Comparative Study of CNN and RNN for Natural Language Processing

TL;DR

This paper systematically compares CNN and RNN architectures across various NLP tasks to guide the selection of the most suitable deep neural network model for specific applications.

Contribution

It provides the first comprehensive analysis of CNN versus RNN performance on multiple NLP tasks, offering practical guidance for model choice.

Findings

CNN excels at position-invariant feature extraction.

RNN performs better at modeling sequential data.

Performance varies depending on the specific NLP task.

Abstract

Deep neural networks (DNN) have revolutionized the field of natural language processing (NLP). Convolutional neural network (CNN) and recurrent neural network (RNN), the two main types of DNN architectures, are widely explored to handle various NLP tasks. CNN is supposed to be good at extracting position-invariant features and RNN at modeling units in sequence. The state of the art on many NLP tasks often switches due to the battle between CNNs and RNNs. This work is the first systematic comparison of CNN and RNN on a wide range of representative NLP tasks, aiming to give basic guidance for DNN selection.