Answer-Agnostic Question Generation in Privacy Policy Domain using Sequence-to-Sequence and Transformer Models

This paper presents a transformer and sequence-to-sequence mapping approach, augmented using relevant named entities, towards generating questions for text understanding in the domain of policies (such as privacy agreements). To date, most work in question generation has used general-purpose benchmarks such as the SQuAD corpus; however, the application of question generation to document understanding has largely omitted use cases in the privacy policy domain. A privacy policy is a legal document that clearly divulges the policies of an organization regarding the gathering and usage of customer data. Reading and understanding privacy policies to gain a measure of the rationality of terms of service before accepting them is of critical importance to users. However, users frequently ignore these policies or base their decisions on insufficient understanding of the “fine print” due to the complex language and excessive length of policies. This work focuses on building a question generation system to assist reading comprehension of privacy policies. This work can be used to improve the quality of question-answering systems in this domain, which consequently can help users understand the privacy policies before agreeing to them. This paper uses existing deep learning models like T5, which is a transformer model, and several sequence-to-sequence models to generate questions. Since, existing named entity recognition (NER) tools do not work in this domain, we also created our own named entity labels to add auxiliary information to our models. Adding the auxiliary information improves the results over the baseline models giving us a promising future direction to further add some constraints, as a means to add more background knowledge to our models to generate better quality questions.