Papers with few-shot relation classification models
Towards Realistic Few-Shot Relation Extraction (2021.emnlp-main)
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| Challenge: | Recent studies have shown that few-shot relation classification models can be used to extract any relation of interest from a collection of text with only a few example instances. |
| Approach: | They propose to modify the training routine to encourage models to better discriminate between relations involving similar entity types. |
| Outcome: | The proposed models outperform human models on relation extraction tasks while relying on entity type information. |
FewRel 2.0: Towards More Challenging Few-Shot Relation Classification (D19-1)
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| Challenge: | Few-shot domain adaptation and NOTA detection are two real-world challenges for few-shot relation classification models. |
| Approach: | They propose a task to investigate two aspects of few-shot relation classification models . they build upon the FewRel dataset by adding a new test set in a different domain . |
| Outcome: | The proposed task can evaluate few-shot domain adaptation and few- shot none-of-the-above detection on a new domain and NOTA relation choice. |