1.0Microsoft Researchhttps://www.microsoft.com/en-us/researchYun-Nung Vivian Chenhttps://www.microsoft.com/en-us/research/people/vivic/JointSLU: Joint Semantic Frame Parsing for Spoken Language Understanding - Microsoft Researchrich600338<blockquote class="wp-embedded-content" data-secret="tfTfZDXPo8"><a href="https://www.microsoft.com/en-us/research/project/jointslu/">JointSLU: Joint Semantic Frame Parsing for Spoken Language Understanding</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/project/jointslu/embed/#?secret=tfTfZDXPo8" width="600" height="338" title="“JointSLU: Joint Semantic Frame Parsing for Spoken Language Understanding” — Microsoft Research" data-secret="tfTfZDXPo8" 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 project […]