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 ![]() | |
Allix, Kevin ![]() | |
Lebichot, Bertrand ![]() | |
Veiber, Lisa ![]() | |
Bissyande, Tegawendé François D Assise ![]() | |
Klein, Jacques ![]() | |
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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