Choudhury, Najeefa NikhatSayma, SadikaTonima, Fariha HasanBiswas, SouravFerdos, JannatulHaque, Tasnuva2024-09-082024-09-08©20242024-06ID 20101131ID 23341078ID 20101324ID 23341067ID 24141267http://hdl.handle.net/10361/24012Cataloged from PDF version of thesis.Includes bibliographical references (pages 32-34).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.Machine Reading Comprehension (MRC) is an artificial intelligence task that ex amines a given passage or text and answers queries regarding it. The objective is to make an intelligent support system that has the ability to understand the contex tual information of the passage and give correct answers for multi-reasoning ques tions, commonsense based questions and multiple-choice questions, etc. One of the main challenges faced by MRC models in commonsense based and multi-reasoning questions is the need for understanding and reasoning beyond explicit textual infor mation. To enhance the capabilities of MRC systems in these areas, the research focuses on the comparative analysis of state-of-the-art transformer-based models in cluding BERT, ALBERT, RoBERTa, DistilBERT, MobileBERT, and ELECTRA. Our investigation specifically targets the enhancement of commonsense reasoning within MRC frameworks. In regards to this, we have used a binary decision mak ing approach in our algorithm, in order to achieve a better outcome from these transformer-based models. To evaluate the performance, the experiments were con ducted using CosmosQA dataset, which consists of narrative-driven questions that necessitate commonsense understanding to resolve.34 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Machine reading comprehensionArtificial intelligenceTransformer-based modelsArtificial intelligence.Machine sewing--Data processing.Thesis