Performance comparison of transformer-based models for multi-reasoning in machine reading comprehension
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.
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Brac University
2024
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| Länkar: | http://hdl.handle.net/10361/24012 |
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10361-240122024-09-10T06:36:06Z Performance comparison of transformer-based models for multi-reasoning in machine reading comprehension Sayma, Sadika Tonima, Fariha Hasan Biswas, Sourav Ferdos, Jannatul Haque, Tasnuva Choudhury, Najeefa Nikhat Department of Computer Science and Engineering, Brac University Machine reading comprehension Artificial intelligence Transformer-based models Artificial intelligence. Machine sewing--Data processing. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024. Cataloged from PDF version of thesis. Includes bibliographical references (pages 32-34). 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. Sadika Sayma Fariha Hasan Tonima Sourav Biswas Jannatul Ferdos Tasnuva Haque B.Sc in Computer Science 2024-09-08T09:18:03Z 2024-09-08T09:18:03Z ©2024 2024-06 Thesis ID 20101131 ID 23341078 ID 20101324 ID 23341067 ID 24141267 http://hdl.handle.net/10361/24012 en Brac 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. 34 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Machine reading comprehension Artificial intelligence Transformer-based models Artificial intelligence. Machine sewing--Data processing. |
| spellingShingle |
Machine reading comprehension Artificial intelligence Transformer-based models Artificial intelligence. Machine sewing--Data processing. Sayma, Sadika Tonima, Fariha Hasan Biswas, Sourav Ferdos, Jannatul Haque, Tasnuva Performance comparison of transformer-based models for multi-reasoning in machine reading comprehension |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024. |
| author2 |
Choudhury, Najeefa Nikhat |
| author_facet |
Choudhury, Najeefa Nikhat Sayma, Sadika Tonima, Fariha Hasan Biswas, Sourav Ferdos, Jannatul Haque, Tasnuva |
| format |
Thesis |
| author |
Sayma, Sadika Tonima, Fariha Hasan Biswas, Sourav Ferdos, Jannatul Haque, Tasnuva |
| author_sort |
Sayma, Sadika |
| title |
Performance comparison of transformer-based models for multi-reasoning in machine reading comprehension |
| title_short |
Performance comparison of transformer-based models for multi-reasoning in machine reading comprehension |
| title_full |
Performance comparison of transformer-based models for multi-reasoning in machine reading comprehension |
| title_fullStr |
Performance comparison of transformer-based models for multi-reasoning in machine reading comprehension |
| title_full_unstemmed |
Performance comparison of transformer-based models for multi-reasoning in machine reading comprehension |
| title_sort |
performance comparison of transformer-based models for multi-reasoning in machine reading comprehension |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/24012 |
| work_keys_str_mv |
AT saymasadika performancecomparisonoftransformerbasedmodelsformultireasoninginmachinereadingcomprehension AT tonimafarihahasan performancecomparisonoftransformerbasedmodelsformultireasoninginmachinereadingcomprehension AT biswassourav performancecomparisonoftransformerbasedmodelsformultireasoninginmachinereadingcomprehension AT ferdosjannatul performancecomparisonoftransformerbasedmodelsformultireasoninginmachinereadingcomprehension AT haquetasnuva performancecomparisonoftransformerbasedmodelsformultireasoninginmachinereadingcomprehension |
| _version_ |
1814307894634479616 |