This paper presents a deep learning-based pipeline for categorizing Bengali toxic comments, in which at first a binary classification model is used to determine whether a comment is toxic or not, and then a multi-label classifier is employed to determine which toxicity type the comment belongs to. For this purpose, we have prepared a manually labeled dataset consisting of 16,073 instances among which 8,488 are Toxic and any toxic comment may correspond to one or more of the six toxic categories - vulgar, hate, religious, threat, troll, and insult simultaneously. Long Short Term Memory (LSTM) with BERT Embedding achieved 89.42% accuracy for the binary classification task while as a multi-label classifier, a combination of Convolutional Neural Network and Bi-directional Long Short Term Memory (CNN-BiLSTM) with attention mechanism achieved 78.92% accuracy and 0.86 as weighted F1-score. To explain the predictions and interpret the word feature importance during classification by the proposed models, we utilized Local Interpretable Model-Agnostic Explanations (LIME) framework. We have made our dataset public and can be accessed at - https://github.com/deepu099cse/Multi-Labeled-Bengali-Toxic-Comments-Classification

翻译：本文提出了一种基于深度学习的孟加拉语有毒评论分类流程，首先使用二分类模型判断评论是否有毒，随后采用多标签分类器确定其所属的毒性类型。为此，我们构建了一个手动标注的数据集，包含16,073条实例，其中8,488条为有毒评论，每条有毒评论可能对应一个或多个毒性类别——粗俗、仇恨、宗教、威胁、嘲讽和侮辱。在二分类任务中，基于BERT嵌入的长短时记忆网络（LSTM）达到了89.42%的准确率；而作为多标签分类器，结合注意力机制的卷积神经网络与双向长短时记忆网络（CNN-BiLSTM）取得了78.92%的准确率和0.86的加权F1分数。为解释模型预测并揭示分类过程中词语特征的重要性，我们采用了局部可解释模型无关解释（LIME）框架。我们已将数据集公开，可通过以下链接访问：https://github.com/deepu099cse/Multi-Labeled-Bengali-Toxic-Comments-Classification