Long Short-Term Memory Over Tree Structures

Xiaodan Zhu; Parinaz Sobhani; Hongyu Guo

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arXiv · 1503.04881

Long Short-Term Memory Over Tree Structures

Xiaodan Zhu·Parinaz Sobhani·Hongyu Guo

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Abstract

The chain-structured long short-term memory (LSTM) has showed to be effective in a wide range of problems such as speech recognition and machine translation. In this paper, we propose to extend it to tree structures, in which a memory cell can reflect the history memories of multiple child cells or multiple descendant cells in a recursive process. We call the model S-LSTM, which provides a principled way of considering long-distance interaction over hierarchies, e.g., language or image parse structures. We leverage the models for semantic composition to understand the meaning of text, a fundamental problem in natural language understanding, and show that it outperforms a state-of-the-art recursive model by replacing its composition layers with the S-LSTM memory blocks. We also show that utilizing the given structures is helpful in achieving a performance better than that without considering the structures.

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BibTeX RIS

Xiaodan Zhu, Parinaz Sobhani, Hongyu Guo. 2015-03-16. Long Short-Term Memory Over Tree Structures. https://arxiv.org/abs/1503.04881

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