Questionator-Automated Question Generation using Deep Learning
Animesh Srivastava, Shantanu Shinde, Naeem Patel, Siddhesh Despande, Anuj Dalvi, Shweta Tripathi · 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE) · 2020
Due to a boom in the amount of data generated every day, there is a need for automation in the education domain where it is humanly impossible for a single individual to make sense out of the data even for a simple task such as generating questions for a quiz or a test. Automatic question generation for textual inputs is valuable in academics where answering questions helps students to learn and improve their understanding of their field of study. Automatic question generation finds application in dialog systems or virtual assistants where asking questions is an important part of interactions between humans and machines. In this paper, we propose a state-of-the-art solution using a pipeline that utilizes natural language processing and image captioning techniques capable of generating questions not only for textual but also for visual inputs. Along with the question, distractors for the generated questions and their answers are also created.