Papers by Zhi-Xiu Ye
Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification (P19-1)
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| Challenge: | Existing methods for few-shot relation classification use supervised training, but lack of large-scale manually labeled data. |
| Approach: | They propose a multi-level matching and aggregation network (MLMAN) for few-shot relation classification. |
| Outcome: | The proposed model achieves state-of-the-art performance on the FewRel dataset. |
Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions (N19-1)
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| Challenge: | Existing methods to extract relational data generated by distant supervision generate noisy training data. |
| Approach: | They propose a neural relation extraction method to deal with noisy training data generated by distant supervision. |
| Outcome: | Experimental results show that the proposed method is more accurate than state-of-the-art methods on the New York Times dataset. |