Context-Aware Question-Answer for Interactive Media Experiences

Abstract

Media content has become a primary source of information, entertainment, and even education. The ability to provide video content querying as well as interactive experiences is a new challenge. To this end, question answering (QA) systems such as Alexa and Google Assistant have become quite established in consumer markets but are limited to general information and lack context awareness. In this paper, we propose Context-QA, a light-weight contextaware QA framework, to provide QA experiences on multimedia content. The context awareness is achieved through our innovative Staged QA Controller algorithm that keeps the search for answers in the context most relevant to the question. Our evaluation results show that Context-QA improves the quality of the answers by up to 49% and uses up to 56% less time compared to the conventional QA model. Subjective tests show Context-QA improved results over conventional QA models, with 90% reporting enjoying this new media form.

Deep Learning Based Question Answering System (Survey)

“…A "light-weight context-aware QA framework called Context-QA" is the method utilized in the study [17]. It features a ground-breaking algorithm called the Staged QA Controller that aims to confine the hunt for solutions within the parameters that are most closely related to the inquiry.…”