Fine-grained Fallacy Detection with Human Label Variation

• URL: http://arxiv.org/abs/2502.13853v1
• Date: Wed, 19 Feb 2025 16:18:44 GMT
• Title: Fine-grained Fallacy Detection with Human Label Variation
• Authors: Alan Ramponi, Agnese Daffara, Sara Tonelli,
• Abstract summary: We introduce Faina, the first dataset for fallacy detection that embraces multiple plausible answers and natural disagreement.<n>Fauna includes over 11K span-level annotations with overlaps across 20 fallacy types on social media posts in Italian.
• Score: 6.390923249771241
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce Faina, the first dataset for fallacy detection that embraces multiple plausible answers and natural disagreement. Faina includes over 11K span-level annotations with overlaps across 20 fallacy types on social media posts in Italian about migration, climate change, and public health given by two expert annotators. Through an extensive annotation study that allowed discussion over multiple rounds, we minimize annotation errors whilst keeping signals of human label variation. Moreover, we devise a framework that goes beyond "single ground truth" evaluation and simultaneously accounts for multiple (equally reliable) test sets and the peculiarities of the task, i.e., partial span matches, overlaps, and the varying severity of labeling errors. Our experiments across four fallacy detection setups show that multi-task and multi-label transformer-based approaches are strong baselines across all settings. We release our data, code, and annotation guidelines to foster research on fallacy detection and human label variation more broadly.

Related papers

• Adaptation Method for Misinformation Identification [8.581136866856255]
  We propose ADOSE, an Active Domain Adaptation (ADA) framework for multimodal fake news detection. ADOSE actively annotates a small subset of target samples to improve detection performance. ADOSE outperforms existing ADA methods by 2.72% $sim$ 14.02%, indicating the superiority of our model.
  arXiv  Detail & Related papers  (2025-04-19T04:18:32Z)
• Anomize: Better Open Vocabulary Video Anomaly Detection [38.36290705266484]
  Open Vocabulary Video Anomaly Detection seeks to detect and classify both base and novel anomalies. The first challenge is detection ambiguity, where the model struggles to assign accurate anomaly scores to unfamiliar anomalies. The second challenge is categorization confusion, where novel anomalies are often misclassified as visually similar base instances. We propose incorporating label relations to guide the encoding of new labels, thereby improving alignment between novel videos and their corresponding labels.
  arXiv  Detail & Related papers  (2025-03-23T14:49:32Z)
• Label Distribution Learning with Biased Annotations by Learning Multi-Label Representation [120.97262070068224]
  Multi-label learning (MLL) has gained attention for its ability to represent real-world data. Label Distribution Learning (LDL) faces challenges in collecting accurate label distributions.
  arXiv  Detail & Related papers  (2025-02-03T09:04:03Z)
• Harnessing Hierarchical Label Distribution Variations in Test Agnostic Long-tail Recognition [114.96385572118042]
  We argue that the variation in test label distributions can be broken down hierarchically into global and local levels. We propose a new MoE strategy, $mathsfDirMixE$, which assigns experts to different Dirichlet meta-distributions of the label distribution. We show that our proposed objective benefits from enhanced generalization by virtue of the variance-based regularization.
  arXiv  Detail & Related papers  (2024-05-13T14:24:56Z)
• VariErr NLI: Separating Annotation Error from Human Label Variation [23.392480595432676]
  We introduce a systematic methodology and a new dataset, VariErr (variation versus error) VariErr contains 7,732 validity judgments on 1,933 explanations for 500 re-annotated MNLI items. We find that state-of-the-art AED methods significantly underperform GPTs and humans.
  arXiv  Detail & Related papers  (2024-03-04T10:57:14Z)
• Appeal: Allow Mislabeled Samples the Chance to be Rectified in Partial Label Learning [55.4510979153023]
  In partial label learning (PLL), each instance is associated with a set of candidate labels among which only one is ground-truth. To help these mislabeled samples "appeal," we propose the first appeal-based framework.
  arXiv  Detail & Related papers  (2023-12-18T09:09:52Z)
• Probabilistic Test-Time Generalization by Variational Neighbor-Labeling [62.158807685159736]
  This paper strives for domain generalization, where models are trained exclusively on source domains before being deployed on unseen target domains. Probability pseudo-labeling of target samples to generalize the source-trained model to the target domain at test time. Variational neighbor labels that incorporate the information of neighboring target samples to generate more robust pseudo labels.
  arXiv  Detail & Related papers  (2023-07-08T18:58:08Z)
• Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection [149.23913018423022]
  Weakly supervised video anomaly detection aims to identify abnormal events in videos using only video-level labels. Two-stage self-training methods have achieved significant improvements by self-generating pseudo labels. We propose an enhancement framework by exploiting completeness and uncertainty properties for effective self-training.
  arXiv  Detail & Related papers  (2022-12-08T05:53:53Z)
• The 'Problem' of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation [21.513743126525622]
  We argue that this big open problem of human label variation persists and critically needs more attention to move our field forward. We reconcile different previously proposed notions of human label variation, provide a repository of publicly-available datasets with un-aggregated labels, depict approaches proposed so far, identify gaps and suggest ways forward.
  arXiv  Detail & Related papers  (2022-11-04T16:38:09Z)
• One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement [71.9401831465908]
  We investigate single-positive multi-label learning (SPMLL) where each example is annotated with only one relevant label. A novel method named proposed, i.e., Single-positive MultI-label learning with Label Enhancement, is proposed. Experiments on benchmark datasets validate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2022-06-01T14:26:30Z)
• End-to-End Annotator Bias Approximation on Crowdsourced Single-Label Sentiment Analysis [0.4925222726301579]
  Sentiment analysis is often a crowdsourcing task prone to subjective labels given by many annotators. It is not yet fully understood how the annotation bias of each annotator can be modeled correctly with state-of-the-art methods. Our contribution is an explanation and improvement for precise neural end-to-end bias modeling and ground truth estimation.
  arXiv  Detail & Related papers  (2021-11-03T16:20:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.