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MICK: A Meta-Learning Framework for Few-shot Relation Classification with Little Training Data
[article]
2020
arXiv
pre-print
Few-shot relation classification describes a circumstance where a model is required to classify new-coming query instances after meeting only few support instances during testing. In this paper, we place a challenging restriction to conventional few-shot relation classification by additionally limiting the amount of training data. We also propose a few-shot learning framework for relation classification, which is particularly powerful when the training data is very small. In our framework,
arXiv:2004.14164v1
fatcat:u65ygi6ahjekrbdx35pomhtqqe