Papers by Seungjun Moon
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. |
Coffee-Gym: An Environment for Evaluating and Improving Natural Language Feedback on Erroneous Code (2024.emnlp-main)
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Hyungjoo Chae, Taeyoon Kwon, Seungjun Moon, Yongho Song, Dongjin Kang, Kai Ong, Beong-woo Kwak, Seonghyeon Bae, Seung-won Hwang, Jinyoung Yeo
| Challenge: | Large language models (LLMs) have made great progress in code generation, however, they still produce errors. |
| Approach: | They propose a RL environment that provides feedback on code editing by analyzing the performance of the revised code in unit tests. |
| Outcome: | The proposed model outperforms baselines in enhancing open-source code LLMs’ code editing, making them comparable with closed-source LLM. |
Translation of Multifaceted Data without Re-Training of Machine Translation Systems (2024.findings-emnlp)
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| Challenge: | a novel MT pipeline that considers the intra-data relation is proposed . previous MT systems have demonstrated relatively low performance, making them hardly utilized as another data source. |
| Approach: | They propose a new MT pipeline that considers the intra-data relation . they propose CS and IT to enhance the intra data relation based on a data point . |
| Outcome: | The proposed pipeline improves translation quality and training data compared with the existing approach . it yields better training data and better translation quality than previous approaches . |