While having a similar objective, the proposed model requires fewer model parameters than the predecessors because it does not necessarily learn each word matrix representation separately, and the number of parameters for the composition function is not proportional to the cube of the hidden state dimension.
treeLStM achieves the state-of-the-art performance among the tree-structured models in various tasks, including natural language inference and sentiment classification.