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2021 | OriginalPaper | Chapter

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

Authors : Cedric Lothritz, Kevin Allix, Bertrand Lebichot, Lisa Veiber, Tegawendé F. Bissyandé, Jacques Klein

Published in: Natural Language Processing and Information Systems

Publisher: Springer International Publishing

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Abstract

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.

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previous chapter Profiling Fake News Spreaders: Personality and Visual Information Matter

next chapter Automated Retrieval of Graphical User Interface Prototypes from Natural Language Requirements

https://github.com/RasaHQ/financial-demo.

Available at https://github.com/Trustworthy-Software/BCS-dataset.

Further information on Rasa models: https://rasa.com/docs/rasa/components/.

https://github.com/google-research/bert/blob/master/multilingual.md.

https://github.com/saffsd/langid.py.

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Title: Comparing MultiLingual and Multiple MonoLingual Models for Intent Classification and Slot Filling
Authors: Cedric Lothritz
Kevin Allix
Bertrand Lebichot
Lisa Veiber
Tegawendé F. Bissyandé
Jacques Klein
Publisher: Springer International Publishing
Book: Natural Language Processing and Information Systems
Print ISBN: 978-3-030-80598-2

Electronic ISBN: 978-3-030-80599-9

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-80599-9_32

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