A Taxonomy of Conceptual Alignment in Human-Robot Dialogue
- URL: http://arxiv.org/abs/2606.22360v1
- Date: Sun, 21 Jun 2026 07:03:54 GMT
- Title: A Taxonomy of Conceptual Alignment in Human-Robot Dialogue
- Abstract summary: This paper argues for a design-centric understanding of conceptual alignment as a bidirectional and co-constructive process.<n>We introduce a taxonomy that characterizes conceptual alignment dialogues along what triggers its initiation and what level(s) of conceptual understanding it concerns.
- Score: 11.160627282474694
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Successful conversations require speakers to align on the meaning of concepts, a challenging but crucial task for human-robot interaction. Understanding the process of establishing such alignment is hindered by competing interpretations of the term and isolated, unidirectional investigations of its design space. This paper argues for a design-centric understanding of conceptual alignment as a bidirectional and co-constructive process. We introduce a taxonomy that characterizes conceptual alignment dialogues along what triggers its initiation and what level(s) of conceptual understanding it concerns. We further present a dialogue act schema as an operational tool that captures the interactional moves through which alignment is achieved. Together, these contributions provide a structured foundation for analyzing, comparing, and designing conceptual alignment in human-robot interaction.
Related papers
- Towards a Foundational Ontology for Identifying and Resolving Contradictions in Dialogue-based Human-Robot Interactions [0.0]
There is still lack of a formal computational framework to represent and define contradictions in HRI and human-agent interaction domains.<n>This research project aims to capture, represent, and evaluate the notion of dialogue-based interaction and related contradictions in an ontology.
arXiv Detail & Related papers (2026-09-02T09:35:02Z) - Socially Grounded Agentic AI: Coordinating Plural Perspectives through Social Theory [4.253029474101735]
We show how perspectives can be understood as structured by roles, shaped through interaction, and distributed across fields of power and expertise.<n>For agentic systems, this requires aligning not only final outputs, but also the role activations, deliberative traces, aggregation rules, and feedback loops through which those outputs are produced.
arXiv Detail & Related papers (2026-08-04T16:37:09Z) - The Silent Thought: Modeling Internal Cognition in Full-Duplex Spoken Dialogue Models via Latent Reasoning [76.96796481909581]
Internal cognitive processing is instrumental in formulating high-quality responses.<n>We propose a novel method named FLAIR that conducts latent thinking simultaneously with speech perception.<n>Our approach aligns seamlessly with spoken dialogue systems.
arXiv Detail & Related papers (2026-03-18T15:30:29Z) - From Perception to Cognition: A Survey of Vision-Language Interactive Reasoning in Multimodal Large Language Models [66.36007274540113]
Multimodal Large Language Models (MLLMs) strive to achieve a profound, human-like understanding of and interaction with the physical world.<n>They often exhibit a shallow and incoherent integration when acquiring information (Perception) and conducting reasoning (Cognition)<n>This survey introduces a novel and unified analytical framework: From Perception to Cognition"
arXiv Detail & Related papers (2025-09-29T18:25:40Z) - A Metasemantic-Metapragmatic Framework for Taxonomizing Multimodal Communicative Alignment [0.0]
This paper presents a metasemantic-metapragmatic taxonomy for grounding and conceptualizing human-like multimodal communicative alignment.<n>I introduce the concept of indexical contextualization and propose the principle of "contextualization directionality"<n>The framework's broader implications for intentionality, identity, affect, and ethics in within-modal and cross-modal human-machine alignment are also discussed.
arXiv Detail & Related papers (2025-01-02T21:00:19Z) - Human-Robot Dialogue Annotation for Multi-Modal Common Ground [4.665414514091581]
We describe the development of symbolic representations annotated on human-robot dialogue data to make dimensions of meaning accessible to autonomous systems participating in collaborative, natural language dialogue, and to enable common ground with human partners.
A particular challenge for establishing common ground arises in remote dialogue, where a human and robot are engaged in a joint navigation and exploration task of an unfamiliar environment, but where the robot cannot immediately share high quality visual information due to limited communication constraints.
Within this paradigm, we capture propositional semantics and the illocutionary force of a single utterance within the dialogue through our Dialogue-AMR annotation, an augmentation of Abstract Meaning Representation
arXiv Detail & Related papers (2024-11-19T19:33:54Z) - MindDial: Belief Dynamics Tracking with Theory-of-Mind Modeling for Situated Neural Dialogue Generation [62.44907105496227]
MindDial is a novel conversational framework that can generate situated free-form responses with theory-of-mind modeling.
We introduce an explicit mind module that can track the speaker's belief and the speaker's prediction of the listener's belief.
Our framework is applied to both prompting and fine-tuning-based models, and is evaluated across scenarios involving both common ground alignment and negotiation.
arXiv Detail & Related papers (2023-06-27T07:24:32Z) - Dialectical Reconciliation via Structured Argumentative Dialogues [14.584998154271512]
Our framework enables dialectical reconciliation to address knowledge discrepancies between an explainer (AI agent) and an explainee (human user)
Our findings suggest that our framework offers a promising direction for fostering effective human-AI interactions in domains where explainability is important.
arXiv Detail & Related papers (2023-06-26T13:39:36Z) - Revisiting Conversation Discourse for Dialogue Disentanglement [88.3386821205896]
We propose enhancing dialogue disentanglement by taking full advantage of the dialogue discourse characteristics.
We develop a structure-aware framework to integrate the rich structural features for better modeling the conversational semantic context.
Our work has great potential to facilitate broader multi-party multi-thread dialogue applications.
arXiv Detail & Related papers (2023-06-06T19:17:47Z) - Structured Attention for Unsupervised Dialogue Structure Induction [110.12561786644122]
We propose to incorporate structured attention layers into a Variational Recurrent Neural Network (VRNN) model with discrete latent states to learn dialogue structure in an unsupervised fashion.
Compared to a vanilla VRNN, structured attention enables a model to focus on different parts of the source sentence embeddings while enforcing a structural inductive bias.
arXiv Detail & Related papers (2020-09-17T23:07:03Z) - Discourse Coherence, Reference Grounding and Goal Oriented Dialogue [15.766916122461922]
We argue for a new approach to realizing mixed-initiative human--computer referential communication.
We describe a simple dialogue system in a referential communication domain that accumulates constraints across discourse, interprets them using a learned probabilistic model, and plans clarification using reinforcement learning.
arXiv Detail & Related papers (2020-07-08T20:53:14Z) - You Impress Me: Dialogue Generation via Mutual Persona Perception [62.89449096369027]
The research in cognitive science suggests that understanding is an essential signal for a high-quality chit-chat conversation.
Motivated by this, we propose P2 Bot, a transmitter-receiver based framework with the aim of explicitly modeling understanding.
arXiv Detail & Related papers (2020-04-11T12:51:07Z)
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.