Question Generation (QG) is a fundamental NLP task for many downstream
applications. Recent studies on open-book QG, where supportive answer-context
pairs are provided to models, have achieved promising progress. However,
generating natural questions under a more practical closed-book setting that
lacks these supporting documents still remains a challenge. In this work, we
propose a new QG model for this closed-book setting that is designed to better
understand the semantics of long-form abstractive answers and store more
information in its parameters through contrastive learning and an answer
reconstruction module. Through experiments, we validate the proposed QG model
on both public datasets and a new WikiCQA dataset. Empirical results show that
the proposed QG model outperforms baselines in both automatic evaluation and
human evaluation. In addition, we show how to leverage the proposed model to
improve existing question-answering systems. These results further indicate the
effectiveness of our QG model for enhancing closed-book question-answering
tasks.

本文提出了一种新的封闭式问答模型，旨在通过对比学习和答案重构模块更好地理解长形式摘要回答的语义并在其参数中存储更多信息，通过实验验证了该模型在公共数据集和新的 WikiCQA 数据集上的有效性，并展示了如何利用该模型改进现有的问答系统。

对比学习实现的闭卷问题生成

Closed-book Question Generation via Contrastive Learning

Recent work has suggested that language models (LMs) store both common-sense
and factual knowledge learned from pre-training data. In this paper, we
leverage this implicit knowledge to create an effective end-to-end fact checker
using a solely a language model, without any external knowledge or explicit
retrieval components. While previous work on extracting knowledge from LMs have
focused on the task of open-domain question answering, to the best of our
knowledge, this is the first work to examine the use of language models as fact
checkers. In a closed-book setting, we show that our zero-shot LM approach
outperforms a random baseline on the standard FEVER task, and that our
fine-tuned LM compares favorably with standard baselines. Though we do not
ultimately outperform methods which use explicit knowledge bases, we believe
our exploration shows that this method is viable and has much room for
exploration.

本文探索了利用语言模型自带的知识来创建基于 LM 的事实检查器的方法，并在闭卷条件下展示了我们的零 - shot LM 方法在标准 FEVER 任务上优于随机基准线，而我们的微调 LM 与标准基准线相比表现出色。虽然我们最终没有超越使用显式知识库的方法，但我们相信我们的探索显示出这种方法是可行的，并且有很大的发展空间。