# Restricted Recurrent Neural Tensor Networks: Exploiting Word Frequency and Compositionality

## TL;DR

This paper introduces restricted recurrent neural tensor networks (r-RNTN) that allocate distinct weights for frequent words and shared weights for infrequent ones, improving language modeling efficiency and performance.

## Contribution

The paper proposes r-RNTN, a novel model that reduces memory costs while enhancing language model performance by exploiting word frequency and compositionality.

## Key findings

- r-RNTN outperforms standard RNNs in perplexity with fewer parameters
- r-RNTN achieves similar or better results than unrestricted RNTNs
- Effective with Gated Recurrent Units and LSTM

## Abstract

Increasing the capacity of recurrent neural networks (RNN) usually involves augmenting the size of the hidden layer, with significant increase of computational cost. Recurrent neural tensor networks (RNTN) increase capacity using distinct hidden layer weights for each word, but with greater costs in memory usage. In this paper, we introduce restricted recurrent neural tensor networks (r-RNTN) which reserve distinct hidden layer weights for frequent vocabulary words while sharing a single set of weights for infrequent words. Perplexity evaluations show that for fixed hidden layer sizes, r-RNTNs improve language model performance over RNNs using only a small fraction of the parameters of unrestricted RNTNs. These results hold for r-RNTNs using Gated Recurrent Units and Long Short-Term Memory.