Papers by Yunah Jang
MP2D: An Automated Topic Shift Dialogue Generation Framework Leveraging Knowledge Graphs (2024.emnlp-main)
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| Challenge: | Existing methods to manage topic shifts within on-topic dialogues are limited in their ability to generate training datasets. |
| Approach: | They propose a data generation framework that automatically generates conversational question-answering datasets with natural topic transitions by leveraging relationships between entities in a knowledge graph. |
| Outcome: | The proposed framework generates conversational question-answering datasets with natural topic transitions and proves its effectiveness in generating dialogues with topic shifts. |
Task-specific Compression for Multi-task Language Models using Attribution-based Pruning (2023.findings-eacl)
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| Challenge: | Existing compression methods for multi-task language models use large number of parameter parameters even when performing only a specific task. |
| Approach: | They propose a training-free compression method for multi-task language models using pruning method . they use an attribution method to determine which neurons are essential for performing a specific task . |
| Outcome: | The proposed method outperforms baseline pruning methods on six widely-used datasets. |
IterCQR: Iterative Conversational Query Reformulation with Retrieval Guidance (2024.naacl-long)
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| Challenge: | Existing methods for conversational query reformulation depend on human annotations. |
| Approach: | They propose a method that reformulates context-dependent conversational queries without relying on human rewrites. |
| Outcome: | The proposed method shows state-of-the-art performance on two widely-used datasets. |