Utilizing the Transformer Architecture for Question Answering

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dc.contributor.advisorHoque Prince, Enamul
dc.contributor.advisorHuang, Xiangji "Jimmy"
dc.contributor.authorLaskar, Md Tahmid Rahman
dc.date.accessioned2021-03-08T17:24:08Z
dc.date.available2021-03-08T17:24:08Z
dc.date.copyright2020-11
dc.date.issued2021-03-08
dc.date.updated2021-03-08T17:24:08Z
dc.degree.disciplineComputer Science
dc.degree.levelMaster's
dc.degree.nameMSc - Master of Science
dc.description.abstractThe Question Answering (QA) task aims at building systems that can automatically answer a question or query about the given document(s). In this thesis, we utilize the transformer, a state-of-the-art neural architecture to study two QA problems: the answer sentence selection and the answer summary generation. For answer sentence selection, we present two new approaches that rank a list of candidate answers for a given question by utilizing different contextualized embeddings with the encoder of transformer. For answer summary generation, we study the query focused abstractive text summarization task to generate a summary in natural language from the source document(s) for a given query. For this task, we utilize transformer to address the lack of large training datasets issue in single-document scenarios and no labeled training datasets issue in multi-document scenarios. Based on extensive experiments, we observe that our proposed approaches obtain impressive results across several benchmark QA datasets.
dc.identifier.urihttp://hdl.handle.net/10315/38195
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectInformation technology
dc.subject.keywordsQuestion Answering
dc.subject.keywordsText Summarization
dc.subject.keywordsQuery Focused Abstractive Summarization
dc.subject.keywordsDeep Learning
dc.subject.keywordsMachine Learning
dc.subject.keywordsTransformer
dc.subject.keywordsBERT
dc.titleUtilizing the Transformer Architecture for Question Answering
dc.typeElectronic Thesis or Dissertation

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