Question and Answer Generation from Text Using Transformers

Abstract

Natural language generation is one of the major tasks that comes under Natural Language Processing and, Question Generation (QG) and Question Answering (QA) are two NLP-based tasks that have found widespread application. The QA and QG tasks on a text paragraph aim to extract answers and generate questions, respectively. The automatic answering systems, chatbots, and trivia applications make use of these tasks and their use cases are growing. We have used a text-to-text transformer, T5 with a transfer-learning-based approach to achieve these tasks by training a single model in a multi-task fashion. The study is based on fine-tuning a T5 transformer in a multi-task way to produce questions from the text and extract answers upon providing the questions. The fine-tuning step is used to train the pre-trained transformer on data-science-related text paragraphs along with a large question answering dataset. This work aims to implement simplified data processing and training for fine-tuning the transformer model to improve the performance of the pre-trained model on technical data for QA and QG tasks.