Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A Novel Approach for Machine Reading Comprehension using BERT-based Large Language Models Subject The topic of the resource ALBERT; DistilBERT; Longformer; Machine Reading Comprehension; Span Extraction; Transformers Description An account of the resource Teaching machines to learn the information from the natural language documents remains an arduous task because it involves natural language understanding of contexts, excerpting the meaningful insights, and deliver the answer to the questions. Machine Reading Comprehension (MRC) tasks can identify and understand the content from the natural language documents by asking the model to answer questions. The model can extract the answer from the given context or other external repositories. This article proposes a novel Context Minimization method for MRC Span Extraction tasks to improve the accuracy of the models. The Context Minimization method constitutes two subprocedures, Context Reduction and Sentence Aggregation. The proposed model reduced the context with the most relevant sequences for answering by estimating the sentence embedding between the question and the sequences involved in the context. The Context Reduction facilitates the model to retrieve the answer efficiently from the minimal context. The Sentence Aggregation improves the quality of answers by aggregating the most relevant shreds of evidence from the context. Both methods have been developed from the two notable observations from the empirical analysis of existing models. First, the models with minimal context with efficient masking can improve the accuracy and the second is the issue with the scatted sequences on the context that may lead to partial or incomplete answering. The Context Minimization method with Fine-Tuned BERT model compared with the ALBERT, DistilBERT, and Longformer models and the experiments with these models have shown significant improvement in results. 2024 IEEE. Creator An entity primarily responsible for making the resource Varghese N.; Shereef S.; Joy H.K.; Ramasamy G.; Sridevi R.; Cynthia T.; Rajeshkanna R. Source A related resource from which the described resource is derived Proceedings of CONECCT 2024 - 10th IEEE International Conference on Electronics, Computing and Communication Technologies Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/CONECCT62155.2024.10677199" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/CONECCT62155.2024.10677199</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205827823&amp;doi=10.1109%2FCONECCT62155.2024.10677199&amp;partnerID=40&amp;md5=dd2387aa8f89cb758dc936c1f94ddd68" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205827823&amp;doi=10.1109%2fCONECCT62155.2024.10677199&amp;partnerID=40&amp;md5=dd2387aa8f89cb758dc936c1f94ddd68</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835038592-2 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Varghese N., Christ University, Department of Computer Science, Karnataka, Bangalore, 560029, India; Shereef S., Jain (Deemed to Be University), Department of Cs and It, Karnataka, Bangalore, 560059, India; Joy H.K., Christ University, Department of Computer Science, Karnataka, Bangalore, 560029, India; Ramasamy G., Christ University, Department of Computer Science, Karnataka, Bangalore, 560029, India; Sridevi R., Christ University, Department of Computer Science, Karnataka, Bangalore, 560029, India; Cynthia T., Christ University, Department of Computer Science, Karnataka, Bangalore, 560029, India; Rajeshkanna R., Christ University, Department of Computer Science, Karnataka, Bangalore, 560029, India