ACL-RoBERTa-CNN Text Classification Model Combined with Contrastive Learning

Aiming at the problem of sparse Chinese text features and mixing of long and short texts, which lead to the difficulty of extracting word vector features and the single convolution kernel of traditional neural network and redundant parameters, the ACL-RoBERTa-CNN text classification model uses a contrast learning method to learn a uniformly distributed vector representation in order to achieve the effect of regular expression of space. Input the same sentence into dropout twice as "positive pairs", replacing the traditional data enhancement method. Use the contrast learned RoBERTa pre-training model to train the word vector, send the word vector to the CNN layer, use different size convolution kernels to capture the information of different length words in each piece of data, finally combine with Softmax the classifier classifies the extracted features. The experimental results on two public data sets show that ACL-RoBERTa-CNN classification performance is better than TextCNN, TextRNN, LSTM-ATT, RoBERTa-LSTM, RoBERTa-CNN and other deep learning text classification models.

I. Introduction

With the development of Internet technology, the Internet has become the main means of information dissemination, and a large amount of text data is added every day. In the face of these huge amounts of text data, how to effectively classify the data is a key issue in the current natural language processing field.