Grounded Misunderstandings in Asymmetric Dialogue: A Perspectivist Annotation Scheme for MapTask

• URL: http://arxiv.org/abs/2511.03718v1
• Date: Wed, 05 Nov 2025 18:52:28 GMT
• Title: Grounded Misunderstandings in Asymmetric Dialogue: A Perspectivist Annotation Scheme for MapTask
• Abstract summary: Collaborative dialogue relies on participants incrementally establishing common ground, yet in asymmetric settings they may believe they agree while referring to different entities.<n>We introduce a perspectivist annotation scheme for the HCRC MapTask corpus that separately captures speaker and addressee grounded interpretations for each reference expression.<n>We obtain 13k annotated reference expressions with reliability estimates and analyze the resulting understanding states.
• Score: 8.925029287648345
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Collaborative dialogue relies on participants incrementally establishing common ground, yet in asymmetric settings they may believe they agree while referring to different entities. We introduce a perspectivist annotation scheme for the HCRC MapTask corpus (Anderson et al., 1991) that separately captures speaker and addressee grounded interpretations for each reference expression, enabling us to trace how understanding emerges, diverges, and repairs over time. Using a scheme-constrained LLM annotation pipeline, we obtain 13k annotated reference expressions with reliability estimates and analyze the resulting understanding states. The results show that full misunderstandings are rare once lexical variants are unified, but multiplicity discrepancies systematically induce divergences, revealing how apparent grounding can mask referential misalignment. Our framework provides both a resource and an analytic lens for studying grounded misunderstanding and for evaluating (V)LLMs' capacity to model perspective-dependent grounding in collaborative dialogue.

Related papers

• Seeing Is Not Sharing: Some Vision-Language Models Overestimate Common Ground in Asymmetric Dialogue [8.925029287648345]
  In collaborative dialogue, shared perception does not guarantee shared interpretation. Mutual understanding must be established through interaction.<n>We evaluate vision-language models (VLMs) under systematically controlled manipulations of dialogue context and map-information access.<n>Our results show that providing authentic map images improves overall performance but shifts models toward over-predicting alignment.
  arXiv  Detail & Related papers  (2026-06-30T14:22:48Z)
• From Propositional to Perceptual Asymmetry: Extending Frictive Policy Optimization to Asymmetric Partial Information Dialogue [10.142691415785281]
  Frictive Policy Optimization treats friction in collaborative dialogue as a signal essential to common-ground construction.<n>We extend FPO to perceptual asymmetry, where participants hold asymmetric partial information.<n>We find that FPO's friction functional is empirically valid only when evaluated from within each participant's information horizon.
  arXiv  Detail & Related papers  (2026-06-29T23:16:19Z)
• SKG-Eval: Stateful Evaluation of Multi-Turn Dialogue via Incremental Semantic Knowledge Graphs [0.0]
  SKG-Eval is a quasi-deterministic and interpretable framework that models dialogue as an evolving Semantic Knowledge Graph.<n>We show that SKG-Eval achieves higher correlation with human judgments and substantially improves detection of long-range inconsistencies in extended conversations.
  arXiv  Detail & Related papers  (2026-05-15T21:39:48Z)
• When Contextual Inference Fails: Cancelability in Interactive Instruction Following [51.2195840589474]
  We introduce Build What I Mean, an interactive benchmark for contextual meaning construction.<n>In BWIM, models must resolve ambiguity by either performing a contextual inference or requesting clarification at a small communication cost.<n>We observe suboptimal strategies, such as partner-blind over-clarification and question-averse guessing under uncertainty.
  arXiv  Detail & Related papers  (2026-03-20T14:46:59Z)
• Graph-theoretic Agreement Framework for Multi-agent LLM Systems [0.228438857884398]
  This paper establishes a rigorous graph-theoretic framework for analyzing consensus in signed, directed interaction networks.<n>We show how unbalanced critique cycles produce logical frustration and persistent reasoning oscillations.<n>We prove that rank-one spectral edge perturbations deterministically break symmetry by shifting eigenvalues into the stable left-half plane.
  arXiv  Detail & Related papers  (2026-02-23T02:30:48Z)
• Beyond Consensus: Perspectivist Modeling and Evaluation of Annotator Disagreement in NLP [25.097081181685613]
  Annotator disagreement is widespread in NLP, particularly for subjective and ambiguous tasks such as toxicity detection and stance analysis.<n>We first present a domain-agnostic taxonomy of the sources of disagreement spanning data, task, and annotator factors.<n>We then synthesize modeling approaches using a common framework defined by prediction targets and pooling structure.
  arXiv  Detail & Related papers  (2026-01-14T01:26:29Z)
• Towards Temporal Knowledge-Base Creation for Fine-Grained Opinion Analysis with Language Models [3.954853544590893]
  We propose a scalable method for constructing a temporal opinion knowledge base with large language models (LLMs) as automated annotators.<n>We define three data models grounded in sentiment and opinion mining literature, serving as schemas for structured representation.<n>The resulting knowledge base encapsulates time-aligned, structured opinions and is compatible with applications in Retrieval-Augmented Generation (RAG), temporal question answering, and timeline summarisation.
  arXiv  Detail & Related papers  (2025-09-02T14:24:25Z)
• 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)
• Imputation-free and Alignment-free: Incomplete Multi-view Clustering Driven by Consensus Semantic Learning [65.75756724642932]
  In incomplete multi-view clustering, missing data induce prototype shifts within views and semantic inconsistencies across views.<n>We propose an IMVC framework, imputation- and alignment-free for consensus semantics learning (FreeCSL)<n>FreeCSL achieves more confident and robust assignments on IMVC task, compared to state-of-the-art competitors.
  arXiv  Detail & Related papers  (2025-05-16T12:37:10Z)
• Estimating Commonsense Plausibility through Semantic Shifts [66.06254418551737]
  We propose ComPaSS, a novel discriminative framework that quantifies commonsense plausibility by measuring semantic shifts.<n> Evaluations on two types of fine-grained commonsense plausibility estimation tasks show that ComPaSS consistently outperforms baselines.
  arXiv  Detail & Related papers  (2025-02-19T06:31:06Z)
• Is LLM an Overconfident Judge? Unveiling the Capabilities of LLMs in Detecting Offensive Language with Annotation Disagreement [22.992484902761994]
  This study systematically evaluates the performance of multiple Large Language Models (LLMs) in detecting offensive language.<n>We analyze binary classification accuracy, examine the relationship between model confidence and human disagreement, and explore how disagreement samples influence model decision-making.
  arXiv  Detail & Related papers  (2025-02-10T07:14:26Z)
• Localizing Factual Inconsistencies in Attributable Text Generation [74.11403803488643]
  We introduce QASemConsistency, a new formalism for localizing factual inconsistencies in attributable text generation.<n>We show that QASemConsistency yields factual consistency scores that correlate well with human judgments.
  arXiv  Detail & Related papers  (2024-10-09T22:53:48Z)
• Counterfactuals of Counterfactuals: a back-translation-inspired approach to analyse counterfactual editors [3.4253416336476246]
  We focus on the analysis of counterfactual, contrastive explanations. We propose a new back translation-inspired evaluation methodology. We show that by iteratively feeding the counterfactual to the explainer we can obtain valuable insights into the behaviour of both the predictor and the explainer models.
  arXiv  Detail & Related papers  (2023-05-26T16:04:28Z)
• SWING: Balancing Coverage and Faithfulness for Dialogue Summarization [67.76393867114923]
  We propose to utilize natural language inference (NLI) models to improve coverage while avoiding factual inconsistencies. We use NLI to compute fine-grained training signals to encourage the model to generate content in the reference summaries that have not been covered. Experiments on the DialogSum and SAMSum datasets confirm the effectiveness of the proposed approach.
  arXiv  Detail & Related papers  (2023-01-25T09:33:11Z)
• 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)
• A Correspondence Variational Autoencoder for Unsupervised Acoustic Word Embeddings [50.524054820564395]
  We propose a new unsupervised model for mapping a variable-duration speech segment to a fixed-dimensional representation. The resulting acoustic word embeddings can form the basis of search, discovery, and indexing systems for low- and zero-resource languages.
  arXiv  Detail & Related papers  (2020-12-03T19:24:42Z)

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.