Online debates provide critical insights into public opinion and societal trends, yet the unstructured nature of these discussions presents significant challenges for analysis. In this paper, we present SAGESSE, a novel argumentation parsing pipeline tailored to Reddit debates, leveraging the capabilities of large language models to structure and interpret complex online arguments. SAGESSE generates detailed argument maps that organize debates systematically, offering a clearer understanding of discourse dynamics. We have developed a web application where users can select controversial Reddit topics and visualize the corresponding argument maps generated from user comments. This tool has the potential to aid analysts, policymakers, and researchers in tracking debate progress, gauging public sentiment, and identifying influential arguments. The web application is available at https://modemos.epfl.ch/sagesse/ .

Understanding narratives requires reasoning about the cause-and-effect relationships between events mentioned in the text.While existing foundation models yield impressive results in many NLP tasks requiring reasoning, it is unclear whether they understand the complexity of the underlying network of causal relationships of events in narratives.In this work, we present CRAB, a new Causal Reasoning Assessment Benchmark designed to evaluate causal understanding of events in real-world narratives.CRAB contains fine-grained, contextual causality annotations for ∼ 2.7K pairs of real-world events that describe various newsworthy event timelines (e.g., the acquisition of Twitter by Elon Musk).Using CRAB, we measure the performance of several large language models, demonstrating that most systems achieve poor performance on the task.Motivated by classical causal principles, we also analyze the causal structures of groups of events in CRAB, and find that models perform worse on causal reasoning when events are derived from complex causal structures compared to simple linear causal chains.We make our dataset and code available to the research community.

John Pavlopoulos, Leo Laugier, Alexandros Xenos, Jeffrey Sorensen, Ion Androutsopoulos. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.

Platforms that support online commentary, from social networks to news sites, are increasingly leveraging machine learning to assist their moderation efforts.But this process does not typically provide feedback to the author that would help them contribute according to the community guidelines.This is prohibitively time-consuming for human moderators to do, and computational approaches are still nascent.This work focuses on models that can help suggest rephrasings of toxic comments in a more civil manner.Inspired by recent progress in unpaired sequence-tosequence tasks, a self-supervised learning model is introduced, called CAE-T5 1 .CAE-T5 employs a pre-trained text-to-text transformer, which is fine tuned with a denoising and cyclic auto-encoder loss.Experimenting with the largest toxicity detection dataset to date (Civil Comments) our model generates sentences that are more fluent and better at preserving the initial content compared to earlier text style transfer systems which we compare with using several scoring systems and human evaluation.