# CARU: A Content-Adaptive Recurrent Unit for the Transition of Hidden State in NLP

@inproceedings{Chan2020CARUAC, title={CARU: A Content-Adaptive Recurrent Unit for the Transition of Hidden State in NLP}, author={Ka‐Hou Chan and W. Ke and Sio Kei Im}, booktitle={ICONIP}, year={2020} }

This article introduces a novel RNN unit inspired by GRU, namely the Content-Adaptive Recurrent Unit (CARU). The design of CARU contains all the features of GRU but requires fewer training parameters. We make use of the concept of weights in our design to analyze the transition of hidden states. At the same time, we also describe how the content adaptive gate handles the received words and alleviates the long-term dependence problem. As a result, the unit can improve the accuracy of the…

## 2 Citations

### A Multilayer CARU Framework to Obtain Probability Distribution for Paragraph-Based Sentiment Analysis

- Computer ScienceApplied Sciences
- 2021

This work proposes a Multilayer Content-Adaptive Recurrent Unit (CARU) network for paragraph information extraction and presents a type of CNN-based model as an extractor to explore and capture useful features in the hidden state.

### Dynamic SIoT Network Status Prediction

- Computer ScienceJournal of Networking and Network Applications
- 2022

The proposed CARU-EKF can improve the performance of time-series data forecasting for nonlinear SIoT data traffic and shows better performance than existing prediction methods in terms of metrics of Mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE) and determination coefficient (R2).

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