{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/1598"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/1598","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Transformer-based models for answer extraction in text-based question/answering","abstract":"The success of transformer-based language models has led to a surge of research in various natural language processing tasks, among which extractive question-answering/answer span detection, has received considerable attention in recent years. However, to date, no comprehensive studies have been conducted to compare and examine the performance of different transformer-based language models in the task of question-answering (QA). Furthermore, while these models can capture significant semantic and syntactic knowledge of a natural language, their potential for enhancing performance, in QA, through the incorporation of linguistic features remains unexplored. In this study, we compare the efficacy of multiple transformer-based models for the task of QA, as well as their performance on particular question types. Moreover, we investigate whether augmenting a set of linguistic features extracted from the question and context passage can enhance the performance of transformer-based language models in QA. In particular, we examine a few feature-augmented transformer-based architectures for the task of QA to explore the impact of these linguistic features on several transformer-based language models. Furthermore, an ablation study is conducted to analyze the individual effect of each feature. Through conducting extensive experiments on two question-answering datasets (i.e., SQuAD and NLQuAD), we show that the proposed framework can improve the performance of transformer-based models.","abstract_html":"The success of transformer-based language models has led to a surge of research in various natural language processing tasks, among which extractive question-answering/answer span detection, has received considerable attention in recent years. However, to date, no comprehensive studies have been conducted to compare and examine the performance of different transformer-based language models in the task of question-answering (QA). Furthermore, while these models can capture significant semantic and syntactic knowledge of a natural language, their potential for enhancing performance, in QA, through the incorporation of linguistic features remains unexplored. In this study, we compare the efficacy of multiple transformer-based models for the task of QA, as well as their performance on particular question types. Moreover, we investigate whether augmenting a set of linguistic features extracted from the question and context passage can enhance the performance of transformer-based language models in QA. In particular, we examine a few feature-augmented transformer-based architectures for the task of QA to explore the impact of these linguistic features on several transformer-based language models. Furthermore, an ablation study is conducted to analyze the individual effect of each feature. Through conducting extensive experiments on two question-answering datasets (i.e., SQuAD and NLQuAD), we show that the proposed framework can improve the performance of transformer-based models.","abstract_has_math":false,"creators":["Ahmadi Najafabadi, Marzieh"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Science (MSc)","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":["Davoudi, Heidar (Kourosh)"],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-04-01","date_published":"2023-04-01","updated_at":"2026-07-24T05:35:20Z","subjects":["Question-answering","Answer span detection","Transformer-based models","Pre-trained models"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/1598","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Davoudi, Heidar (Kourosh)"]},{"key":"dc:creator","label":"Author","values":["Ahmadi Najafabadi, Marzieh"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2023-04-24T15:19:18Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2023-04-24T15:19:18Z"]},{"key":"dc:date.issued","label":"Date","values":["2023-04-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MSc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Question-answering","Answer span detection","Transformer-based models","Pre-trained models"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10155/1598"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The success of transformer-based language models has led to a surge of research in various natural language processing tasks, among which extractive question-answering/answer span detection, has received considerable attention in recent years. However, to date, no comprehensive studies have been conducted to compare and examine the performance of different transformer-based language models in the task of question-answering (QA). Furthermore, while these models can capture significant semantic and syntactic knowledge of a natural language, their potential for enhancing performance, in QA, through the incorporation of linguistic features remains unexplored. In this study, we compare the efficacy of multiple transformer-based models for the task of QA, as well as their performance on particular question types. Moreover, we investigate whether augmenting a set of linguistic features extracted from the question and context passage can enhance the performance of transformer-based language models in QA. In particular, we examine a few feature-augmented transformer-based architectures for the task of QA to explore the impact of these linguistic features on several transformer-based language models. Furthermore, an ablation study is conducted to analyze the individual effect of each feature. Through conducting extensive experiments on two question-answering datasets (i.e., SQuAD and NLQuAD), we show that the proposed framework can improve the performance of transformer-based models."]},{"key":"dc:title","label":"Title","values":["Transformer-based models for answer extraction in text-based question/answering"]}]}],"canonical_facts":{"dc:contributor.advisor":["Davoudi, Heidar (Kourosh)"],"dc:creator":["Ahmadi Najafabadi, Marzieh"],"dc:date.accessioned":["2023-04-24T15:19:18Z"],"dc:date.available":["2023-04-24T15:19:18Z"],"dc:date.issued":["2023-04-01"],"dc:description.abstract":["The success of transformer-based language models has led to a surge of research in various natural language processing tasks, among which extractive question-answering/answer span detection, has received considerable attention in recent years. However, to date, no comprehensive studies have been conducted to compare and examine the performance of different transformer-based language models in the task of question-answering (QA). Furthermore, while these models can capture significant semantic and syntactic knowledge of a natural language, their potential for enhancing performance, in QA, through the incorporation of linguistic features remains unexplored. In this study, we compare the efficacy of multiple transformer-based models for the task of QA, as well as their performance on particular question types. Moreover, we investigate whether augmenting a set of linguistic features extracted from the question and context passage can enhance the performance of transformer-based language models in QA. In particular, we examine a few feature-augmented transformer-based architectures for the task of QA to explore the impact of these linguistic features on several transformer-based language models. Furthermore, an ablation study is conducted to analyze the individual effect of each feature. Through conducting extensive experiments on two question-answering datasets (i.e., SQuAD and NLQuAD), we show that the proposed framework can improve the performance of transformer-based models."],"dc:identifier.uri":["https://hdl.handle.net/10155/1598"],"dc:language.iso":["en"],"dc:subject":["Question-answering","Answer span detection","Transformer-based models","Pre-trained models"],"dc:title":["Transformer-based models for answer extraction in text-based question/answering"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_name":["Master of Science (MSc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:20Z"}