TY - JOUR AU - Pandraju, Saichandra AU - Mahalingam, Sakthi Ganesh PY - 2021/09/20 Y2 - 2024/03/29 TI - Answer-Aware Question Generation from Tabular and Textual Data using T5 JF - International Journal of Emerging Technologies in Learning (iJET) JA - Int. J. Emerg. Technol. Learn. VL - 16 IS - 18 SE - Papers DO - 10.3991/ijet.v16i18.25121 UR - https://online-journals.org/index.php/i-jet/article/view/25121 SP - pp. 256-267 AB - Automatic Question Generation (AQG) systems are applied in a myriad of domains to generate questions from sources such as documents, images, knowledge graphs to name a few. With the rising interest in such AQG systems, it is equally important to recognize structured data like tables while generating questions from documents. In this paper, we propose a single model architecture for question generation from tables along with text using “Text-to-Text Transfer Transformer” (T5) - a fully end-to-end model which does not rely on any intermediate planning steps, delexicalization, or copy mechanisms. We also present our systematic approach in modifying the ToTTo dataset, release the augmented dataset as TabQGen along with the scores achieved using T5 as a baseline to aid further research. ER -