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CompoundCue Textual Question Answering Application For Compound Sentences

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dc.contributor.author Ranawaka Arachchige, Srimali
dc.date.accessioned 2025-06-16T08:52:35Z
dc.date.available 2025-06-16T08:52:35Z
dc.date.issued 2024
dc.identifier.citation Ranawaka Arachchige, Srimali (2024) CompoundCue Textual Question Answering Application For Compound Sentences. BSc. Dissertation, Informatics Institute of Technology en_US
dc.identifier.issn 20191114
dc.identifier.uri http://dlib.iit.ac.lk/xmlui/handle/123456789/2591
dc.description.abstract "In my research, I aimed to improve the accuracy of question-answering models specifically on compound sentences. Existing question-answering models tend to perform well on simple questions that require short answers, but struggle with more complex questions that involve compound sentences. Compound sentences can be difficult to parse and understand, leading to inaccurate answers. To solve this problem, I used a pre-trained question-answering model from the Hugging Face library, specifically the Bert-base-squad2 model. I fine-tuned this model on a dataset of squad v2 dataset that involved compound sentences. Fine-tuning involved updating the model's parameters on the new dataset to make it more accurate in answering questions that involved compound sentences." en_US
dc.language.iso en en_US
dc.subject Question answering en_US
dc.subject Compound sentences en_US
dc.subject Fine-tuning en_US
dc.title CompoundCue Textual Question Answering Application For Compound Sentences en_US
dc.type Thesis en_US


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