{"id":{"repo_id":"york","oai_identifier":"oai:yorkspace.library.yorku.ca:10315/38195"},"canonical_url":"https://search.dev.ndltd.org/etd/york/oai:yorkspace.library.yorku.ca:10315/38195","repository":{"repo_id":"york","name":"York University","base_url":"https://yorkspace.library.yorku.ca/oai/request"},"display":{"title":"Utilizing the Transformer Architecture for Question Answering","abstract":"The 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.","abstract_html":"The 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. 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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. 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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.uri":["http://hdl.handle.net/10315/38195"],"dc:language":["en"],"dc:rights":["Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests."],"dc:subject":["Information technology"],"dc:title":["Utilizing the Transformer Architecture for Question Answering"],"dc:type":["Electronic Thesis or Dissertation"]},"updated_at":"2026-07-24T06:34:03Z"}