Gotta: Generative Few-shot Question Answering by Prompt-based Cloze Data
  Augmentation
Xiusi Chen Yu Zhang Jinliang Deng Jyun-Yu Jiang Wei Wang
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.