Structured Representation Learning for Online Debate Stance Prediction
Online debates can help provide valuable information about various perspectives on a wide range of issues. However, understanding the stances expressed in these debates is a highly challenging task, which requires modeling both textual content and users{'} conversational interactions. Current approaches take a collective classification approach, which ignores the relationships between different debate topics. In this work, we suggest to view this task as a representation learning problem, and embed the text and authors jointly based on their interactions. We evaluate our model over the Internet Argumentation Corpus, and compare different approaches for structural information embedding. Experimental results show that our model can achieve significantly better results compared to previous competitive models.
Code (1)
Tasks
General ClassificationPredictionRepresentation LearningSimilar Papers 제목 키워드 기반
A French Corpus of Québec’s Parliamentary Debates
Parliamentary debates offer a window on political stances as well as a repository of linguistic and semantic knowledge. They provide insights and reasons for laws and regulations that impact electors in their everyday li…
Exploring Health Misinformation Detection with Multi-Agent Debate
Fact-checking health-related claims has become increasingly critical as misinformation proliferates online. Effective verification requires both the retrieval of high-quality evidence and rigorous reasoning processes. In…
DEBISS: a Corpus of Individual, Semi-structured and Spoken Debates
The process of debating is essential in our daily lives, whether in studying, work activities, simple everyday discussions, political debates on TV, or online discussions on social networks. The range of uses for debates…
Speaker DiarizationArgument MiningAgreement Prediction of Arguments in Cyber Argumentation for Detecting Stance Polarity and Intensity
In online debates, users express different levels of agreement/disagreement with one another{'}s arguments and ideas. Often levels of agreement/disagreement are implicit in the text, and must be predicted to analyze coll…
regressionStance DetectionDebating Europe: A Multilingual Multi-Target Stance Classification Dataset of Online Debates
We present a new dataset of online debates in English, annotated with stance. The dataset was scraped from the “Debating Europe” platform, where users exchange opinions over different subjects related to the European Uni…
Stance ClassificationXLM-R