We present KGConv, a large, conversational corpus of 71k conversations where
each question-answer pair is grounded in a Wikidata fact. Conversations contain
on average 8.6 questions and for each Wikidata fact, we provide multiple
variants (12 on average) of the corresponding question using templates, human
annotations, hand-crafted rules and a question rewriting neural model. We
provide baselines for the task of Knowledge-Based, Conversational Question
Generation. KGConv can further be used for other generation and analysis tasks
such as single-turn question generation from Wikidata triples, question
rewriting, question answering from conversation or from knowledge graphs and
quiz generation.

KGConv 是一个大型的对话数据集，包含 71,000 个基于 Wikidata 事实的对话，平均每个对话包含 8.6 个问题和对应问题的多个变体。该数据集可用于知识驱动对话问题生成以及其他生成和分析任务。

KGConv, 一个基于 Wikidata 的对话语料库

KGConv, a Conversational Corpus grounded in Wikidata

The ability of generative language models (GLMs) to generate text has
improved considerably in the last few years, enabling their use for generative
data augmentation. In this work, we propose CONDA, an approach to further
improve GLMs' ability to generate synthetic data by reformulating data
generation as context generation for a given question-answer (QA) pair and
leveraging QA datasets for training context generators. Then, we cast
downstream tasks into the same question answering format and adapt the
fine-tuned context generators to the target task domain. Finally, we use the
fine-tuned GLM to generate relevant contexts, which are in turn used as
synthetic training data for their corresponding tasks. We perform extensive
experiments on multiple classification datasets and demonstrate substantial
improvements in performance for both few- and zero-shot settings. Our analysis
reveals that QA datasets that require high-level reasoning abilities (e.g.,
abstractive and common-sense QA datasets) tend to give the best boost in
performance in both few-shot and zero-shot settings.

本论文提出 CONDA 方法以生成虚拟数据，并通过将结构放在 QA 对上并使用 QA 数据集来训练上下文生成器来进一步提高生成语言模型的能力，通过将下游任务转化为相同的问答格式并调整上下文生成器适应目标任务域，使用精调的生成语言模型生成相关上下文，这些上下文作为相应任务的虚拟训练数据，在多个分类数据集上进行实验，显示出在几乎零样本和少样本设置中的性能显着改进，分析表明要求高级推理能力的 QA 数据集（例如，抽象和常识 QA 数据集）在几乎零样本和少样本设置中都能显着改进性能。

利用问答数据集来改进生成型数据增强

Leveraging QA Datasets to Improve Generative Data Augmentation

Multi-choice reading comprehension is a challenging task, which involves the
matching between a passage and a question-answer pair. This paper proposes a
new co-matching approach to this problem, which jointly models whether a
passage can match both a question and a candidate answer. Experimental results
on the RACE dataset demonstrate that our approach achieves state-of-the-art
performance.

本文提出一种新的共同匹配方法来解决多选阅读理解问题，该方法同时建模一个段落是否匹配一个问题和一个候选答案，在 RACE 数据集上的实验结果表明我们的方法达到了最先进的性能。