A Resource for Enthymeme Detection in Controversial Political Discourse

• URL: http://arxiv.org/abs/2606.12186v1
• Date: Wed, 10 Jun 2026 15:09:36 GMT
• Title: A Resource for Enthymeme Detection in Controversial Political Discourse
• Abstract summary: We present a resource of 1,482 tweets annotated by five annotators for the presence of enthymemes and their argument structure.<n>We propose annotation guidelines anchored in Walton's argumentation schemes, offering a structured and constrained approach.<n>Preliminary experiments show that models trained on annotator disagreement outperform models trained on hard majority-vote labels.
• Score: 0.42970700836450487
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Enthymemes, arguments with unstated premises or conclusions, are pervasive in persuasive discourse, yet their annotation remains notoriously subjective. We present a resource of 1,482 tweets from politically controversial discourse, annotated by five annotators for the presence of enthymemes and their argument structure, designed to study label variation. We first revisit the definition of enthymemes and propose annotation guidelines anchored in Walton's argumentation schemes, offering a structured and constrained approach that nonetheless preserves room for the interpretive nature of the task. This contrasts with past resources, which tend to eliminate disagreement, obscuring its sources and preventing investigation of its potential benefits for model performance. We further propose a complexity analysis of the task, identifying where annotation imposes high cognitive load and may give rise to inconsistent annotation. Our preliminary experiments show that models trained on annotator disagreement outperform models trained on hard majority-vote labels. We close by reflecting on how structural openness in enthymeme definitions and guidelines enables the study of variation in subjective inferential processes for future resources and downstream NLP applications concerned with human inference.

Related papers

• Embedding Models for Stance-Aware Argument Retrieval [16.736290000291188]
  We show how dense embedding models (hereafter, models) can serve as the basis of semantic search incorporating this dual assessment.<n>We show experimentally that existing models struggle with asymmetric reasoning, exhibiting a strong bias toward topical overlap while ignoring instructional stance.<n>We thus introduce diagnostic word-ablation metrics to quantify this phenomenon and propose a data-centric solution.
  arXiv  Detail & Related papers  (2026-08-28T12:47:45Z)
• ReasonScaffold: A Scaffolded Reasoning-based Annotation Protocol for Human-AI Co-Annotation [2.5819252531158683]
  We introduce textbfReasonScaffold, a scaffolded reasoning annotation protocol that exposes LLM-generated explanations while withholding predicted labels.<n>We study how reasoning affects human annotation behavior in a controlled setting, rather than evaluating annotation accuracy.<n>Our results show that exposure to reasoning is associated with increased agreement, along with minimal revision, suggesting that reasoning helps resolve ambiguous cases without inducing widespread changes.
  arXiv  Detail & Related papers  (2026-03-22T07:14:27Z)
• Mastering Negation: Boosting Grounding Models via Grouped Opposition-Based Learning [87.15765427638195]
  We introduce D-Negation, a new dataset that provides objects annotated with both positive and negative semantic descriptions.<n>We propose a grouped opposition-based learning framework that learns negation-aware representations from limited samples.<n>By fine-tuning fewer than 10 percent of the model parameters, our approach achieves improvements of up to 4.4 mAP and 5.7 mAP on positive and negative semantic evaluations.
  arXiv  Detail & Related papers  (2026-03-13T03:21:48Z)
• From Disagreement to Understanding: The Case for Ambiguity Detection in NLI [3.5164356851161984]
  This position paper argues that annotation disagreement in Natural Language Inference (NLI) is not mere noise but often reflects meaningful variation.<n>We call for a shift toward ambiguity-aware NLI that first identifies ambiguous input pairs, classifies their types, and only then proceeds to inference.
  arXiv  Detail & Related papers  (2025-07-20T20:27:35Z)
• A Survey on Latent Reasoning [100.54120559169735]
  Large Language Models (LLMs) have demonstrated impressive reasoning capabilities.<n>CoT reasoning that verbalizes intermediate steps limits the model's expressive bandwidth.<n>Latent reasoning tackles this bottleneck by performing multi-step inference entirely in the model's continuous hidden state.
  arXiv  Detail & Related papers  (2025-07-08T17:29:07Z)
• A Closer Look at Bias and Chain-of-Thought Faithfulness of Large (Vision) Language Models [58.32070787537946]
  Chain-of-thought (CoT) reasoning enhances performance of large language models.<n>We present the first comprehensive study of CoT faithfulness in large vision-language models.
  arXiv  Detail & Related papers  (2025-05-29T18:55:05Z)
• Design Choices for Crowdsourcing Implicit Discourse Relations: Revealing the Biases Introduced by Task Design [23.632204469647526]
  We show that the task design can push annotators towards certain relations. We conclude that this type of bias should be taken into account when training and testing models.
  arXiv  Detail & Related papers  (2023-04-03T09:04:18Z)
• Keywords and Instances: A Hierarchical Contrastive Learning Framework Unifying Hybrid Granularities for Text Generation [60.62039705180484]
  We propose a hierarchical contrastive learning mechanism, which can unify hybrid granularities semantic meaning in the input text.<n> Experiments demonstrate that our model outperforms competitive baselines on paraphrasing, dialogue generation, and storytelling tasks.
  arXiv  Detail & Related papers  (2022-05-26T13:26:03Z)
• Dive into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression Recognition [59.52434325897716]
  We propose a solution, named DMUE, to address the problem of annotation ambiguity from two perspectives. For the former, an auxiliary multi-branch learning framework is introduced to better mine and describe the latent distribution in the label space. For the latter, the pairwise relationship of semantic feature between instances are fully exploited to estimate the ambiguity extent in the instance space.
  arXiv  Detail & Related papers  (2021-04-01T03:21:57Z)
• Embracing Uncertainty: Decoupling and De-bias for Robust Temporal Grounding [23.571580627202405]
  Temporal grounding aims to localize temporal boundaries within untrimmed videos by language queries. It faces the challenge of two types of inevitable human uncertainties: query uncertainty and label uncertainty. We propose a novel DeNet (Decoupling and De-bias) to embrace human uncertainty.
  arXiv  Detail & Related papers  (2021-03-31T07:00:56Z)
• Infusing Finetuning with Semantic Dependencies [62.37697048781823]
  We show that, unlike syntax, semantics is not brought to the surface by today's pretrained models. We then use convolutional graph encoders to explicitly incorporate semantic parses into task-specific finetuning.
  arXiv  Detail & Related papers  (2020-12-10T01:27:24Z)

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.