{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/104919"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/104919","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Multi task learning and incorporating common sense knowledge for question answering","abstract":"Question Answering (QA) system is an automated approach to retrieve correct responses to the questions asked by human in natural language. Reading comprehension (RC)in contrast to information retrieval, requires integrating information and reasoning about events, entities, and their relations across a full document. Immense progress has been made in the recent years for this task, since the advent of deep learning and use of sequence to sequence models for NLP. This thesis deals with two complex tasks in Question Answering with their own inherent challenges: Multi Task Learning for Narrative Question Answering, which involves developing models to deal with the complexity of the domain of stories, movie scripts and human written answers, and second task is to develop novel ways of incorporating common sense knowledge from external knowledge bases for automated question answering. The models developed for these tasks help to advance research in the area of question answering and highlights some of the shortcomings of the methods proposed in literature.","abstract_html":"Question Answering (QA) system is an automated approach to retrieve correct responses to the questions asked by human in natural language. Reading comprehension (RC)in contrast to information retrieval, requires integrating information and reasoning about events, entities, and their relations across a full document. Immense progress has been made in the recent years for this task, since the advent of deep learning and use of sequence to sequence models for NLP. This thesis deals with two complex tasks in Question Answering with their own inherent challenges: Multi Task Learning for Narrative Question Answering, which involves developing models to deal with the complexity of the domain of stories, movie scripts and human written answers, and second task is to develop novel ways of incorporating common sense knowledge from external knowledge bases for automated question answering. The models developed for these tasks help to advance research in the area of question answering and highlights some of the shortcomings of the methods proposed in literature.","abstract_has_math":false,"creators":["Agarwal, Dhruv"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Hockenmaier, Julia"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-08-23T20:02:06Z","date_published":"2019-08-23T20:02:06Z","updated_at":"2026-07-22T22:24:42Z","subjects":["NLP,Question Answering, Deep Learning, narrativeQA, common sense"],"languages":["en"],"rights":["Copyright 2019 Dhruv Agarwal"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/104919","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hockenmaier, Julia"]},{"key":"dc:creator","label":"Author","values":["Agarwal, Dhruv"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-08-23T20:02:06Z","2019-04-24","2019-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["NLP,Question Answering, Deep Learning, narrativeQA, common sense"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Dhruv Agarwal"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/104919"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Question Answering (QA) system is an automated approach to retrieve correct responses to the questions asked by human in natural language. Reading comprehension (RC)in contrast to information retrieval, requires integrating information and reasoning about events, entities, and their relations across a full document. Immense progress has been made in the recent years for this task, since the advent of deep learning and use of sequence to sequence models for NLP. This thesis deals with two complex tasks in Question Answering with their own inherent challenges: Multi Task Learning for Narrative Question Answering, which involves developing models to deal with the complexity of the domain of stories, movie scripts and human written answers, and second task is to develop novel ways of incorporating common sense knowledge from external knowledge bases for automated question answering. The models developed for these tasks help to advance research in the area of question answering and highlights some of the shortcomings of the methods proposed in literature.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2019-08-22 without embargo terms","The student, Dhruv Agarwal, accepted the attached license on 2019-04-24 at 01:46.","The student, Dhruv Agarwal, submitted this Thesis for approval on 2019-04-24 at 01:49.","This Thesis was approved for publication on 2019-04-24 at 08:41.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13862 on 2019-08-22 at 14:46:25","Made available in DSpace on 2019-08-23T20:02:06Z (GMT). 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Immense progress has been made in the recent years for this task, since the advent of deep learning and use of sequence to sequence models for NLP. This thesis deals with two complex tasks in Question Answering with their own inherent challenges: Multi Task Learning for Narrative Question Answering, which involves developing models to deal with the complexity of the domain of stories, movie scripts and human written answers, and second task is to develop novel ways of incorporating common sense knowledge from external knowledge bases for automated question answering. The models developed for these tasks help to advance research in the area of question answering and highlights some of the shortcomings of the methods proposed in literature.","Submission original under an indefinite embargo labeled 'Open Access'. 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