Investigating Multimodal Informativity under Different Partner Visibility Conditions in Video-Mediated Dialogue

• URL: http://arxiv.org/abs/2608.08915v1
• Date: Sun, 09 Aug 2026 21:08:22 GMT
• Title: Investigating Multimodal Informativity under Different Partner Visibility Conditions in Video-Mediated Dialogue
• Abstract summary: We study how much referential information gestures and their combination with speech carry in multimodal dialogue.<n>We build models that identify the intended referent in a video-mediated referential communication game.
• Score: 4.916688229811061
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Situated language use is multimodal and embodied. For example, gestures can carry information that is absent or underspecified in the speech signal, yet dialogue models typically rely on transcripts alone. We study how much referential information gestures and their combination with speech carry in multimodal dialogue under different partner visibility conditions. % We build models that identify the intended referent in a video-mediated referential communication game based on either the speech transcript, the skeletal representation of gesture, or both modalities. Our results show that gesture alone is predictive of the intended referent and that multimodal fusion is most beneficial when the transcript-based model is uncertain. Training-only alignment of learned representations with the referent image further improves the fusion model performance. % In a comparison with human interaction data, we further see pragmatic effects of interlocutor visibility on gesture production and informativeness as well as an entrainment effect in speech and multimodal, but not gesture, performance across rounds of repeated interaction. We thus make contributions to the technical modelling of multimodal information in human dialogue and the analysis of human interaction data via trained model representations.

Related papers

• MT-PingEval: Evaluating Multi-Turn Collaboration with Private Information Games [70.37904949359938]
  We evaluate language models in multi-turn interactions using a suite of collaborative games that require effective communication about private information.<n>We find that language models are unable to use interactive collaboration to improve over the non-interactive baseline scenario.<n>We analyze the linguistic features of these dialogues, assessing the roles of sycophancy, information density, and discourse coherence.
  arXiv  Detail & Related papers  (2026-02-27T17:13:20Z)
• I see what you mean: Co-Speech Gestures for Reference Resolution in Multimodal Dialogue [5.0332064683666005]
  We introduce a multimodal reference resolution task centred on representational gestures.<n>We simultaneously tackle the challenge of learning robust gesture embeddings.<n>Our findings highlight the complementary roles of gesture and speech in reference resolution, offering a step towards more naturalistic models of human-machine interaction.
  arXiv  Detail & Related papers  (2025-02-27T17:28:12Z)
• Hierarchical Banzhaf Interaction for General Video-Language Representation Learning [60.44337740854767]
  Multimodal representation learning plays an important role in the artificial intelligence domain.<n>We introduce a new approach that models video-text as game players using multivariate cooperative game theory.<n>We extend our original structure into a flexible encoder-decoder framework, enabling the model to adapt to various downstream tasks.
  arXiv  Detail & Related papers  (2024-12-30T14:09:15Z)
• DER-GCN: Dialogue and Event Relation-Aware Graph Convolutional Neural Network for Multimodal Dialogue Emotion Recognition [14.639340916340801]
  We propose a novel Dialogue and Event Relation-Aware Graph Convolutional Neural Network for Multimodal Emotion Recognition (DER-GCN) method. It models dialogue relations between speakers and captures latent event relations information. We conduct extensive experiments on the IEMOCAP and MELD benchmark datasets, which verify the effectiveness of the DER-GCN model.
  arXiv  Detail & Related papers  (2023-12-17T01:49:40Z)
• Pre-training Multi-party Dialogue Models with Latent Discourse Inference [85.9683181507206]
  We pre-train a model that understands the discourse structure of multi-party dialogues, namely, to whom each utterance is replying. To fully utilize the unlabeled data, we propose to treat the discourse structures as latent variables, then jointly infer them and pre-train the discourse-aware model.
  arXiv  Detail & Related papers  (2023-05-24T14:06:27Z)
• Collaborative Reasoning on Multi-Modal Semantic Graphs for Video-Grounded Dialogue Generation [53.87485260058957]
  We study video-grounded dialogue generation, where a response is generated based on the dialogue context and the associated video. The primary challenges of this task lie in (1) the difficulty of integrating video data into pre-trained language models (PLMs) We propose a multi-agent reinforcement learning method to collaboratively perform reasoning on different modalities.
  arXiv  Detail & Related papers  (2022-10-22T14:45:29Z)
• Modeling Coreference Relations in Visual Dialog [18.926582410644375]
  The occurrences of coreference relations in the dialog makes it a more challenging task than visual question-answering. We propose two soft constraints that can improve the model's ability of resolving coreferences in dialog in an unsupervised way.
  arXiv  Detail & Related papers  (2022-03-06T15:22:24Z)
• Neuro-Symbolic Representations for Video Captioning: A Case for Leveraging Inductive Biases for Vision and Language [148.0843278195794]
  We propose a new model architecture for learning multi-modal neuro-symbolic representations for video captioning. Our approach uses a dictionary learning-based method of learning relations between videos and their paired text descriptions.
  arXiv  Detail & Related papers  (2020-11-18T20:21:19Z)
• Filling the Gap of Utterance-aware and Speaker-aware Representation for Multi-turn Dialogue [76.88174667929665]
  A multi-turn dialogue is composed of multiple utterances from two or more different speaker roles. In the existing retrieval-based multi-turn dialogue modeling, the pre-trained language models (PrLMs) as encoder represent the dialogues coarsely. We propose a novel model to fill such a gap by modeling the effective utterance-aware and speaker-aware representations entailed in a dialogue history.
  arXiv  Detail & Related papers  (2020-09-14T15:07:19Z)

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.