  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.
