Knowledge-Grounded Dialogue Flow Management for Social Robots and Conversational Agents
This work addresses the challenge of creating more coherent and engaging conversations for social robots and agents, though it appears incremental as it builds on existing ontology and dialogue management methods.
The paper tackles the problem of managing dialogue flow in social robots and conversational agents by proposing a knowledge-based system that uses an ontology to select conversation topics and maintain coherence. It reports tests with 100 participants, showing that their system outperformed keyword-based, random, and Replika agents in subjective coherence and interface assessments.
The article proposes a system for knowledge-based conversation designed for Social Robots and other conversational agents. The proposed system relies on an Ontology for the description of all concepts that may be relevant conversation topics, as well as their mutual relationships. The article focuses on the algorithm for Dialogue Management that selects the most appropriate conversation topic depending on the user's input. Moreover, it discusses strategies to ensure a conversation flow that captures, as more coherently as possible, the user's intention to drive the conversation in specific directions while avoiding purely reactive responses to what the user says. To measure the quality of the conversation, the article reports the tests performed with 100 recruited participants, comparing five conversational agents: (i) an agent addressing dialogue flow management based only on the detection of keywords in the speech, (ii) an agent based both on the detection of keywords and the Content Classification feature of Google Cloud Natural Language, (iii) an agent that picks conversation topics randomly, (iv) a human pretending to be a chatbot, and (v) one of the most famous chatbots worldwide: Replika. The subjective perception of the participants is measured both with the SASSI (Subjective Assessment of Speech System Interfaces) tool, as well as with a custom survey for measuring the subjective perception of coherence.