Papers by Linyi Ding
Topic-Oriented Open Relation Extraction with A Priori Seed Generation (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for open relation extraction give sub-optimal results on specific topics. |
| Approach: | They propose a method that leverages the built-in knowledge of large language models to maintain a dynamic seed relation dictionary for the topic. |
| Outcome: | The proposed approach empowers better topic-oriented control over the generated relations and improves ORE performance along the five dimensions, especially on specialized and narrow topics. |
A Rationale-centric Counterfactual Data Augmentation Method for Cross-Document Event Coreference Resolution (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing state-of-the-art event coreference resolution systems rely on spurious and spurious associations in the input mention pair text. |
| Approach: | They propose a rationale-centric counterfactual data augmentation method that leverages the debiasing capability of counterfact data haussed by LLM-in-the-loop to mitigate spurious association while emphasizing causation. |
| Outcome: | The proposed method achieves state-of-the-art on three popular cross-document benchmarks and demonstrates robustness in out-of domain scenarios. |