Examining the Ordering of Rhetorical Strategies in Persuasive Requests

• URL: http://arxiv.org/abs/2010.04625v2
• Date: Mon, 12 Oct 2020 00:37:11 GMT
• Title: Examining the Ordering of Rhetorical Strategies in Persuasive Requests
• Authors: Omar Shaikh, Jiaao Chen, Jon Saad-Falcon, Duen Horng Chau and Diyi Yang
• Abstract summary: We use a Variational Autoencoder model to disentangle content and rhetorical strategies in textual requests from a large-scale loan request corpus. 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.
• Score: 58.63432866432461
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Persuasion Strategies in Advertisements [68.70313043201882]
  We introduce an extensive vocabulary of persuasion strategies and build the first ad image corpus annotated with persuasion strategies. We then formulate the task of persuasion strategy prediction with multi-modal learning. We conduct a real-world case study on 1600 advertising campaigns of 30 Fortune-500 companies.
  arXiv  Detail & Related papers  (2022-08-20T07:33:13Z)
• Do You Know My Emotion? Emotion-Aware Strategy Recognition towards a Persuasive Dialogue System [14.724751780218297]
  Persuasive strategy recognition task requires the system to recognize the adopted strategy of the persuader according to the conversation. Previous methods mainly focus on the contextual information, little is known about incorporating the psychological feedback, i.e. emotion of the persuadee, to predict the strategy. We propose a Cross-channel Feedback memOry Network (CFO-Net) to leverage the emotional feedback to iteratively measure the potential benefits of strategies and incorporate them into the contextual-aware dialogue information.
  arXiv  Detail & Related papers  (2022-06-24T06:24:46Z)
• RESPER: Computationally Modelling Resisting Strategies in Persuasive Conversations [0.7505101297221454]
  We propose a generalised framework for identifying resisting strategies in persuasive conversations. Our experiments reveal the asymmetry of power roles in non-collaborative goal-directed conversations. We also investigate the role of different resisting strategies on the conversation outcome.
  arXiv  Detail & Related papers  (2021-01-26T03:44:17Z)
• Weakly-Supervised Hierarchical Models for Predicting Persuasive Strategies in Good-faith Textual Requests [22.58861442978803]
  We introduce a large-scale multi-domain text corpus for modeling persuasive strategies in good-faith text requests. We design a hierarchical weakly-supervised latent variable model that can leverage partially labeled data to predict such associated persuasive strategies for each sentence. Experimental results showed that our proposed method outperformed existing semi-supervised baselines significantly.
  arXiv  Detail & Related papers  (2021-01-16T02:31:04Z)
• Persuasive Dialogue Understanding: the Baselines and Negative Results [27.162062321321805]
  We demonstrate the limitations of a Transformer-based approach coupled with Conditional Random Field (CRF) for the task of persuasive strategy recognition. We leverage inter- and intra-speaker contextual semantic features, as well as label dependencies to improve the recognition.
  arXiv  Detail & Related papers  (2020-11-19T16:52:43Z)
• Learning to Infer User Hidden States for Online Sequential Advertising [52.169666997331724]
  We propose our Deep Intents Sequential Advertising (DISA) method to address these issues. The key part of interpretability is to understand a consumer's purchase intent which is, however, unobservable (called hidden states)
  arXiv  Detail & Related papers  (2020-09-03T05:12:26Z)
• Multi-Stage Conversational Passage Retrieval: An Approach to Fusing Term Importance Estimation and Neural Query Rewriting [56.268862325167575]
  We tackle conversational passage retrieval (ConvPR) with query reformulation integrated into a multi-stage ad-hoc IR system. We propose two conversational query reformulation (CQR) methods: (1) term importance estimation and (2) neural query rewriting. For the former, we expand conversational queries using important terms extracted from the conversational context with frequency-based signals. For the latter, we reformulate conversational queries into natural, standalone, human-understandable queries with a pretrained sequence-tosequence model.
  arXiv  Detail & Related papers  (2020-05-05T14:30:20Z)
• Knowledgeable Dialogue Reading Comprehension on Key Turns [84.1784903043884]
  Multi-choice machine reading comprehension (MRC) requires models to choose the correct answer from candidate options given a passage and a question. Our research focuses dialogue-based MRC, where the passages are multi-turn dialogues. It suffers from two challenges, the answer selection decision is made without support of latently helpful commonsense, and the multi-turn context may hide considerable irrelevant information.
  arXiv  Detail & Related papers  (2020-04-29T07:04:43Z)
• Dynamic Knowledge Routing Network For Target-Guided Open-Domain Conversation [79.7781436501706]
  We propose a structured approach that controls the intended content of system responses by introducing coarse-grained keywords. We also propose a novel dual discourse-level target-guided strategy to guide conversations to reach their goals smoothly with higher success rate.
  arXiv  Detail & Related papers  (2020-02-04T09:49:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.