Intent and Entity Detection with Data Augmentation for a Mental Health Virtual Assistant Chatbot
Ali Zamani, Matthew Reeson, Tyler J. Marshall, Mohamad Ali Gharaat, Alex Lambe Foster, Jasmine Marie Noble, Osmar R. Zai͏̈ane · 2023
We report on implementing MIRA, a mental health resource chatbot to support healthcare workers in finding timely and relevant mental health resources. To generate appropriate queries to our carefully curated resource database, the chatbot must correctly identify the intents of an interlocutor and extract relevant entities from the conversation. With insufficient labelled examples, we employ data augmentation to generate training data automatically. Moreover, instead of detecting intent and extracting entities independently with two different classifiers, we integrate the two tasks by taking advantage of their interdependencies obtaining 99% accuracy for intent detection and 95.4% accuracy in entity extraction.