Feature Weight Tuning for Recursive Neural Networks

Jiwei Li

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arXiv · 1412.3714

Feature Weight Tuning for Recursive Neural Networks

Jiwei Li

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Abstract

This paper addresses how a recursive neural network model can automatically leave out useless information and emphasize important evidence, in other words, to perform "weight tuning" for higher-level representation acquisition. We propose two models, Weighted Neural Network (WNN) and Binary-Expectation Neural Network (BENN), which automatically control how much one specific unit contributes to the higher-level representation. The proposed model can be viewed as incorporating a more powerful compositional function for embedding acquisition in recursive neural networks. Experimental results demonstrate the significant improvement over standard neural models.

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BibTeX RIS

Jiwei Li. 2014-12-13. Feature Weight Tuning for Recursive Neural Networks. https://arxiv.org/abs/1412.3714

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