KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering
This addresses the challenge of few-shot learning in machine reading comprehension for researchers and practitioners, though it is incremental as it builds on existing prompt-tuning and knowledge enhancement methods.
The paper tackles the problem of poor performance in few-shot extractive question answering by proposing KECP, a framework that transforms the task into a non-autoregressive MLM generation problem and uses external knowledge and contrastive learning, achieving state-of-the-art results on multiple benchmarks with significant margins.
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.