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

Feature Weight Tuning for Recursive Neural Networks