Examining the Ordering of Rhetorical Strategies in Persuasive Requests
| Autor: | Omar Shaikh, Polo Chau, Jiaao Chen, Diyi Yang, Jon Saad-Falcon |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Cognitive science Computer Science - Machine Learning Persuasion Computer Science - Computation and Language Computer Science - Artificial Intelligence Computer science media_common.quotation_subject 02 engineering and technology 010501 environmental sciences 16. Peace & justice 01 natural sciences Machine Learning (cs.LG) Argumentation theory Artificial Intelligence (cs.AI) Order (business) Loan 0202 electrical engineering electronic engineering information engineering Rhetorical question 020201 artificial intelligence & image processing Computation and Language (cs.CL) 0105 earth and related environmental sciences media_common |
| Zdroj: | EMNLP (Findings) |
| Popis: | Interpreting how persuasive language influences audiences has implications across many domains like advertising, argumentation, and propaganda. Persuasion relies on more than a message's content. Arranging the order of the message itself (i.e., ordering specific rhetorical strategies) also plays an important role. To examine how strategy orderings contribute to persuasiveness, we first utilize a Variational Autoencoder model to disentangle content and rhetorical strategies in textual requests from a large-scale loan request corpus. We then visualize interplay between content and strategy through an attentional LSTM that predicts the success of textual requests. We find that specific (orderings of) strategies interact uniquely with a request's content to impact success rate, and thus the persuasiveness of a request. Findings of EMNLP 2020 |
| Databáze: | OpenAIRE |
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