ERICA: An Empathetic Android Companion for Covid-19 Quarantine

• URL: http://arxiv.org/abs/2106.02325v1
• Date: Fri, 4 Jun 2021 08:14:43 GMT
• Title: ERICA: An Empathetic Android Companion for Covid-19 Quarantine
• Authors: Etsuko Ishii, Genta Indra Winata, Samuel Cahyawijaya, Divesh Lala, Tatsuya Kawahara, Pascale Fung
• Abstract summary: We introduce an end-to-end dialogue system which aims to ease the isolation of people under self-quarantine. We conduct a control simulation experiment to assess the effects of the user interface, a web-based virtual agent called Nora vs. the android ERICA via a video call.
• Score: 63.79997830430368
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Over the past year, research in various domains, including Natural Language Processing (NLP), has been accelerated to fight against the COVID-19 pandemic, yet such research has just started on dialogue systems. In this paper, we introduce an end-to-end dialogue system which aims to ease the isolation of people under self-quarantine. We conduct a control simulation experiment to assess the effects of the user interface, a web-based virtual agent called Nora vs. the android ERICA via a video call. The experimental results show that the android offers a more valuable user experience by giving the impression of being more empathetic and engaging in the conversation due to its nonverbal information, such as facial expressions and body gestures.

Related papers

• Vocal Sandbox: Continual Learning and Adaptation for Situated Human-Robot Collaboration [64.6107798750142]
  Vocal Sandbox is a framework for enabling seamless human-robot collaboration in situated environments. We design lightweight and interpretable learning algorithms that allow users to build an understanding and co-adapt to a robot's capabilities in real-time. We evaluate Vocal Sandbox in two settings: collaborative gift bag assembly and LEGO stop-motion animation.
  arXiv  Detail & Related papers  (2024-11-04T20:44:40Z)
• Self-Directed Turing Test for Large Language Models [56.64615470513102]
  The Turing test examines whether AIs can exhibit human-like behaviour in natural language conversations. Traditional Turing tests adopt a rigid dialogue format where each participant sends only one message each time. This paper proposes the Self-Directed Turing Test, which extends the original test with a burst dialogue format.
  arXiv  Detail & Related papers  (2024-08-19T09:57:28Z)
• Toward a Dialogue System Using a Large Language Model to Recognize User Emotions with a Camera [0.0]
  Methods for AI agents to recognize emotions from the user's facial expressions have not been studied. We examined whether or not LLM-based AI agents can interact with users according to their emotional states by capturing the user in dialogue with a camera. Results confirmed that AI agents can have conversations according to the emotional state for emotional states with relatively high scores, such as Happy and Angry.
  arXiv  Detail & Related papers  (2024-08-15T07:03:00Z)
• Self-Emotion Blended Dialogue Generation in Social Simulation Agents [18.781929161272853]
  This study explores how self-emotion affects the agents' behaviors in dialogue strategies and decision-making within a large language model (LLM)-driven simulation framework. The results show that incorporating self-emotion helps agents exhibit more human-like dialogue strategies. In a virtual simulation environment where agents have discussions on multiple topics, we show that self-emotion of agents can significantly influence the decision-making process of the agents.
  arXiv  Detail & Related papers  (2024-08-03T02:11:48Z)
• Personality-affected Emotion Generation in Dialog Systems [67.40609683389947]
  We propose a new task, Personality-affected Emotion Generation, to generate emotion based on the personality given to the dialog system. We analyze the challenges in this task, i.e., (1) heterogeneously integrating personality and emotional factors and (2) extracting multi-granularity emotional information in the dialog context. Results suggest that by adopting our method, the emotion generation performance is improved by 13% in macro-F1 and 5% in weighted-F1 from the BERT-base model.
  arXiv  Detail & Related papers  (2024-04-03T08:48:50Z)
• Towards Embedding Dynamic Personas in Interactive Robots: Masquerading Animated Social Kinematics (MASK) [10.351714893090964]
  This paper presents the design and development of an innovative interactive robotic system to enhance audience engagement using character-like personas. Built upon the foundations of persona-driven dialog agents, this work extends the agent's application to the physical realm, employing robots to provide a more captivating and interactive experience.
  arXiv  Detail & Related papers  (2024-03-15T06:22:32Z)
• Does Collaborative Human-LM Dialogue Generation Help Information Extraction from Human Dialogues? [55.28340832822234]
  Problem-solving human dialogues in real applications can be much more complex than existing Wizard-of-Oz collections. We introduce a human-in-the-loop dialogue generation framework capable of synthesizing realistic dialogues.
  arXiv  Detail & Related papers  (2023-07-13T20:02:50Z)
• CDialog: A Multi-turn Covid-19 Conversation Dataset for Entity-Aware Dialog Generation [18.047064216849204]
  We release a high-quality multi-turn Medical Dialog dataset relating to Covid-19 disease named CDialog. We propose a novel neural medical dialog system based on the CDialog dataset to advance future research on developing automated medical dialog systems.
  arXiv  Detail & Related papers  (2022-11-16T11:07:34Z)
• The Effects of Interactive AI Design on User Behavior: An Eye-tracking Study of Fact-checking COVID-19 Claims [12.00747200817161]
  We conducted a lab-based eye-tracking study to investigate how the interactivity of an AI-powered fact-checking system affects user interactions. We found that the presence of interactively manipulating the AI system's prediction parameters affected users' dwell times, and eye-fixations on AOIs, but not mental workload.
  arXiv  Detail & Related papers  (2022-02-17T21:08:57Z)
• Towards Persona-Based Empathetic Conversational Models [58.65492299237112]
  Empathetic conversational models have been shown to improve user satisfaction and task outcomes in numerous domains. In Psychology, persona has been shown to be highly correlated to personality, which in turn influences empathy. We propose a new task towards persona-based empathetic conversations and present the first empirical study on the impact of persona on empathetic responding.
  arXiv  Detail & Related papers  (2020-04-26T08:51:01Z)

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.