Utilizing the Transformer Architecture for Question Answering

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.

Author dc:creator

• Laskar, Md Tahmid Rahman

Advisors dc:contributor.advisor

• Hoque Prince, Enamul
• Huang, Xiangji "Jimmy"

• Information technology

Statement dc:rights

• Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.

Language dc:language

en

Handle dc:identifier.uri

http://hdl.handle.net/10315/38195

OAI identifier oai:identifier

oai:yorkspace.library.yorku.ca:10315/38195