Modeling Compositionality with Multiplicative Recurrent Neural Networks

Ozan İrsoy; Claire Cardie

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arXiv · 1412.6577

Modeling Compositionality with Multiplicative Recurrent Neural Networks

Ozan İrsoy·Claire Cardie

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Abstract

We present the multiplicative recurrent neural network as a general model for compositional meaning in language, and evaluate it on the task of fine-grained sentiment analysis. We establish a connection to the previously investigated matrix-space models for compositionality, and show they are special cases of the multiplicative recurrent net. Our experiments show that these models perform comparably or better than Elman-type additive recurrent neural networks and outperform matrix-space models on a standard fine-grained sentiment analysis corpus. Furthermore, they yield comparable results to structural deep models on the recently published Stanford Sentiment Treebank without the need for generating parse trees.

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Ozan İrsoy, Claire Cardie. 2014-12-20. Modeling Compositionality with Multiplicative Recurrent Neural Networks. https://arxiv.org/abs/1412.6577

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