Grant: Alliance Grants

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.