Few-Shot Transfer Learning for Text Classification With Lightweight Word Embedding Based Models

Few-Shot Transfer Learning for Text Classification With Lightweight Word Embedding Based Models

Many deep learning architectures have been employed to model the semantic compositionality for text sequences, requiring a huge amount of supervised data for parameters training, making it unfeasible in situations where numerous annotated samples are not available or even do not exist. Different fro...

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Bibliographic Details
Main Authors: Chongyu Pan, Jian Huang, Jianxing Gong, Xingsheng Yuan
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Few-shot learning
transfer learning
text classification
word embedding based models
pooling strategy
Online Access:https://ieeexplore.ieee.org/document/8693837/

Internet

https://ieeexplore.ieee.org/document/8693837/

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