A Joint Learning Framework With BERT for Spoken Language Understanding

A Joint Learning Framework With BERT for Spoken Language Understanding

Intent classification and slot filling are two essential tasks for spoken language understanding. Recently, joint learning has been shown to be effective for the two tasks. However, most joint learning methods only consider joint learning using shared parameters on the surface level rather than the...

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Bibliographic Details
Main Authors: Zhichang Zhang, Zhenwen Zhang, Haoyuan Chen, Zhiman Zhang
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Spoken language understanding
intent classification and slot filling
joint learning
intent-augmented mechanism
pre-trained language model
Online Access:https://ieeexplore.ieee.org/document/8907842/

Internet

https://ieeexplore.ieee.org/document/8907842/

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