Papers by Daoyuan Chen
Cooperative Denoising for Distantly Supervised Relation Extraction (C18-1)
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| Challenge: | Existing methods for distantly supervised relation extraction suffer from noisy labeling problem, which can severely degrade its performance. |
| Approach: | They propose a framework for distantly supervised relation extraction that leverages text corpus and knowledge graph and a cooperative module involving their mutual learning. |
| Outcome: | The proposed method reduces the noisy labels and achieves substantial improvement over the state-of-the-art methods. |
Relabel the Noise: Joint Extraction of Entities and Relations via Cooperative Multiagents (2020.acl-main)
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| Challenge: | Existing methods for entity and relation extraction require light human annotation efforts. |
| Approach: | They propose a method to re-label noisy instances with a cooperative group . they use a confidence consensus module to gather the wisdom of all agents . |
| Outcome: | The proposed model outperforms state-of-the-art methods on two real-world datasets. |
ChartThinker: A Contextual Chain-of-Thought Approach to Optimized Chart Summarization (2024.lrec-main)
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| Challenge: | Existing methods for chart summarization lack visual-language matching and reasoning ability. |
| Approach: | They propose a method which synthesizes deep analysis based on chains of thought and strategies of context retrieval to improve the logical coherence and accuracy of the generated summaries. |
| Outcome: | The proposed method outperforms 8 state-of-the-art models over 7 evaluation metrics and can significantly reduce time and cognitive resources required. |