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.

本篇论文比较了基于 Transformer 和 Res-CNN-BiLSTM 的神经网络在针对网络欺凌文本分类问题上的性能表现，结果显示，使用仅有 0.65 万参数的 Transformer 可以以更快训练速度和更完备的度量标准胜过具有 4882 万参数的 Res-CNN-BiLSTM；同时，本文还比较了基于 1-dimension 字符级嵌入网络、100-dimension glove 嵌入网络和 Transformer 的性能差异。

Simple Transformer 与 Res-CNN-BiLSTM 在网络欺凌分类中的性能比较

Performance Comparison of Simple Transformer and Res-CNN-BiLSTM for Cyberbullying Classification

Mental Health Disturbance has many reasons and cyberbullying is one of the
major causes that does exploitation using social media as an instrument. The
cyberbullying is done on the basis of Religion, Ethnicity, Age and Gender which
is a sensitive psychological issue. This can be addressed using Natural
Language Processing with Deep Learning, since social media is the medium and it
generates massive form of data in textual form. Such data can be leveraged to
find the semantics and derive what type of cyberbullying is done and who are
the people involved for early measures. Since deriving semantics is essential
we proposed a Hybrid Deep Learning Model named 1-Dimensional
CNN-Bidirectional-LSTMs with Residuals shortly known as Res-CNN-BiLSTM. In this
paper we have proposed the architecture and compared its performance with
different approaches of Embedding Deep Learning Algorithms.

本文提出了一种基于 Res-CNN-BiLSTM 混合深度学习模型的方法，用于分析社交媒体上的文本语言数据，识别并处理基于宗教、种族、年龄和性别等因素的网络欺凌行为，以对心理健康问题进行干预。