Papers by Jungyun Seo
Multi-Task Learning for Knowledge Graph Completion with Pre-trained Language Models (2020.coling-main)
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| Challenge: | Existing knowledge graph completion methods are lacking in ranking metrics such as Hits@k . despite the high performance, the proposed method is still behind state-of-the-art models. |
| Approach: | They propose a multi-task learning method that integrates relational and relevance ranking tasks with target link prediction to improve ranking performance. |
| Outcome: | The proposed method improves ranking performance but still behind state-of-the-art models in Hits@k and Mean Rank metrics. |
Fine-grained Post-training for Improving Retrieval-based Dialogue Systems (2021.naacl-main)
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| Challenge: | Existing methods to select the correct response for a dialogue system are generation-based and retrieval-based. |
| Approach: | They propose a fine-grained post-training method that reflects the characteristics of the multi-turn dialogue. |
| Outcome: | The proposed model achieves state-of-the-art with significant margins on three benchmark datasets. |