Troubles and Failures in Interactional Language. Towards a Linguistically Informed Taxonomy

• URL: http://arxiv.org/abs/2311.07217v1
• Date: Mon, 13 Nov 2023 10:24:51 GMT
• Title: Troubles and Failures in Interactional Language. Towards a Linguistically Informed Taxonomy
• Authors: Martina Wiltschko
• Abstract summary: The goal of this talk is to introduce a systematic research agenda which aims to understand the nature of interaction between humans and artificial conversational agents (CA) Specifically, we shall take on linguistically defined variables that are known to influence the flow of conversations among humans (henceforth human-human interaction, HHI)
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The goal of this talk is to introduce a systematic research agenda which aims to understand the nature of interaction between humans and artificial conversational agents (CA) (henceforth humanmachine interaction, HMI). Specifically, we shall take an explicit linguistic perspective focusing on linguistically defined variables that are known to influence the flow of conversations among humans (henceforth human-human interaction, HHI).

Related papers

• Toward Cultural Interpretability: A Linguistic Anthropological Framework for Describing and Evaluating Large Language Models (LLMs) [13.71024600466761]
  This article proposes a new integration of linguistic anthropology and machine learning (ML) We show the theoretical feasibility of a new, conjoint field of inquiry, cultural interpretability (CI) CI emphasizes how the dynamic relationship between language and culture makes contextually sensitive, open-ended conversation possible.
  arXiv  Detail & Related papers  (2024-11-07T22:01:50Z)
• Visual-Geometric Collaborative Guidance for Affordance Learning [63.038406948791454]
  We propose a visual-geometric collaborative guided affordance learning network that incorporates visual and geometric cues. Our method outperforms the representative models regarding objective metrics and visual quality.
  arXiv  Detail & Related papers  (2024-10-15T07:35:51Z)
• ChatGPT Role-play Dataset: Analysis of User Motives and Model Naturalness [4.564433526993029]
  We study how ChatGPT behaves during conversations in different settings by analyzing its interactions in both a normal way and a role-play setting. Our study highlights the diversity of user motives when interacting with ChatGPT and variable AI naturalness, showing not only the nuanced dynamics of natural conversations between humans and AI, but also providing new avenues for improving the effectiveness of human-AI communication.
  arXiv  Detail & Related papers  (2024-03-26T22:01:13Z)
• THOR: Text to Human-Object Interaction Diffusion via Relation Intervention [51.02435289160616]
  We propose a novel Text-guided Human-Object Interaction diffusion model with Relation Intervention (THOR) In each diffusion step, we initiate text-guided human and object motion and then leverage human-object relations to intervene in object motion. We construct Text-BEHAVE, a Text2HOI dataset that seamlessly integrates textual descriptions with the currently largest publicly available 3D HOI dataset.
  arXiv  Detail & Related papers  (2024-03-17T13:17:25Z)
• Are Human Conversations Special? A Large Language Model Perspective [8.623471682333964]
  This study analyzes changes in the attention mechanisms of large language models (LLMs) when used to understand natural conversations between humans (human-human) Our findings reveal that while language models exhibit domain-specific attention behaviors, there is a significant gap in their ability to specialize in human conversations.
  arXiv  Detail & Related papers  (2024-03-08T04:44:25Z)
• A Linguistic Comparison between Human and ChatGPT-Generated Conversations [9.022590646680095]
  The research employs Linguistic Inquiry and Word Count analysis, comparing ChatGPT-generated conversations with human conversations. Results show greater variability and authenticity in human dialogues, but ChatGPT excels in categories such as social processes, analytical style, cognition, attentional focus, and positive emotional tone.
  arXiv  Detail & Related papers  (2024-01-29T21:43:27Z)
• HODN: Disentangling Human-Object Feature for HOI Detection [51.48164941412871]
  We propose a Human and Object Disentangling Network (HODN) to model the Human-Object Interaction (HOI) relationships explicitly. Considering that human features are more contributive to interaction, we propose a Human-Guide Linking method to make sure the interaction decoder focuses on the human-centric regions. Our proposed method achieves competitive performance on both the V-COCO and the HICO-Det Linking datasets.
  arXiv  Detail & Related papers  (2023-08-20T04:12:50Z)
• Interactive Natural Language Processing [67.87925315773924]
  Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP. This paper offers a comprehensive survey of iNLP, starting by proposing a unified definition and framework of the concept.
  arXiv  Detail & Related papers  (2023-05-22T17:18:29Z)
• Mediators: Conversational Agents Explaining NLP Model Behavior [2.7878644615660457]
  The human-centric explainable artificial intelligence (HCXAI) community has raised the need for framing the explanation process as a conversation between human and machine. We establish desiderata for Mediators, text-based conversational agents capable of explaining the behavior of neural models interactively using natural language.
  arXiv  Detail & Related papers  (2022-06-13T10:31:18Z)
• 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)
• Learning Human-Object Interaction Detection using Interaction Points [140.0200950601552]
  We propose a novel fully-convolutional approach that directly detects the interactions between human-object pairs. Our network predicts interaction points, which directly localize and classify the inter-action. Experiments are performed on two popular benchmarks: V-COCO and HICO-DET.
  arXiv  Detail & Related papers  (2020-03-31T08:42:06Z)

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.