Reference : Comparing MultiLingual and Multiple MonoLingual Models for Intent Classification and ...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/47529
Comparing MultiLingual and Multiple MonoLingual Models for Intent Classification and Slot Filling
English
Lothritz, Cedric mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX >]
Allix, Kevin mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX >]
Lebichot, Bertrand mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX >]
Veiber, Lisa mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX >]
Bissyande, Tegawendé François D Assise mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX >]
Klein, Jacques mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX >]
25-Jun-2021
26th International Conference on Applications of Natural Language to Information Systems
Springer
367-375
Yes
NLDB2021: 26th International Conference on Natural Language & Information Systems
from 23-06-2021 to 25-05-2021
[en] Chatbots ; Multilingualism ; Intent Classification ; Slot Filling
[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.
http://hdl.handle.net/10993/47529
10.1007/978-3-030-80599-9_32
https://link.springer.com/chapter/10.1007%2F978-3-030-80599-9_32

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