Negation Beyond the Verbal Channel: Temporal Multimodal Correlates in Dialogue

arXiv:2609.16396 · cs.CL · Submitted 2026-09-14 · Read on arXiv

cs.CL

Submitted: 2026-09-14

Updated: 2026-09-14

Comments: To be submitted to the October 2026 cycle of ACL Rolling Review (ARR)

Code: https://github.com/vrneg/vrneg01

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Transformer — 18× in this paper · explained in 2 episodes
• Ablation — 15× in this paper · explained in 1 episode
• Fine-Tuning — 6× in this paper · explained in 5 episodes
• Adaptation — 5× in this paper · explained in 1 episode
• Benchmark — 5× in this paper · explained in 2 episodes
• Robustness — 5× in this paper · explained in 5 episodes
• Actor — 4× in this paper · explained in 3 episodes
• Adam — 4× in this paper · explained in 2 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Accuracy — 2× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Embeddings — 2× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Pragmatics — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes

Sources

• An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
• In-Context Time Series Classification with Random Convolutional Features
• RocketPFN: Accurate Time Series Classification via In-Context Learning
• Time series classification with random convolution kernels: pooling operators and input representations matter
• Castor: Competing shapelets for fast and accurate time series classification

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