Persuasiveness
23 papers with code • 0 benchmarks • 0 datasets
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Most implemented papers
Echoes of Persuasion: The Effect of Euphony in Persuasive Communication
While the effect of various lexical, syntactic, semantic and stylistic features have been addressed in persuasive language from a computational point of view, the persuasive effect of phonetics has received little attention.
Incorporating Argument-Level Interactions for Persuasion Comments Evaluation using Co-attention Model
In this paper, we investigate the issue of persuasiveness evaluation for argumentative comments.
Estimating Causal Effects of Tone in Online Debates
In this paper, we estimate the causal effect of reply tones in debates on linguistic and sentiment changes in subsequent responses.
Outfit Compatibility Prediction and Diagnosis with Multi-Layered Comparison Network
Existing works about fashion outfit compatibility focus on predicting the overall compatibility of a set of fashion items with their information from different modalities.
Examining the Ordering of Rhetorical Strategies in Persuasive 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.
Delving into Variance Transmission and Normalization: Shift of Average Gradient Makes the Network Collapse
We propose Parametric Weights Standardization (PWS), a fast and robust to mini-batch size module used for conv filters, to solve the shift of the average gradient.
Learning to Rank Rationales for Explainable Recommendation
Seeing this gap, we propose a model named Semantic-Enhanced Bayesian Personalized Explanation Ranking (SE-BPER) to effectively combine the interaction information and semantic information.
ImageArg: A Multi-modal Tweet Dataset for Image Persuasiveness Mining
The growing interest in developing corpora of persuasive texts has promoted applications in automated systems, e. g., debating and essay scoring systems; however, there is little prior work mining image persuasiveness from an argumentative perspective.
Audience-Centric Natural Language Generation via Style Infusion
While existing approaches demonstrate textual style transfer with large volumes of parallel or non-parallel data, we argue that grounding style on audience-independent external factors is innately limiting for two reasons.
Can Large Language Models Transform Computational Social Science?
We conclude that the performance of today's LLMs can augment the CSS research pipeline in two ways: (1) serving as zero-shot data annotators on human annotation teams, and (2) bootstrapping challenging creative generation tasks (e. g., explaining the underlying attributes of a text).