# Weakly-Supervised Hierarchical Models for Predicting Persuasive Strategies in Good-faith Textual Requests

arXiv: https://arxiv.org/abs/2101.06351 · HTML: https://mlsanity.com/paper/2101.06351/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 53 references that are in the map.
- First posted on arXiv 16 Jan 2021.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Unsupervised Data Augmentation for Consistency Training](https://mlsanity.com/paper/1904.12848/) (2019)
- [Latent Retrieval for Weakly Supervised Open Domain Question Answering](https://mlsanity.com/paper/1906.00300/) (2019)
- [Semi-Supervised Learning with Deep Generative Models](https://mlsanity.com/paper/1406.5298/) (2014)
- [Skip-Thought Vectors](https://mlsanity.com/paper/1506.06726/) (2015)
- [Adversarial Training Methods for Semi-Supervised Text Classification](https://mlsanity.com/paper/1605.07725/) (2016)
- [Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good](https://mlsanity.com/paper/1906.06725/) (2019)
- [Semi-supervised Sequence Learning](https://mlsanity.com/paper/1511.01432/) (2015)
- [Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation](https://mlsanity.com/paper/1502.02734/) (2015)
- [MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification](https://mlsanity.com/paper/2004.12239/) (2020)
- [Winning Arguments: Interaction Dynamics and Persuasion Strategies in Good-faith Online Discussions](https://mlsanity.com/paper/1602.01103/) (2016)
- [Improved Variational Autoencoders for Text Modeling using Dilated Convolutions](https://mlsanity.com/paper/1702.08139/) (2017)
- [A Discrete Hard EM Approach for Weakly Supervised Question Answering](https://mlsanity.com/paper/1909.04849/) (2019)
- [Variational Pretraining for Semi-supervised Text Classification](https://mlsanity.com/paper/1906.02242/) (2019)
- [Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasion](https://mlsanity.com/paper/1708.09085/) (2017)
- [Exploring the Role of Prior Beliefs for Argument Persuasion](https://mlsanity.com/paper/1906.11301/) (2019)
- [How to Ask for a Favor: A Case Study on the Success of Altruistic Requests](https://mlsanity.com/paper/1405.3282/) (2014)
- [What Gets Echoed? Understanding the "Pointers" in Explanations of Persuasive Arguments](https://mlsanity.com/paper/1911.00523/) (2019)
- [The Mean-Field Approximation: Information Inequalities, Algorithms, and Complexity](https://mlsanity.com/paper/1802.06126/) (2018)
- [Examining the Ordering of Rhetorical Strategies in Persuasive Requests](https://mlsanity.com/paper/2010.04625/) (2020)
- [Semi-Supervised Models via Data Augmentationfor Classifying Interactive Affective Responses](https://mlsanity.com/paper/2004.10972/) (2020)

## Built on by (most cited first, 16 of 16)

- [LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety in Large Language Models](https://mlsanity.com/paper/2504.10430/) (2025)
- [Must Read: A Comprehensive Survey of Computational Persuasion](https://mlsanity.com/paper/2505.07775/) (2025)
- [Werewolf Among Us: A Multimodal Dataset for Modeling Persuasion Behaviors in Social Deduction Games](https://mlsanity.com/paper/2212.08279/) (2022)
- [A Video Is Worth 4096 Tokens: Verbalize Videos To Understand Them In Zero Shot](https://mlsanity.com/paper/2305.09758/) (2023)
- [Towards Strategic Persuasion with Language Models](https://mlsanity.com/paper/2509.22989/) (2025)
- [Human Choice Prediction in Language-based Persuasion Games: Simulation-based Off-Policy Evaluation](https://mlsanity.com/paper/2305.10361/) (2023)
- [It Takes Two to Negotiate: Modeling Social Exchange in Online Multiplayer Games](https://mlsanity.com/paper/2311.08666/) (2023)
- [Designing an Automatic Agent for Repeated Language based Persuasion Games](https://mlsanity.com/paper/2105.04976/) (2021)
- [NormMark: A Weakly Supervised Markov Model for Socio-cultural Norm Discovery](https://mlsanity.com/paper/2305.16598/) (2023)
- [Communication is All You Need: Persuasion Dataset Construction via Multi-LLM Communication](https://mlsanity.com/paper/2502.08896/) (2025)
- [Persuasion Strategies in Advertisements](https://mlsanity.com/paper/2208.09626/) (2022)
- [Hatred Stems from Ignorance! Distillation of the Persuasion Modes in Countering Conversational Hate Speech](https://mlsanity.com/paper/2403.15449/) (2024)
- [Communication styles and reader preferences of LLM- and human-authored COVID-19 information explanations: a case study](https://mlsanity.com/paper/2505.08143/) (2025)
- [A Generalizable Rhetorical Strategy Annotation Model Using LLM-based Debate Simulation and Labelling](https://mlsanity.com/paper/2510.15081/) (2025)
- [Improving User Behavior Prediction: Leveraging Annotator Metadata in Supervised Machine Learning Models](https://mlsanity.com/paper/2503.21000/) (2025)
- [Persuasion Index: A Theory-Guided Framework for Persuasion Analysis](https://mlsanity.com/paper/2606.14580/) (2026)

## Abstract (arXiv)

Modeling persuasive language has the potential to better facilitate our decision-making processes. Despite its importance, computational modeling of persuasion is still in its infancy, largely due to the lack of benchmark datasets that can provide quantitative labels of persuasive strategies to expedite this line of research. To this end, we introduce a large-scale multi-domain text corpus for modeling persuasive strategies in good-faith text requests. Moreover, we design a hierarchical weakly-supervised latent variable model that can leverage partially labeled data to predict such associated persuasive strategies for each sentence, where the supervision comes from both the overall document-level labels and very limited sentence-level labels. Experimental results showed that our proposed method outperformed existing semi-supervised baselines significantly. We have publicly released our code at https://github.com/GT-SALT/Persuasion_Strategy_WVAE.