Negation Beyond the Verbal Channel: Temporal Multimodal Correlates in Dialogue
Not on the map. Why?
Abstract (arXiv)
Negation is typically modeled through its linguistic realization, although spoken interaction is accompanied by tightly coordinated nonverbal behavior. We ask whether contexts centered on spoken negation cues contain measurable multimodal behavioral information: whether they can be distinguished from matched control contexts without lexical or acoustic input, where this information occurs in time, which modalities carry it, and whether it extends to the dialogue partner. We study 27 human-human interviews conducted in virtual reality, comprising temporally aligned gaze, facial, head, body, hand, and finger behavior and 964 annotated negation cues. Treating classification as a predictive probe, we compare 20 time-series models while excluding lexical and acoustic information, and then systematically vary temporal context, interactional source, modality availability, and event timing. Across grouped 10-fold cross-validation, the strongest probes reach up to .75 mean held-out AUROC from speaker-side behavior. Temporal analyses show that predictive information is concentrated around cue onset but remains detectable over a broader surrounding interval, while dialogue-partner behavior carries weaker predictive information with a comparatively diffuse temporal profile. Ablation and timing perturbations further show that facial features produce the largest modality-ablation effect and that the trained probe is sensitive to the temporal organization of the observed events.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 27 of its 66 references that are in the map. First posted on arXiv 14 Sep 2026.
Builds on
27 of 66 references.
References · 27
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,447
- 2019Multimodal Transformer for Unaligned Multimodal Language Sequencescited by 637
- 2023T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representationscited by 577
- 2023MotionGPT: Human Motion as a Foreign Languagecited by 521
- 2019InceptionTime: Finding AlexNet for Time Series Classificationcited by 386
- 2019Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot Flycited by 240
- 2020MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classificationcited by 186
- 2021VALSE: A Task-Independent Benchmark for Vision and Language Models Centered on Linguistic Phenomenacited by 134
- 2021HIVE-COTE 2.0: a new meta ensemble for time series classificationcited by 118
- 2017NegBio: a high-performance tool for negation and uncertainty detection in radiology reportscited by 79
- 2023Language models are not naysayers: An analysis of language models on negation benchmarkscited by 73
15 more
- 2025Vision-Language Models Do Not Understand Negationcited by 69
- 2024aeon: a Python toolkit for learning from time seriescited by 46
- 2022An Analysis of Negation in Natural Language Understanding Corporacited by 39
- 2019NegBERT: A Transfer Learning Approach for Negation Detection and Scope Resolutioncited by 36
- 2025MotionGPT3: Human Motion as a Second Modalitycited by 36
- 2024CrisperWhisper: Accurate Timestamps on Verbatim Speech Transcriptionscited by 33
- 2025Know "No" Better: A Data-Driven Approach for Enhancing Negation Awareness in CLIPcited by 26
- 2022Learn to Understand Negation in Video Retrievalcited by 16
- 2024How and where does CLIP process negation?cited by 16
- 2025Speaking Beyond Language: A Large-Scale Multimodal Dataset for Learning Nonverbal Cues from Video-Grounded Dialoguescited by 4
- 2026RocketPFN: Accurate Time Series Classification via In-Context Learningcited by 4
- 2023Generative Hierarchical Temporal Transformer for Hand Pose and Action Modelingcited by 3
- 2024Time series classification with random convolution kernels: pooling operators and input representations mattercited by 2
- 2026Learning to Detect Cross-Modal Negation: An Analysis of Latent Representations and an Attention-Based Solutioncited by 1
- 2026In-Context Time Series Classification with Random Convolutional Featurescited by 1
Built on by
Citing papers per year: 2026: 1.
1 paper.