Papers by Jungseob Lee
Length-aware Byte Pair Encoding for Mitigating Over-segmentation in Korean Machine Translation (2024.findings-acl)
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Jungseob Lee, Hyeonseok Moon, Seungjun Lee, Chanjun Park, Sugyeong Eo, Hyunwoong Ko, Jaehyung Seo, Seungyoon Lee, Heuiseok Lim
| Challenge: | Byte Pair Encoding (BPE) is an effective approach in machine translation across several languages, but it is prone to over-segmentation in Korean, an agglutinative and morphologically rich language. |
| Approach: | They propose a new method that incorporates long words into the Korean vocabulary by strategically preserving morphological information and reducing semantic confusion. |
| Outcome: | The proposed method outperforms BPE and surpasses state-of-the-art morpheme-aware tokenization methods. |
PEEP-Talk: A Situational Dialogue-based Chatbot for English Education (2023.acl-demo)
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Seungjun Lee, Yoonna Jang, Chanjun Park, Jungseob Lee, Jaehyung Seo, Hyeonseok Moon, Sugyeong Eo, Seounghoon Lee, Bernardo Yahya, Heuiseok Lim
| Challenge: | Existing chatbots lack realistic practice scenarios for English learners . existing platforms employ hand-crafted and patternmatching rules, limiting communication ability and responding appropriately to out-of-situation utterances. |
| Approach: | They propose a real-world situational dialogue-based chatbot for English education . it generates appropriate responses in various real-life situations while providing accurate feedback . |
| Outcome: | The proposed chatbot generates appropriate responses in various real-life situations while providing accurate feedback to learners. |
Cross-Lingual Optimization for Language Transfer in Large Language Models (2025.acl-long)
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| Challenge: | Adapting large language models to other languages often suffers from an overemphasis on English performance. |
| Approach: | They propose a cross-lingual optimization technique that efficiently transfers an English-centric LLM to a target language while preserving its English capabilities. |
| Outcome: | The proposed model outperforms SFT in acquiring target language proficiency and maintaining English performance in low-resource languages. |
Empirical Analysis of Noising Scheme based Synthetic Data Generation for Automatic Post-editing (2022.lrec-1)
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| Challenge: | Automatic post-editing (APE) is a research field that aims to correct errors in translated sentences regardless of the utilized machine translation system. |
| Approach: | They propose a method for automatically generating APE data based on a noising scheme from a parallel corpus. |
| Outcome: | The proposed method shows that depending on the type of noise, the noising scheme-based APE data generation may lead to inferior performance. |
QUAK: A Synthetic Quality Estimation Dataset for Korean-English Neural Machine Translation (2022.coling-1)
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| Challenge: | despite its high utility, there are limitations concerning manual QE data creation. |
| Approach: | They propose to generate a Korean-English QE dataset that is fully automatic . they find that the algorithm is more accurate and faster than manual QE . |
| Outcome: | The proposed datasets show that they scale up to 1.58M and 6.58M, respectively, and show that the results are significantly better when compared to the previous datasets. |