Version 1
: Received: 20 December 2023 / Approved: 22 December 2023 / Online: 22 December 2023 (11:48:43 CET)
How to cite:
Saeed, N.; ashraf, H.; Jhanjhi, N. DEEP LEARNING BASED QUESTION ANSWERING SYSTEM (SURVEY). Preprints2023, 2023121739. https://doi.org/10.20944/preprints202312.1739.v1
Saeed, N.; ashraf, H.; Jhanjhi, N. DEEP LEARNING BASED QUESTION ANSWERING SYSTEM (SURVEY). Preprints 2023, 2023121739. https://doi.org/10.20944/preprints202312.1739.v1
Saeed, N.; ashraf, H.; Jhanjhi, N. DEEP LEARNING BASED QUESTION ANSWERING SYSTEM (SURVEY). Preprints2023, 2023121739. https://doi.org/10.20944/preprints202312.1739.v1
APA Style
Saeed, N., ashraf, H., & Jhanjhi, N. (2023). DEEP LEARNING BASED QUESTION ANSWERING SYSTEM (SURVEY). Preprints. https://doi.org/10.20944/preprints202312.1739.v1
Chicago/Turabian Style
Saeed, N., humaira ashraf and NZ Jhanjhi. 2023 "DEEP LEARNING BASED QUESTION ANSWERING SYSTEM (SURVEY)" Preprints. https://doi.org/10.20944/preprints202312.1739.v1
Abstract
Deep learning-based question answering systems have transformed the discipline of natural language processing (NLP) by automating the extraction of answers from textual data. This survey paper provides a captivating overview of these systems, exploring methodologies, techniques, and architectures such as recurrent neural networks (RNNs), BERT model, and transformer models. Extractive and generative approaches are examined, alongside the challenges of handling complex questions, managing noisy input, and addressing rare or unseen words. This survey serves as a stimulating reference, offering valuable insights to researchers and practitioners, fueling innovation and advancement in question answering systems within NLP.
Keywords
BERT, RNN, Depp Learning
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
