Knowledge based neural question generation from text

从文本中自动生成问题由于其在诸如会话系统、智能辅导系统和阅读理解评估等各种应用中的有用性而受到越来越多的关注。已经开发了两种不同的策略来从文本中生成问题：基于规则的方法和神经序列到序列（seq-to-seq）模型。基于规则的方法利用从文本中提取的语言特征，通过人类专家设计的模板规则将句子转换为问题。或者，seq-to-seq模型，其将输入序列（例如，答案）转换成问题，由于它们能够从数据中学习复杂的映射函数，最近在问题生成中表现出良好的性能。然而，生成高质量的问题仍然是一个开放的挑战，因为目前的方法生成的问题往往是模糊的，毫无意义的，缺乏多样性，因此需要进行重大改进，以便将其用于工业产品。与行业合作伙伴iNAGO Inc.我们将开发新的技术，通过将领域内和领域外的知识结合到seq-to-seq模型中，从文本数据中生成高质量的问题，来改进问题生成。我们将研究语言资源的使用，结合应用领域中为问题生成而设计的规则，以指导seq-to-seq方法。 我们还将研究域适应的问题，在一个领域中可用的规则和/或训练样本集在另一个领域中使用，以解决神经网络方法的训练数据有限的问题。该项目将通过使其能够自动生成知识库来增强iNAGO的Netpeople会话助理平台产品（由问题和答案组成）用于在诸如联网汽车及其相关服务的领域中构建会话辅助系统。

Automatic generation of questions from text has gained increasing attention due to its usefulness in various applications, such as conversational systems, intelligent tutoring systems, and reading comprehension assessment. Two different strategies have been developed to generate questions from text: rule-based methods and neural sequence-to-sequence (seq-to-seq) models. Rule-based methods employ linguistic features extracted from text to transform sentences into questions through template rules designed by human experts. Alternatively, seq-to-seq models, which convert an input sequence (e.g., an answer) into a question using artificial neural networks, have recently shown good performance in question generation due to their ability to learn complex mapping functions from data. However, generating high-quality questions is still an open challenge since the questions generated by current approaches are often vague, meaningless and lack of diversity, and thus significant improvements are needed in order for them to be used in industrial products.Working with industry partner iNAGO Inc., we will develop novel techniques to improve question generation by incorporating in-domain and out-domain knowledge into seq-to-seq models to produce high-quality questions from text data. We will investigate the use of linguistic resources, in combination with the rules designed for question generation in the application domain, to guide seq-to-seq approaches. We will also investigate the problem of domain adaptation, where the sets of rules and/or training samples are available in one domain are used in another domain to solve the limited training data problem of neural network methods.This project will enhance iNAGO's Netpeople conversational assistant platform product by enabling it to automatically generating the knowledge base (consisting of questions and answers) for building conversational assistant systems in domains such as connected automobiles and their related services.