Comparing MultiLingual and Multiple MonoLingual Models for Intent Classification and Slot Filling

With the momentum of conversational AI for enhancing
client-to-business interactions, chatbots are sought in various domains,
including FinTech where they can automatically handle requests for
opening/closing bank accounts or issuing/terminating credit cards. Since
they are expected to replace emails and phone calls, chatbots must be
capable to deal with diversities of client populations. In this work, we
focus on the variety of languages, in particular in multilingual countries.
Specifically, we investigate the strategies for training deep learning models
of chatbots with multilingual data. We perform experiments for the
specific tasks of Intent Classification and Slot Filling in financial domain
chatbots and assess the performance of mBERT multilingual model vs
multiple monolingual models.