Using Knowledge Induction strategies: LLMs can do better in knowledge-driven dialogue tasks

This paper presents a first theoretical framework for a dialog strategy handling miscommunication in natural language Human-Robot Interaction (HRI). On the one hand the dialog strategy is deduced from findings about human-human communication patterns and coping strategies for miscommunication. On the other hand, relevant cognitive theories concerning human perception serve as a conceptual basis for the dialog strategy. The novel approach is firstly to combine these communication patterns with coping strategies and cognitive theories from human-human interaction (HHI) and secondly transfer them to HRI as a general dialog strategy for handling miscommunication. The presented approach is applicable to any task-oriented dialog. In a first step the conversational context is confined to route descriptions, given that asking for directions is an restricted but nevertheless challenging example for task-oriented dialog between humans and a robot.

When the Diagnosis Is Dual

This work presents a generic semi-automatic strategy to populate the domain ontology of an ontology-driven task-oriented dialogue system, with the aim of performing successful intent detection in the dialogue process, reusing already existing multilingual resources. This semi-automatic approach allows ontology engineers to exploit available resources so as to associate the potential situations in the use case to FrameNet frames and obtain the relevant lexical units associated to them in the target language, following lexical and semantic criteria, without linguistic expert knowledge. This strategy has been validated and evaluated in two use cases, from industrial scenarios, for interaction in Spanish with a guide robot and with a Computerized Maintenance Management System (CMMS). In both cases, this method has allowed the ontology engineer to instantiate the domain ontology with the intent-relevant information with quality data in a simple and low-resource-consuming manner.