QuAVA: A privacy-aware architecture for conversational desktop Content Retrieval systems

Question Answering (QA) and Content Retrieval (CR) systems have experienced a boost in performance in recent years leveraging state-of-the-art Transformer models to process user expressions and retrieve and extract information requested. Despite the constant language understanding improvements, very little effort has been put into the design of such systems for personal desktop use, where data are kept locally and are not sent to cloud services and decisions and outputs are transparent and explainable to the user. To that end, we present QuAVA, a conversational desktop content retrieval assistant, designed on four pillars: privacy and security, explainability, low-resource requirements, and multi-source data fusion. QuAVA is a data and privacy-preserving assistant that enables users to access their private data such as files, emails, and message exchanges, conversationally and transparently. The proposed architecture automatically extracts and preprocesses content from various sources and organizes it in a 3-layered hierarchical structure, namely a topic, a subtopic, and a content layer by employing ML algorithms for clustering and labeling. This way, users can navigate and access information via a set of conversation rules embedded in the assistant. We conduct a qualitative comparison analysis of the QuAVA architecture with other well-established QA and CR architectures against the four pillars defined, as well as privacy tests, and conclude that QuAVA is the only -to our knowledge- virtual assistant that successfully satisfies them. • Design and development of a desktop Content Retrieval assistant. • Private and secure Content Retrieval system. • Personal desktop assistant for Content Retrieval for everyday use. • Hierarchical structure for explainable and transparent Virtual Assistants.