Reference : Comparing MultiLingual and Multiple MonoLingual Models for Intent Classification and ...

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/47529

Title :	Comparing MultiLingual and Multiple MonoLingual Models for Intent Classification and Slot Filling
Language :	English
Author, co-author :	Lothritz, Cedric [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX >]
	Allix, Kevin [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX >]
	Lebichot, Bertrand [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX >]
	Veiber, Lisa [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX >]
	Bissyande, Tegawendé François D Assise [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX >]
	Klein, Jacques [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX >]
Publication date :	25-Jun-2021
Main document title :	26th International Conference on Applications of Natural Language to Information Systems
Publisher :	Springer
Pages :	367-375
Peer reviewed :	Yes
Event name :	NLDB2021: 26th International Conference on Natural Language & Information Systems
Event date :	from 23-06-2021 to 25-05-2021
Keywords :	[en] Chatbots ; Multilingualism ; Intent Classification ; Slot Filling
Abstract :	[en] 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.
Permalink :	http://hdl.handle.net/10993/47529
DOI :	10.1007/978-3-030-80599-9_32
Other URL :	https://link.springer.com/chapter/10.1007%2F978-3-030-80599-9_32

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