Few-Shot Fine-Grained Entity Typing with Automatic Label Interpretation and Instance Generation. (arXiv:2206.13746v1 [cs.CL])

We study the problem of few-shot Fine-grained Entity Typing (FET), where only
a few annotated entity mentions with contexts are given for each entity type.
Recently, prompt-based tuning has demonstrated superior performance to standard
fine-tuning in few-shot scenarios by formulating the entity type classification
task as a ''fill-in-the-blank'' problem. This allows effective utilization of
the strong language modeling capability of Pre-trained Language Models (PLMs).
Despite the success of current prompt-based tuning approaches, two major
challenges remain: (1) the verbalizer in …

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