RASA Conversational Agent in Romanian for Predefined Microworlds

Abstract

Technology is becoming omnipresent in our lives due to its accessibility and ease of use. Conversational agents facilitate interactions in natural language and are frequently employed to perform repetitive tasks in a specific context. We introduce a conversational agent for Romanian built on top of the open-source RASA framework, capable to communicate in predefined microworlds. Two scenarios were considered, namely: a smart home assistant which interprets commands to IoT devices, and an interactive infopoint for our university focusing on providing guidance to students. Several enhancements were considered, including an NLP pre-processing pipeline from spaCy and a knowledge graph implemented using Grakn for conceptualizing the information accessible to the agent. Our agent can quickly classify intents and extract entities with high accuracy for a given microworld (F1-score of 97% for the first microworld and 93% for the second). A survey on 10 users showed high satisfaction in terms of the usefulness and the succinctness of the provided information.