Syntactic Graph Convolutional Network for Spoken Language ...
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In this paper, we propose a novel joint model that applies a graph convolutional network over dependency trees to integrate the syntactic structure.
Jun 16, 2024 · In this paper, we utilize the Graph Convolutional Networks (GCNs) and dependency tree to incorporate the syntactical knowledge.
Syntactic Graph Convolutional Network for Spoken Language Understanding · no ... Slot filling and intent detection are two major tasks for spoken language ...
GNN is a specially designed deep learning algorithm for graph data, which stands out for its efficient capability of data relationship mining and has been ...
Nov 21, 2022 · Connected Papers is a visual tool to help researchers and applied scientists find academic papers relevant to their field of work.
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What is convolutional neural network in language processing?
How do graph convolutional networks work?
5 days ago · More precisely, graph convolutional networks can consider contextual information and automatically aggregate contextual information, allowing ...
Syntactic Graph Convolutional Network for Spoken Language Understanding. no ... Spoken Language Understanding (SLU) model can transfer knowledge across languages.
Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks. · A Lexicon-Based Graph Neural Network for Chinese NER.
In this paper, we propose a Syntactic Structure-Enhanced Dual Graph Convolutional Network (SSEDGCN) model for an ALSC task.
Missing: Spoken | Show results with:Spoken
A new multimodal word representation model, namely, GCNW, which uses the graph convolutional network to incorporate the phonetic and syntactic information into ...