Papers by Minji Jung
Debiasing Event Understanding for Visual Commonsense Tasks (2022.findings-acl)
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| Challenge: | a recent study shows that object-based event understanding is purely likelihood-based, leading to incorrect event prediction. |
| Approach: | They propose to mitigate object-based event understanding by optimizing aggregation with association-based prediction. |
| Outcome: | The proposed approach improves visual commonsense reasoning tasks by combining do-calculus with association-based prediction. |
Enhancing Complex Reasoning in Knowledge Graph Question Answering through Query Graph Approximation (2025.findings-acl)
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| Challenge: | Existing knowledge-grounded question answering frameworks lack essential triplets related to the questions . Existing approaches to knowledge-based QA are incomplete in the context of KGs . |
| Approach: | They propose a framework to provide answers to structured queries by leveraging Knowledge Graphs. |
| Outcome: | The proposed framework outperforms existing methods on QA tasks where KGs are incomplete . the framework is based on a set of data from a dataset of QA questions . |
Retrieval-augmented Video Encoding for Instructional Captioning (2023.findings-acl)
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| Challenge: | Instructional videos provide a detailed multimodal context of each procedure in instruction. key-object degeneracy is a problem for machine systems, causing incorrect captions. |
| Approach: | They propose a retrieval-based framework to augment the model representations in the presence of key-object degeneracy. |
| Outcome: | The proposed framework can be extended over baselines using modalities with key-object degeneracy. |
Is Prompt Transfer Always Effective? An Empirical Study of Prompt Transfer for Question Answering (2024.naacl-short)
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| Challenge: | Prompt tuning is an efficient method for initializing pre-trained models . but initialization of prompts is sensitive when the model size is small . |
| Approach: | They propose a method to measure catastrophic forgetting by analyzing prompts for the first time . they characterize a question answering task based on answer format and prompt initialization . |
| Outcome: | The proposed approach can help deepen understanding of prompt tuning. |