{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/120261"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/120261","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Controllable natural language generation for audience-centric styles","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","abstract_has_math":false,"creators":["Moorjani, Samraj"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Sundaram, Hari"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-05","date_published":"2023-05","updated_at":"2026-07-22T22:24:57Z","subjects":["Natural Langauge Generation","Persuasiveness","Audience-centric Styles","Persuasive Natural Language Generation"],"languages":["en","eng"],"rights":["Copyright 2023 Samraj Moorjani"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/120261","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Sundaram, Hari"]},{"key":"dc:creator","label":"Author","values":["Moorjani, Samraj"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-05","2023-04-14"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"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":["Natural Langauge Generation","Persuasiveness","Audience-centric Styles","Persuasive Natural Language Generation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2023 Samraj Moorjani"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/120261"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","The student, Samraj Moorjani, accepted the attached license on 2023-04-12 at 17:34.","The student, Samraj Moorjani, submitted this Thesis for approval on 2023-04-12 at 17:35.","This Thesis was approved for publication on 2023-04-14 at 13:25.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18970 on 2023-09-01 at 17:08:31","Adopting contextually appropriate, audience-tailored linguistic styles, namely persuasiveness, is critical to the success of user-centric language generation systems (e.g., chatbots, computer-aided writing, dialog systems). While existing approaches demonstrate textual style transfer with large volumes of data, grounding style on audience-independent factors is innately limiting because many stylistic objectives (e.g., persuasiveness, memorability, empathy) are hard to define without audience feedback. In this thesis, we first propose the novel task of style infusion - infusing the stylistic preferences of audiences in pretrained language generation models. Since humans are better at pairwise comparisons than direct scoring - i.e., is Sample-A more persuasive than SampleB - we leverage limited pairwise human judgments to bootstrap a style analysis model and augment our seed set of judgments. We infuse the learned textual style in a GPT-2 based text generator while balancing fluency and style adoption. With quantitative and novel qualitative assessments, we show that our infusion approach can generate compelling stylized examples with generic text prompts. We then utilize complex linguistic features strongly correlated with persuasiveness to guide the generation of sequence-to-sequence models. We explore modifications of two approaches - an edit-then-prototype model and the style infusion architecture - to exhibit a “tuning-knob”- like control over the speed of text (i.e., how quickly content is covered). We empirically show that our modifications lead to strong controls over generated text and discuss directions to improve the fluency and control of generations further."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Controllable natural language generation for audience-centric styles"]}]}],"canonical_facts":{"dc:contributor":["Sundaram, Hari"],"dc:creator":["Moorjani, Samraj"],"dc:date":["2023-05","2023-04-14"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","The student, Samraj Moorjani, accepted the attached license on 2023-04-12 at 17:34.","The student, Samraj Moorjani, submitted this Thesis for approval on 2023-04-12 at 17:35.","This Thesis was approved for publication on 2023-04-14 at 13:25.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18970 on 2023-09-01 at 17:08:31","Adopting contextually appropriate, audience-tailored linguistic styles, namely persuasiveness, is critical to the success of user-centric language generation systems (e.g., chatbots, computer-aided writing, dialog systems). While existing approaches demonstrate textual style transfer with large volumes of data, grounding style on audience-independent factors is innately limiting because many stylistic objectives (e.g., persuasiveness, memorability, empathy) are hard to define without audience feedback. In this thesis, we first propose the novel task of style infusion - infusing the stylistic preferences of audiences in pretrained language generation models. Since humans are better at pairwise comparisons than direct scoring - i.e., is Sample-A more persuasive than SampleB - we leverage limited pairwise human judgments to bootstrap a style analysis model and augment our seed set of judgments. We infuse the learned textual style in a GPT-2 based text generator while balancing fluency and style adoption. With quantitative and novel qualitative assessments, we show that our infusion approach can generate compelling stylized examples with generic text prompts. We then utilize complex linguistic features strongly correlated with persuasiveness to guide the generation of sequence-to-sequence models. We explore modifications of two approaches - an edit-then-prototype model and the style infusion architecture - to exhibit a “tuning-knob”- like control over the speed of text (i.e., how quickly content is covered). We empirically show that our modifications lead to strong controls over generated text and discuss directions to improve the fluency and control of generations further."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/120261"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Samraj Moorjani"],"dc:subject":["Natural Langauge Generation","Persuasiveness","Audience-centric Styles","Persuasive Natural Language Generation"],"dc:title":["Controllable natural language generation for audience-centric styles"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:57Z"}