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KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering
[article]
2022
arXiv
pre-print
Extractive Question Answering (EQA) is one of the most important tasks in Machine Reading Comprehension (MRC), which can be solved by fine-tuning the span selecting heads of Pre-trained Language Models (PLMs). However, most existing approaches for MRC may perform poorly in the few-shot learning scenario. To solve this issue, we propose a novel framework named Knowledge Enhanced Contrastive Prompt-tuning (KECP). Instead of adding pointer heads to PLMs, we introduce a seminal paradigm for EQA
arXiv:2205.03071v1
fatcat:sxa4ufwaarf3bnkyvmkop6u7xa