Comparative Study of CNN and RNN for Natural Language Processing
It addresses the problem of choosing between CNN and RNN for NLP practitioners, but it is incremental as it compares existing methods without introducing new techniques.
This paper conducted a systematic comparison of CNN and RNN architectures across various NLP tasks to provide guidance for model selection, but it did not report specific performance numbers or results.
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.