Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
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).
Cite
@article{arxiv.1503.00075,
title = {Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks},
author = {Kai Sheng Tai and Richard Socher and Christopher D. Manning},
journal= {arXiv preprint arXiv:1503.00075},
year = {2015}
}
Comments
Accepted for publication at ACL 2015