# Modeling Compositionality with Multiplicative Recurrent Neural Networks

arXiv: https://arxiv.org/abs/1412.6577 · HTML: https://mlsanity.com/paper/1412.6577/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2014.

## Built on by (most cited first, 1 of 1)

- [A Tensor Decomposition Perspective on Second-order RNNs](https://mlsanity.com/paper/2406.05045/) (2024)

## Abstract (arXiv)

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.