论文标题
用于网络欺凌分类的简单变压器和RES-CNN-BILSTM的性能比较
Performance Comparison of Simple Transformer and Res-CNN-BiLSTM for Cyberbullying Classification
论文作者
论文摘要
使用基于双向LSTM的LSTM架构进行文本分类的任务在计算上是昂贵且耗时的训练。为此，与传统的深度学习体系结构相比，发现了变压器有效地表现出色。在本文中，我们介绍了基于简单的变压器网络与基于RES-CNN-BILSTM的网络之间的基于性能的比较，用于网络欺凌文本分类问题。获得的结果表明，我们使用65万个参数训练的变压器能够以4882万参数的参数来击败RES-CNN-BILSTM的性能，以实现更快的训练速度和更广义的指标。本文还比较了与变压器的1维角色嵌入网络和100维手套嵌入网络。
The task of text classification using Bidirectional based LSTM architectures is computationally expensive and time consuming to train. For this, transformers were discovered which effectively give good performance as compared to the traditional deep learning architectures. In this paper we present a performance based comparison between simple transformer based network and Res-CNN-BiLSTM based network for cyberbullying text classification problem. The results obtained show that transformer we trained with 0.65 million parameters has significantly being able to beat the performance of Res-CNN-BiLSTM with 48.82 million parameters for faster training speeds and more generalized metrics. The paper also compares the 1-dimensional character level embedding network and 100-dimensional glove embedding network with transformer.
