Joint Syntactic and Semantic Analysis with a Multitask Deep Learning Framework for Spoken Language Understanding

2016 ◽  
Author(s):  
Jeremie Tafforeau ◽  
Frederic Bechet ◽  
Thierry Artiere ◽  
Benoit Favre
Keyword(s):  
Deep Learning ◽  
Semantic Analysis ◽  
Spoken Language ◽  
Language Understanding ◽  
Spoken Language Understanding ◽  
Learning Framework

scholarly journals A Joint Learning Framework With BERT for Spoken Language Understanding

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 168849-168858 ◽  
Author(s):  
Zhichang Zhang ◽  
Zhenwen Zhang ◽  
Haoyuan Chen ◽  
Zhiman Zhang
Keyword(s):  
Spoken Language ◽  
Language Understanding ◽  
Joint Learning ◽  
Spoken Language Understanding ◽  
Learning Framework

scholarly journals Research on Spoken Language Understanding Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hui Yanli
Keyword(s):  
Deep Learning ◽  
Spoken Language ◽  
Recognition Algorithm ◽  
Practical Application ◽  
Language Understanding ◽  
Spoken Language Understanding ◽  
Simulation Results ◽  
Gate Unit ◽  
Slot Filling ◽  
Cyclic Network

Aiming at solving the problem that the recognition effect of rare slot values in spoken language is poor, which affects the accuracy of oral understanding task, a spoken language understanding method is designed based on deep learning. The local features of semantic text are extracted and classified to make the classification results match the dialogue task. An intention recognition algorithm is designed for the classification results. Each datum has a corresponding intention label to complete the task of semantic slot filling. The attention mechanism is applied to the recognition of rare slot value information, the weight of hidden state and corresponding slot characteristics are obtained, and the updated slot value is used to represent the tracking state. An auxiliary gate unit is constructed between the upper and lower slots of historical dialogue, and the word vector is trained based on deep learning to complete the task of spoken language understanding. The simulation results show that the proposed method can realize multiple rounds of man-machine spoken language. Compared with the spoken language understanding methods based on cyclic network, context information, and label decomposition, it has higher accuracy and F1 value and has higher practical application value.

Integrating Syntax and Semantics into Spoken Language Understanding

1991 ◽  
Author(s):  
Lynette Hirschman ◽  
Stephanie Seneff ◽  
David Goodine ◽  
Michael Phillips
Keyword(s):  
Spoken Language ◽  
Language Understanding ◽  
Spoken Language Understanding

Spoken Language Understanding using Pretraining Technique

2020 ◽  
Author(s):  
Saad Ghojaria ◽  
Rahul Kotian ◽  
Yash Sawant ◽  
Suresh Mestry
Keyword(s):  
Spoken Language ◽  
Language Understanding ◽  
Spoken Language Understanding

scholarly journals RNN Based Incremental Online Spoken Language Understanding

2021 ◽  
Author(s):  
Prashanth Gurunath Shivakumar ◽  
Naveen Kumar ◽  
Panayiotis Georgiou ◽  
Shrikanth Narayanan
Keyword(s):  
Spoken Language ◽  
Language Understanding ◽  
Spoken Language Understanding
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