun Jang, Seungwan Seo, Kyeongchan Park and Pilsung Kang.
IEEE Access, Volume 9. 58088-58101, 2021
Learning-free Unsupervised Extractive Summarization Model
Myeongjun Jang and Pilsung Kang.
IEEE Access, Volume 9. 14358-14368, 2021
Sentence Transition Matrix: An efficient approach that preserves sentence semantics
Myeongjun Jang and Pilsung Kang.
Computer Speech & Language, Volume 71, 101266, 2022
Beyond Distributional Hypothesis: Let Language Models Learn Meaning-Text Correspondence
Myeongjun Jang, Frank Mutumbuka and Thomas Lukasiewicz.
In Findings of the Association for Computational Linguistics: NAACL 2022, Seattle, United States, pp. 2030-2042, Association for Computational Linguistics, 2022
NoiER: An Approach for Training more Reliable Fine-Tuned Downstream Task Models
Myeongjun Jang and Thomas Lukasiewicz.
IEEE/ACM Transactions on Audio,Speech, and Language Processing, Volume 30. 2514-2525, 2022
KoBEST: Korean Balanced Evaluation of Significant Tasks
Myeongjun Jang, Dohyung Kim, Deuk Sin Kwon and Eric Davis.
In Proceedings of the 29th International Conference on Computational Linguistics, Gyeongju, Republic of Korea, pp. 3697-3708, International Committee on Computational Linguistics, 2022
BECEL: Benchmark for Consistency Evaluation of Language Models
Myeongjun Jang, Deuk Sin Kwon and Thomas Lukasiewicz.
In Proceedings of the 29th International Conference on Computational Linguistics, Gyeongju, Republic of Korea, pp. 3680-3696, International Committee on Computational Linguistics, 2022
Know How to Make Up Your Mind! Adversarially Detecting and Alleviating Inconsistencies in Natural Language Explanations
Myeongjun Jang, Bodhisattwa Prasad Majumder, Julian McAuley, Thomas Lukasiewicz, and Oana-Maria Camburu.
In Proceedings of of the Association for Computational Linguistics: ACL 2023, pp.540-553, Associations for Computational Linguistics, 2023
Consistency Analysis of ChatGPT
Myeongjun Erik Jang and Thomas Lukasiewicz.
In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023), Singapore, 2023
Improving Language Models’ Meaning Understanding and Consistency by Learning Conceptual Roles from Dictionary
Myeongjun Erik Jang and Thomas Lukasiewicz.
In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023), Singapore, 2023
Pre-training and Diagnosing Knowledge Base Completion Models
Vid Kocijan, Myeongjun Jang and Thomas Lukasiewicz.
Artificial Intelligence, VOLUME 329, 104081, 2024
DriftWatch: A Tool that Automatically Detects Data Drift and Extracts Representative Drifted Examples
Myeongjun Erik Jang, Antonios Georgiadis, Yiyun Zhao, and Fran Silavong.
In Proceedings of The 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics: Industry Track (NAACL 2024), Mexico City, Mexico, Associations for Computational Linguistics, 2024
Leveraging Natural Language Processing and Large Language Models for Assisting Due Diligence in the Legal Domain
Myeongjun Erik Jang and Gábor Stikkel.
In Proceedings of The 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics: Industry Track (NAACL 2024), Mexico City, Mexico, Associations for Computational Linguistics, 2024
talks
Consistency Analysis of ChatGPT
Published:
teaching
Data Science for Engineers Course
- Worked as a T.A for the two-week technical training course.
- Held Q & A and practical sessions.
Workplace training course, LG Display, 2017
Machine Learning for Engineers Course
- Worked as a T.A for the 10-week technical traning course.
Workplace training course, SK Telecom, 2017
Text Mining for Data Analysts Course
- Worked as a T.A for the 5-week technical traning course.
- Held Q & A and practical sessions.
Workplace training course, Samsung Electronics Co., Ltd., 2018
Machine Learning Course
- Worked as a T.A for the Machine Learning class during the Michaelmas term.
Departmental class, Department of Computer Science, University of Oxford, 2022
Machine Learning Practical Session
- Worked as a practical demonstrator for the Machine Learning course during the Michaelmas term.
Practical session, Department of Computer Science, University of Oxford, 2022
Graph Representaion Learning Practical Session
- Worked as a practical demonstrator for the Graph Representaion Learning course during the Michaelmas term.
Practical session, Department of Computer Science, University of Oxford, 2022