Papers by Young-Jun Lee
Enhancing Arguments Recognition for Financial Mathematical Reasoning over Hybrid Data (2024.findings-emnlp)
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| Challenge: | Existing methods for question answering on textual data are difficult to train and pose a misrecognition problem. |
| Approach: | They propose an approach to train a reasoning program generator to improve argument recognition by aggregating arguments and loss argument set. |
| Outcome: | The proposed method improves the probabilities of proper arguments in a reasoning program generation so that arguments comprising the ground truth have higher weights. |
DialogCC: An Automated Pipeline for Creating High-Quality Multi-Modal Dialogue Dataset (2024.naacl-long)
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| Challenge: | Existing multi-modal dialogue datasets that focus on image-based dialogues have low quality and limited diversity of images per dialogue. |
| Approach: | They propose to construct a multi-modal dialogue dataset that guarantees both dialogue quality and image diversity without requiring minimum human effort. |
| Outcome: | The proposed dataset outperforms existing datasets in terms of quality and diversity in human evaluation. |
Does GPT-3 Generate Empathetic Dialogues? A Novel In-Context Example Selection Method and Automatic Evaluation Metric for Empathetic Dialogue Generation (2022.coling-1)
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| Challenge: | Empathy is a multi-dimensional concept consisting of cognitive and affective aspects. |
| Approach: | They propose two new in-context example selection methods that utilize emotion and situational information. |
| Outcome: | The proposed method is effective in measuring the degree of human empathy. |
Stark: Social Long-Term Multi-Modal Conversation with Persona Commonsense Knowledge (2024.findings-emnlp)
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| Challenge: | Existing studies focus on image-sharing behavior in singular sessions, leading to limited long-term social interaction. |
| Approach: | They propose a large-scale long-term multi-modal dialogue dataset that generates long-time multi-modity dialogue distilled from ChatGPT and proposed image aligner. |
| Outcome: | The proposed framework generates long-term multi-modal dialogue from ChatGPT and image aligner. |
Large Language Models can Share Images, Too! (2024.findings-acl)
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| Challenge: | Using a zero-shot prompting, large language models can be used to share images in a multi-tasking environment. |
| Approach: | They introduce a dataset that includes enriched annotations and a framework to evaluate LLMs. |
| Outcome: | The proposed framework unlocks image-sharing capability of LLMs in zero-shot prompting, with ChatGPT achieving the best performance. |
KNU-HYUNDAI’s NMT system for Scientific Paper and Patent Tasks onWAT 2019 (D19-52)
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Cheoneum Park, Young-Jun Jung, Kihoon Kim, Geonyeong Kim, Jae-Won Jeon, Seongmin Lee, Junseok Kim, Changki Lee
| Challenge: | We submitted our transformer-based neural machine translation system to the translation tasks of the 6th workshop on Asian Translation (WAT 2019). |
| Approach: | They propose a transformer-based neural machine translation system for Chinese-Japanese, English-Japanese, and Korean->Japanoise translation tasks. |
| Outcome: | The proposed system performed well on the two translation tasks and was ranked first in terms of the BLEU scores in all the JPC2 subtasks. |
Korean-Specific Emotion Annotation Procedure Using N-Gram-Based Distant Supervision and Korean-Specific-Feature-Based Distant Supervision (2020.lrec-1)
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| Challenge: | Existing methods to annotate unlabeled data with emotions are expensive and time-consuming. |
| Approach: | They propose an annotation procedure that leverages Korean emotion lexicons and Korean-specific emotion features to annotate unlabeled data. |
| Outcome: | The proposed procedure compares with the KTEA dataset and a large-scale emotion-labeled dataset. |