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User intent detection plays a critical role in question-answering and dialog systems.
Long short-term memory
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Convolutional neural network based triangular crf for joint intent detection and slot filling
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Mining user intentions from medical queries: A neural network based heterogeneous jointly modeling approach
Chenwei Zhang, Wei Fan, Nan Du, and Philip S Yu. 2016 · 2016
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Zero-shot learning across heterogeneous overlapping domains
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Attention is all you need
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Defining intents
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A model of zero-shot learning of spoken language understanding
Majid Yazdani and James Henderson. 2015 · 2015
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Soravit Changpinyo, Wei-Lun Chao, Boqing Gong, and Fei Sha. 2016 · 2016
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Attention-based recurrent neural network models for joint intent detection and slot filling
Bing Liu and Ian Lane. 2016 · 2016
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Zero-shot learning of intent embeddings for expansion by convolutional deep structured semantic models
Yun-Nung Chen, Dilek Hakkani-Tür, and Xiaodong He. 2016a
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End-to-end memory networks with knowledge carryover for multi-turn spoken language understanding
Yun-Nung Chen, Dilek Hakkani-Tür, Gökhan Tür, Jianfeng Gao, and Li Deng. 2016b
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Online adaptative zero-shot learning spoken language understanding using word-embedding
Emmanuel Ferreira, Bassam Jabaian, and Fabrice Lefevre. 2015a
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Zero-shot semantic parser for spoken language understanding
Emmanuel Ferreira, Bassam Jabaian, and Fabrice Lefèvre. 2015b
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Alexa, siri, cortana, and more: An introduction to voice assistants
Matthew B Hoy. 2018 · 2018
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