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。