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• W2587385466 abstract "Spoken language understanding (SLU) is one of the important problem in natural language processing, and especially in dialog system. Fifth Dialog State Tracking Challenge (DSTC5) introduced a SLU challenge task, which is automatic tagging to speech utterances by two speaker roles with speech acts tag and semantic slots tag. In this paper, we focus on speech acts tagging. We propose local coactivate multi-task learning model for capturing structured speech acts, based on sentence features by recurrent convolutional neural networks. An experiment result, shows that our model outperformed all other submitted entries, and were able to capture coactivated local features of category and attribute, which are parts of speech act." @default.
• W2587385466 created "2017-02-17" @default.
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• W2587385466 date "2016-12-01" @default.
• W2587385466 modified "2023-09-26" @default.
• W2587385466 title "Recurrent convolutional neural networks for structured speech act tagging" @default.
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• W2587385466 doi "https://doi.org/10.1109/slt.2016.7846312" @default.
• W2587385466 hasPublicationYear "2016" @default.
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