Are You Convinced? Choosing the More Convincing Evidence with a Siamese Network (P19-1)
Copied to clipboard
Martin Gleize, Eyal Shnarch, Leshem Choshen, Lena Dankin, Guy Moshkowich, Ranit Aharonov, Noam Slonim
| Challenge: | Recent advances in argument detection have made it easier to identify the more convincing arguments. |
| Approach: | They propose a new data set of pairs of evidence labeled for convincingness that is more challenging than existing alternatives. |
| Outcome: | The proposed method outperforms baselines on convincingness data and its own. |
Similar Papers
Can Language Models Recognize Convincing Arguments? (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies have found that large language models can generate persuasive content without engaging in human experimentation. |
| Approach: | They extend a dataset with debates, votes, and user traits to measure LLMs' ability to distinguish between strong and weak arguments, predict stances based on beliefs and demographic characteristics, and determine appeal of argument to individual based upon their traits. |
| Outcome: | The proposed tasks outperform human predictions in detecting convincing arguments in debates, votes, and user traits. |
Detecting Winning Arguments with Large Language Models and Persuasion Strategies (2026.findings-eacl)
Copied to clipboard
| Challenge: | Recent studies have focused on predicting winning arguments, i.e., those that effectively convince a reader to adopt a certain opinion. |
| Approach: | They propose to use large language models with a chain-of-thought framework to guide reasoning over six persuasion strategies to determine persuasiveness. |
| Outcome: | The proposed approach leverages large language models with a chain-of-thought framework that guides reasoning over six persuasion strategies. |
What Evidence Do Language Models Find Convincing? (2024.acl-long)
Copied to clipboard
| Challenge: | Current retrieval-augmented language models are tasked with subjective, contentious, and conflicting queries. |
| Approach: | They construct a dataset that pairs controversial queries with real-world evidence documents . they find current models rely heavily on relevance of a website to the query . |
| Outcome: | The proposed dataset pairs controversial queries with real-world evidence documents that contain different facts, arguments, and answers. |
Argument Mining for Review Helpfulness Prediction (2022.emnlp-main)
Copied to clipboard
| Challenge: | Argumentational features have been shown to be promising indicators of product review helpfulness, but their utility has been limited due to the lack of resources and large-scale experiments investigating their utility. |
| Approach: | They present an argumentational argumentation model that annotates 878 Amazon reviews on headphones and uses it to evaluate argument quality. |
| Outcome: | The proposed model improves the state-of-the-art model under text-only and text-and-image settings. |
Offer a Different Perspective: Modeling the Belief Alignment of Arguments in Multi-party Debates (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing work on persuasion in online forums focuses on identifying debate winners and winning negotiation games. |
| Approach: | They adopt a hierarchical generative Variational Autoencoder model to model winning arguments . they propose competing hypotheses about the nature of argumentation . |
| Outcome: | The proposed model predicts winning arguments in reddit debates . it uses a hierarchical generative Variational Autoencoder to model argumentation . |
Which Side Are You On? A Multi-task Dataset for End-to-End Argument Summarisation and Evaluation (2024.findings-acl)
Copied to clipboard
Hao Li, Yuping Wu, Viktor Schlegel, Riza Batista-Navarro, Tharindu Madusanka, Iqra Zahid, Jiayan Zeng, Xiaochi Wang, Xinran He, Yizhi Li, Goran Nenadic
| Challenge: | Recent advances in large language models (LLMs) have made it difficult to build an automated debate system that helps people to synthesise persuasive arguments. |
| Approach: | They propose to use an argument mining dataset to capture the end-to-end process of preparing an argumentative essay for a debate. |
| Outcome: | The proposed dataset shows that it performs better on individual tasks than on human-centred evaluations. |
Prior Beliefs Prejudice LLM-as-Judge: Evidence from Persuasion Evaluation (2026.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models are increasingly used as judges to evaluate text quality, content and assess arguments. |
| Approach: | They propose to exploit belief-conditioned rating inflation by using persuasion-based probing to examine persuasive arguments. |
| Outcome: | The proposed model fails to evaluate persuasive arguments based on belief alignment . the model fails in three of the three tasks, with belief-conditioned rating inflation accounting for 88% of cases. |
Automatic Argument Quality Assessment - New Datasets and Methods (D19-1)
Copied to clipboard
Assaf Toledo, Shai Gretz, Edo Cohen-Karlik, Roni Friedman, Elad Venezian, Dan Lahav, Michal Jacovi, Ranit Aharonov, Noam Slonim
| Challenge: | 6.3k arguments were collected from contributors of various levels, and are released as part of this work. |
| Approach: | They propose to use a language model to annotate arguments for argument ranking and argument-pair classification. |
| Outcome: | The proposed methods outperform state-of-the-art methods in the argument ranking task and argument-pair classification task. |
Yes, we can! Mining Arguments in 50 Years of US Presidential Campaign Debates (P19-1)
Copied to clipboard
| Challenge: | Political debates are a natural application scenario for Argument Mining. |
| Approach: | They propose an argument mining approach to political debates that uses argument components to annotate 39 political debate from the last 50 years of US presidential campaigns. |
| Outcome: | The proposed approach outperforms baselines in argument mining over political debates. |
AutoPersuade: A Framework for Evaluating and Explaining Persuasive Arguments (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing tools for persuasion are well-equipped to identify which of a pre-existing set of messages is most persuasive, but they do not offer causal evidence on whether or how they have succeeded. |
| Approach: | They propose a framework for identifying topical components of persuasive arguments that are autopersuade. |
| Outcome: | The proposed framework validates the results through human studies and out-of-sample predictions. |