Transformer-Capsule Model for Intent Detection (Student Abstract)
2020 ◽
Vol 34
(10)
◽
pp. 13885-13886
Intent recognition is one of the most crucial tasks in NLU systems, which are nowadays especially important for designing intelligent conversation. We propose a novel approach to intent recognition which involves combining transformer architecture with capsule networks. Our results show that such architecture performs better than original capsule-NLU network implementations and achieves state-of-the-art results on datasets such as ATIS, AskUbuntu ,and WebApp.
2019 ◽
Vol 33
◽
pp. 7484-7491
◽
2021 ◽
Vol 12
(1)
◽
pp. 29-49
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