  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 that transform the task into a
non-autoregressive Masked Language Modeling (MLM) generation problem.
Simultaneously, rich semantics from the external knowledge base (KB) and the
passage context are support for enhancing the representations of the query. In
addition, to boost the performance of PLMs, we jointly train the model by the
MLM and contrastive learning objectives. Experiments on multiple benchmarks
demonstrate that our method consistently outperforms state-of-the-art
approaches in few-shot settings by a large margin.
