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arXiv · 1503.00075

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

Kai Sheng Tai·Richard Socher·Christopher D. Manning
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Abstract

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. Tree-LSTMs outperform all existing systems and strong LSTM baselines on two tasks: predicting the semantic relatedness of two sentences (SemEval 2014, Task 1) and sentiment classification (Stanford Sentiment Treebank).

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Kai Sheng Tai, Richard Socher, Christopher D. Manning. 2015-02-28. Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks. https://arxiv.org/abs/1503.00075

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