Web: http://arxiv.org/abs/2205.03071

May 9, 2022, 1:10 a.m. | Jianing Wang, Chengyu Wang, Minghui Qiu, Qiuhui Shi, Hongbin Wang, Jun Huang, Ming Gao

cs.CL updates on arXiv.org arxiv.org

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 …

arxiv knowledge question answering

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