Answer-Aware Question Generation from Tabular and Textual Data using T5

Authors

  • Saichandra Pandraju QIS College of Engineering and Technology, Ongole
  • Sakthi Ganesh Mahalingam Vellore Institute of Technology, Vellore

DOI:

https://doi.org/10.3991/ijet.v16i18.25121

Keywords:

Question Generation, T5, Table-to-Text, Transfer Learning

Abstract


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.

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Published

2021-09-20

How to Cite

Pandraju, S., & Mahalingam, S. G. (2021). Answer-Aware Question Generation from Tabular and Textual Data using T5. International Journal of Emerging Technologies in Learning (iJET), 16(18), pp. 256–267. https://doi.org/10.3991/ijet.v16i18.25121

Issue

Vol. 16 No. 18 (2021)

Section

Papers

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