Rapport-Driven Virtual Agent: Rapport Building Dialogue Strategy for Improving User Experience at First Meeting

• URL: http://arxiv.org/abs/2406.09839v1
• Date: Fri, 14 Jun 2024 08:47:15 GMT
• Title: Rapport-Driven Virtual Agent: Rapport Building Dialogue Strategy for Improving User Experience at First Meeting
• Authors: Muhammad Yeza Baihaqi, Angel García Contreras, Seiya Kawano, Koichiro Yoshino,
• Abstract summary: This study aims to establish human-agent rapport through small talk by using a rapport-building strategy. We implemented this strategy for the virtual agents based on dialogue strategies by prompting a large language model (LLM)
• Score: 3.059886686838972
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Rapport is known as a conversational aspect focusing on relationship building, which influences outcomes in collaborative tasks. This study aims to establish human-agent rapport through small talk by using a rapport-building strategy. We implemented this strategy for the virtual agents based on dialogue strategies by prompting a large language model (LLM). In particular, we utilized two dialogue strategies-predefined sequence and free-form-to guide the dialogue generation framework. We conducted analyses based on human evaluations, examining correlations between total turn, utterance characters, rapport score, and user experience variables: naturalness, satisfaction, interest, engagement, and usability. We investigated correlations between rapport score and naturalness, satisfaction, engagement, and conversation flow. Our experimental results also indicated that using free-form to prompt the rapport-building strategy performed the best in subjective scores.

Related papers

• Investigating Reinforcement Learning for Communication Strategies in a Task-Initiative Setting [8.680676599607123]
  We analyze the trade-offs between initial presentation and subsequent followup as a function of user clarification strategy. We find surprising advantages to coherence-based representations of dialogue strategy, which bring minimal data requirements, explainable choices, and strong audit capabilities.
  arXiv  Detail & Related papers  (2023-08-03T00:10:23Z)
• Dialogue Agents 101: A Beginner's Guide to Critical Ingredients for Designing Effective Conversational Systems [29.394466123216258]
  This study provides a comprehensive overview of the primary characteristics of a dialogue agent, their corresponding open-domain datasets, and the methods used to benchmark these datasets. We propose UNIT, a UNified dIalogue dataseT constructed from conversations of existing datasets for different dialogue tasks capturing the nuances for each of them.
  arXiv  Detail & Related papers  (2023-07-14T10:05:47Z)
• ChatGPT Evaluation on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations [52.26802326949116]
  We quantitatively evaluate the performance of ChatGPT, an interactive large language model, on inter-sentential relations. ChatGPT exhibits exceptional proficiency in detecting and reasoning about causal relations. It is capable of identifying the majority of discourse relations with existing explicit discourse connectives, but the implicit discourse relation remains a formidable challenge.
  arXiv  Detail & Related papers  (2023-04-28T13:14:36Z)
• Which Factors Predict the Chat Experience of a Natural Language Generation Dialogue Service? [0.0]
  We evaluate a conceptual model to predict the chat experience in a natural language generation dialog system. Users' favorability and coherence, sentiment, and similarity in the dialogue are positive predictors of users' chat experience. An adaptive dialog system might use collected data to infer factors in our model, predict the chat experience for users through these factors, and optimize it by adjusting prompts.
  arXiv  Detail & Related papers  (2023-04-21T07:29:07Z)
• FCC: Fusing Conversation History and Candidate Provenance for Contextual Response Ranking in Dialogue Systems [53.89014188309486]
  We present a flexible neural framework that can integrate contextual information from multiple channels. We evaluate our model on the MSDialog dataset widely used for evaluating conversational response ranking tasks.
  arXiv  Detail & Related papers  (2023-03-31T23:58:28Z)
• Modeling Non-Cooperative Dialogue: Theoretical and Empirical Insights [11.462075538526703]
  We investigate the ability of agents to identify non-cooperative interlocutors while completing a concurrent visual-dialogue task. We use the tools of learning theory to develop a theoretical model for identifying non-cooperative interlocutors and apply this theory to analyze different communication strategies.
  arXiv  Detail & Related papers  (2022-07-15T02:08:41Z)
• I like fish, especially dolphins: Addressing Contradictions in Dialogue Modeling [104.09033240889106]
  We introduce the DialoguE COntradiction DEtection task (DECODE) and a new conversational dataset containing both human-human and human-bot contradictory dialogues. We then compare a structured utterance-based approach of using pre-trained Transformer models for contradiction detection with the typical unstructured approach.
  arXiv  Detail & Related papers  (2020-12-24T18:47:49Z)
• Is this Dialogue Coherent? Learning from Dialogue Acts and Entities [82.44143808977209]
  We create the Switchboard Coherence (SWBD-Coh) corpus, a dataset of human-human spoken dialogues annotated with turn coherence ratings. Our statistical analysis of the corpus indicates how turn coherence perception is affected by patterns of distribution of entities. We find that models combining both DA and entity information yield the best performances both for response selection and turn coherence rating.
  arXiv  Detail & Related papers  (2020-06-17T21:02:40Z)
• Learning Goal-oriented Dialogue Policy with Opposite Agent Awareness [116.804536884437]
  We propose an opposite behavior aware framework for policy learning in goal-oriented dialogues. We estimate the opposite agent's policy from its behavior and use this estimation to improve the target agent by regarding it as part of the target policy.
  arXiv  Detail & Related papers  (2020-04-21T03:13:44Z)
• Dialogue-Based Relation Extraction [53.2896545819799]
  We present the first human-annotated dialogue-based relation extraction (RE) dataset DialogRE. We argue that speaker-related information plays a critical role in the proposed task, based on an analysis of similarities and differences between dialogue-based and traditional RE tasks. Experimental results demonstrate that a speaker-aware extension on the best-performing model leads to gains in both the standard and conversational evaluation settings.
  arXiv  Detail & Related papers  (2020-04-17T03:51:57Z)

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.