Automatic question generation is an important technique that can improve the
training of question answering, help chatbots to start or continue a
conversation with humans, and provide assessment materials for educational
purposes. Existing neural question generation models are not sufficient mainly
due to their inability to properly model the process of how each word in the
question is selected, i.e., whether repeating the given passage or being
generated from a vocabulary. In this paper, we propose our Clue Guided Copy
Network for Question Generation (CGC-QG), which is a sequence-to-sequence
generative model with copying mechanism, yet employing a variety of novel
components and techniques to boost the performance of question generation. In
CGC-QG, we design a multi-task labeling strategy to identify whether a question
word should be copied from the input passage or be generated instead, guiding
the model to learn the accurate boundaries between copying and generation.
Furthermore, our input passage encoder takes as input, among a diverse range of
other features, the prediction made by a clue word predictor, which helps
identify whether each word in the input passage is a potential clue to be
copied into the target question. The clue word predictor is designed based on a
novel application of Graph Convolutional Networks onto a syntactic dependency
tree representation of each passage, thus being able to predict clue words only
based on their context in the passage and their relative positions to the
answer in the tree. We jointly train the clue prediction as well as question
generation with multi-task learning and a number of practical strategies to
reduce the complexity. Extensive evaluations show that our model significantly
improves the performance of question generation and out-performs all previous
state-of-the-art neural question generation models by a substantial margin.

本研究提出了一种基于 Clue Guided Copy Network 的自动问答生成模型，通过多任务学习和复制机制的应用，显著提高了问题生成的性能并超越了所有现有的神经网络问题生成模型。