Controllable natural language generation for audience-centric styles

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2023

Author dc:creator

• Moorjani, Samraj

Contributors dc:contributor

• Sundaram, Hari

• Natural Langauge Generation
• Persuasiveness
• Audience-centric Styles
• Persuasive Natural Language Generation

Statement dc:rights

• Copyright 2023 Samraj Moorjani

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/120261

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F120261