Kyomin Jung

Kyomin Jung


Research Focus & Metrics

Academic Career Length

15Years (2012-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 January 2023
Verified
Conference Paper
Open access
PDF file
DLDongryeol Lee
Dongryeol Lee
SKSegwang Kim
Segwang Kim
MLMinwoo Lee
Minwoo Lee
HLHwanhee Lee
Hwanhee Lee
JPJoonsuk Park
Joonsuk Park
KJKyomin Jung
Kyomin Jung
Dongryeol Lee, Segwang Kim, Minwoo Lee, Hwanhee Lee, Joonsuk Park, Sang-Woo Lee, Kyomin Jung. Findings of the Association for Computational Linguistics: EMNLP 2023. 2023.

1 January 2024
Verified
Conference Paper
Open access
PDF file
MKMinbeom Kim
Minbeom Kim
JKJahyun Koo
Jahyun Koo
HLHwanhee Lee
Hwanhee Lee
JPJoonsuk Park
Joonsuk Park
HLHwaran Lee
Hwaran Lee
KJKyomin Jung
Kyomin Jung
Minbeom Kim, Jahyun Koo, Hwanhee Lee, Joonsuk Park, Hwaran Lee, Kyomin Jung. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers). 2024.

26 October 2021
Verified
Conference Paper
Open access
MLMinwoo Lee
Minwoo Lee
SWSeungpil Won
Seungpil Won
JKJuae Kim
Juae Kim
HLHwanhee Lee
Hwanhee Lee
CPCheoneum Park
Cheoneum Park
KJKyomin Jung
Kyomin Jung
Fact verification datasets are typically constructed using crowdsourcing techniques due to the lack of text sources with veracity labels. However, the crowdsourcing process often produces undesired biases in data that cause models to learn spurious patterns. In this paper, we propose CrossAug, a contrastive data augmentation method for debiasing fact verification models. Specifically, we employ a two-stage augmentation pipeline to generate new claims and evidences from existing samples. The generated samples are then paired cross-wise with the original pair, forming contrastive samples that facilitate the model to rely less on spurious patterns and learn more robust representations. Experimental results show that our method outperforms the previous state-of-the-art debiasing technique by 3.6% on the debiased extension of the FEVER dataset, with a total performance boost of 10.13% from the baseline. Furthermore, we evaluate our approach in data-scarce settings, where models can be more susceptible to biases due to the lack of training data. Experimental results demonstrate that our approach is also effective at debiasing in these low-resource conditions, exceeding the baseline performance on the Symmetric dataset with just 1% of the original data.

1 January 2024
Verified
Conference Paper
Open access
PDF file
HKHyukhun Koh
Hyukhun Koh
DKDohyung Kim
Dohyung Kim
MLMinwoo Lee
Minwoo Lee
KJKyomin Jung
Kyomin Jung
Hyukhun Koh, Dohyung Kim, Minwoo Lee, Kyomin Jung. Findings of the Association for Computational Linguistics: EMNLP 2024. 2024.

1 January 2024
Verified
Conference Paper
Open access
PDF file
YJYunah Jang
Yunah Jang
KLKang-Il Lee
Kang-Il Lee
HBHyunkyung Bae
Hyunkyung Bae
HLHwanhee Lee
Hwanhee Lee
KJKyomin Jung
Kyomin Jung
Yunah Jang, Kang-il Lee, Hyunkyung Bae, Hwanhee Lee, Kyomin Jung. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.