Question generation (QG) from a given context can enhance comprehension,
engagement, assessment, and overall efficacy in learning or conversational
environments. Despite recent advancements in QG, the challenge of enhancing or
measuring the diversity of generated questions often remains unaddressed. In
this paper, we introduce a multi-question generation model (mQG), which is
capable of generating multiple, diverse, and answerable questions by focusing
on context and questions. To validate the answerability of the generated
questions, we employ a SQuAD2.0 fine-tuned question answering model,
classifying the questions as answerable or not. We train and evaluate mQG on
the FairytaleQA dataset, a well-structured QA dataset based on storybooks, with
narrative questions. We further apply a zero-shot adaptation on the TellMeWhy
and SQuAD1.1 datasets. mQG shows promising results across various evaluation
metrics, among strong baselines.

该研究使用多问题生成模型 (mQG) 从给定的上下文生成多样且可回答的问题，并通过 SQuAD2.0 fine-tuned 模型验证问题的可回答性，该模型在 FairytaleQA 数据集上训练和评估，并在 TellMeWhy 和 SQuAD1.1 数据集上进行了零 - shot 适应，显示出与强基线方法相比有希望的结果。

故事书的多样化强化叙事问题生成

Diversity Enhanced Narrative Question Generation for Storybooks

We propose a deep-learning system -- for the SQuAD2.0 task -- that finds, or
indicates the lack of, a correct answer to a question in a context paragraph.
Our goal is to learn an ensemble of heterogeneous SQuAD2.0 models that, when
blended properly, outperforms the best model in the ensemble per se. We created
a stacking ensemble that combines top-N predictions from two models, based on
ALBERT and RoBERTa, into a multiclass classification task to pick the best
answer out of their predictions. We explored various ensemble configurations,
input representations, and model architectures. For evaluation, we examined
test-set EM and F1 scores; our best-performing ensemble incorporated a
CNN-based meta-model and scored 87.117 and 90.306, respectively -- a relative
improvement of 0.55% for EM and 0.61% for F1 scores, compared to the baseline
performance of the best model in the ensemble, an ALBERT-based model, at 86.644
for EM and 89.760 for F1.

本文提出了一种基于深度学习的系统，在 SQuAD2.0 任务中，通过对话段落来寻找或指示问题的正确答案，它可以学习一个异构 SQuAD2.0 模型的集合，在进行合适的融合后，能够胜过单个最佳模型。我们创造了一种堆叠集合，将两个基于 ALBERT 和 RoBERTa 的模型的前 N 个预测组合成一个多类分类任务，以从它们的预测中选择最佳答案。我们研究了各种集合配置、输入表示和模型架构。在评估中，我们检查了测试集 EM 和 F1 得分；我们最好的表现集合结合了基于 CNN 的元模型，在 EM 和 F1 得分方面分别得分为 87.117 和 90.306，相对于基线表现最佳的模型（一个基于 ALBERT 的模型）的 86.644 和 89.760，分别提高了 0.55% 和 0.61%。