Enriching Interactive Explanations with Fuzzy Temporal Constraint Networks

Humans often use expressions with vague terms which play a fundamental role for effective communication. These expressions are successfully modeled with fuzzy technology, but they are not usually integrated yet with Natural Language Processing models and techniques. Large-scale pre-trained language models yield excellent results in many language tasks, but they have some drawbacks such as their lack of transparency and thorough temporal reasoning capabilities. Therefore, the use of such models may provoke inconsistent or incorrect dialogues in the context of conversational agents which were aimed at providing users of intelligent systems with interactive explanations. In this paper, we propose a model for fuzzy temporal reasoning to overcome some inconsistencies detected in pre-trained language models in a specific application domain of a conversational agent carefully designed for providing users with explanations which are endowed with a good balance between naturalness and fidelity. More precisely, starting from a knowledge graph that provides an intuitive representation of the entities and relations in the application domain, we describe how to map the temporal information onto a fuzzy temporal constraint network. This formalism allows to represent imprecise temporal information and provides mechanisms for checking consistency in conversations. In addition, as a proof of concept, we have developed TimeVersa, a conversational agent which integrates the proposed model into an application domain (i.e., a virtual assistant for tourists) that requires handling imprecise temporal constraints. We illustrate in a use case how the agent can identify temporal inconsistencies and answer queries related to temporal information properly. Results after a user study report that users' perception of consistency is significantly higher in a conversation with TimeVersa than in a similar conversation using the well-known GPT-3 Large Language Model, when vague temporal information is involved. The proposed approach is a step forward for developing conversational agents operating in application domains that require temporal reasoning under uncertainty.