1.0Microsoft Researchhttps://www.microsoft.com/en-us/researchYun-Nung Vivian Chenhttps://www.microsoft.com/en-us/research/people/vivic/Multi-Domain Joint Semantic Frame Parsing using Bi-directional RNN-LSTM - Microsoft Researchrich600338<blockquote class="wp-embedded-content" data-secret="Zac6iMu9mu"><a href="https://www.microsoft.com/en-us/research/publication/multijoint/">Multi-Domain Joint Semantic Frame Parsing using Bi-directional RNN-LSTM</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/publication/multijoint/embed/#?secret=Zac6iMu9mu" width="600" height="338" title="“Multi-Domain Joint Semantic Frame Parsing using Bi-directional RNN-LSTM” — Microsoft Research" data-secret="Zac6iMu9mu" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script type="text/javascript">
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Sequence-to-sequence deep learning has recently emerged as a new paradigm in supervised learning for spoken language understanding. However, most of the previous studies explored this framework for building single domain models for each task, such as slot filling or domain classification, comparing deep learning based approaches with conventional ones like conditional random fields. This paper […]