Generating questions along with associated answers from a text has
applications in several domains, such as creating reading comprehension tests
for students, or improving document search by providing auxiliary questions and
answers based on the query. Training models for question and answer generation
(QAG) is not straightforward due to the expected structured output (i.e. a list
of question and answer pairs), as it requires more than generating a single
sentence. This results in a small number of publicly accessible QAG models. In
this paper, we introduce AutoQG, an online service for multilingual QAG, along
with lmqg, an all-in-one Python package for model fine-tuning, generation, and
evaluation. We also release QAG models in eight languages fine-tuned on a few
variants of pre-trained encoder-decoder language models, which can be used
online via AutoQG or locally via lmqg. With these resources, practitioners of
any level can benefit from a toolkit that includes a web interface for end
users, and easy-to-use code for developers who require custom models or
fine-grained controls for generation.

本文介绍了一种多语言的问答生成模型 AutoQG 及其 Python 包 lmqg，模型基于预训练的 encoder-decoder 语言模型，在 8 种语言上 fine-tune，并提供了一个 web 界面和易于使用的代码，方便从业者使用和定制。

多语言问答生成的实用工具包

A Practical Toolkit for Multilingual Question and Answer Generation

Question and answer generation is a data augmentation method that aims to
improve question answering (QA) models given the limited amount of human
labeled data. However, a considerable gap remains between synthetic and
human-generated question-answer pairs. This work aims to narrow this gap by
taking advantage of large language models and explores several factors such as
model size, quality of pretrained models, scale of data synthesized, and
algorithmic choices. On the SQuAD1.1 question answering task, we achieve higher
accuracy using solely synthetic questions and answers than when using the
SQuAD1.1 training set questions alone. Removing access to real Wikipedia data,
we synthesize questions and answers from a synthetic corpus generated by an 8.3
billion parameter GPT-2 model. With no access to human supervision and only
access to other models, we are able to train state of the art question
answering networks on entirely model-generated data that achieve 88.4 Exact
Match (EM) and 93.9 F1 score on the SQuAD1.1 dev set. We further apply our
methodology to SQuAD2.0 and show a 2.8 absolute gain on EM score compared to
prior work using synthetic data.

通过利用大型语言模型，研究了模型大小、预训练模型的质量、合成数据的规模和算法选择等多个因素，缩小了人工和合成问题 - 答案对之间的差距。该研究通过模型生成的数据，在 SQuAD 数据集上实现了良好的准确率，并在 SQuAD2.0 上显示出优于以前的工作的结果。