Natural Language Generation (NLG) has improved exponentially in recent years thanks to
the development of sequence-to-sequence deep learning technologies such as Transformer …

Question generation is an important yet challenging problem in Artificial Intelligence (AI),
which aims to generate natural and relevant questions from various input formats, eg …

Event extraction (EE) is a crucial information extraction task that aims to extract event
information in texts. Previous methods for EE typically model it as a classification task, which …

Pre-training and fine-tuning, eg, BERT, have achieved great success in language
understanding by transferring knowledge from rich-resource pre-training task to the low/zero …

Question generation, the task of automatically creating questions that can be answered by a
certain span of text within a given passage, is important for question-answering and …

In recent times, sequence-to-sequence (seq2seq) models have gained a lot of popularity
and provide state-of-the-art performance in a wide variety of tasks, such as machine …

Natural question generation (QG) aims to generate questions from a passage and an
answer. Previous works on QG either (i) ignore the rich structure information hidden in …

In this study, we investigate the employment of the pre-trained BERT language model to
tackle question generation tasks. We introduce three neural architectures built on top of …

Obtaining training data for Question Answering (QA) is time-consuming and resource-
intensive, and existing QA datasets are only available for limited domains and languages. In …

Text-based Question Generation (QG) aims at generating natural and relevant questions
that can be answered by a given answer in some context. Existing QG models suffer from a" …