Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks

Understand

Because of their superior ability to preserve sequence information over time, Long Short-Term Memory (LSTM) networks, a type of recurrent neural network with a more complex computational unit, have obtained strong results on a variety of sequence modeling tasks.

• The only underlying LSTM structure that has been explored so far is a linear chain.
• However, natural language exhibits syntactic properties that would naturally combine words to phrases.
• We introduce the Tree-LSTM, a generalization of LSTMs to tree-structured network topologies.