This paper introduces Tree-LSTM, a generalization of Long Short-Term Memory (LSTM) networks to tree-structured network topologies. Tree-LSTMs are designed to better capture the syntactic structure of natural language, which is crucial for tasks such as semantic relatedness prediction and sentiment classification. The authors compare Tree-LSTMs with sequential LSTMs on two tasks: predicting the semantic relatedness of sentence pairs and sentiment classification of movie reviews. The results show that Tree-LSTMs outperform existing systems and sequential LSTM baselines, demonstrating their effectiveness in representing sentence meaning. The paper also discusses the advantages of Tree-LSTMs over sequential models, particularly in handling long sequences and preserving information from distant nodes.This paper introduces Tree-LSTM, a generalization of Long Short-Term Memory (LSTM) networks to tree-structured network topologies. Tree-LSTMs are designed to better capture the syntactic structure of natural language, which is crucial for tasks such as semantic relatedness prediction and sentiment classification. The authors compare Tree-LSTMs with sequential LSTMs on two tasks: predicting the semantic relatedness of sentence pairs and sentiment classification of movie reviews. The results show that Tree-LSTMs outperform existing systems and sequential LSTM baselines, demonstrating their effectiveness in representing sentence meaning. The paper also discusses the advantages of Tree-LSTMs over sequential models, particularly in handling long sequences and preserving information from distant nodes.