Few-shot question answering (QA) aims at precisely discovering answers to a
set of questions from context passages while only a few training samples are
available. Although existing studies have made some progress and can usually
achieve proper results, they suffer from understanding deep semantics for
reasoning out the questions. In this paper, we develop Gotta, a Generative
prOmpT-based daTa Augmentation framework to mitigate the challenge above.
Inspired by the human reasoning process, we propose to integrate the cloze task
to enhance few-shot QA learning. Following the recent success of prompt-tuning,
we present the cloze task in the same format as the main QA task, allowing the
model to learn both tasks seamlessly together to fully take advantage of the
power of prompt-tuning. Extensive experiments on widely used benchmarks
demonstrate that Gotta consistently outperforms competitive baselines,
validating the effectiveness of our proposed prompt-tuning-based cloze task,
which not only fine-tunes language models but also learns to guide reasoning in
QA tasks. Further analysis shows that the prompt-based loss incorporates the
auxiliary task better than the multi-task loss, highlighting the strength of
prompt-tuning on the few-shot QA task.

本研究开发了 Gotta，一种基于生成式提示的的数据增强框架，旨在改善 few-shot question answering 学习中深层语义推理的挑战。研究表明，Gotta 始终优于竞争基线，验证了基于提示调整的背景填充任务的有效性，在 QA 任务中学习指导推理并提高语言模型扩展任务能力的优点。