Interpretable Multi Labeled Bengali Toxic Comments Classification using Deep Learning

• URL: http://arxiv.org/abs/2304.04087v1
• Date: Sat, 8 Apr 2023 19:28:26 GMT
• Title: Interpretable Multi Labeled Bengali Toxic Comments Classification using Deep Learning
• Abstract summary: We have prepared a manually labeled dataset consisting of 16,073 instances among which 8,488 are Toxic. Any toxic comment may correspond to one or more of the six toxic categories - vulgar, hate, religious, threat, troll, and insult simultaneously. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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

Related papers

• Beyond Flat Labels: Level-Restricted Contrastive Learning for Hierarchical Fine-Grained Vision Classification [50.20892407648858]
  Multimodal contrastive learning has enabled zero-shot visual classification by aligning images with textual categories.<n>Existing methods often produce predictions that are inconsistent across taxonomic levels.<n>We propose a group-balanced design, ensuring each taxonomic level receives adequate optimization.<n>We train our model with TreeOfLife-10M based on BioCLIP and evaluate it across multiple hierarchical classification benchmarks.
  arXiv  Detail & Related papers  (2026-06-20T02:12:05Z)
• A Comparative Study of PyCaret AutoML and CNN-BiLSTM for Binary Hate Speech Detection in Indonesian Twitter [0.0]
  This paper compares a PyCaret AutoML branch and a CNN-BiLSTM branch for binary hate speech detection on Indonesian Twitter using the HS label from the corpus of Ibrohim and Budi.<n>The conventional branch uses TF-IDF with a lexicon-based abusive-word count, whereas the neural branch learns dense token representations and captures both local phrase patterns and bidirectional context.<n>On the held-out split, CNN-BiLSTM achieves the best result with 83.8% accuracy, 79.8% precision, 82.7% recall, and 81.2% F1-score.
  arXiv  Detail & Related papers  (2026-05-06T13:20:51Z)
• Detoxification for LLM: From Dataset Itself [69.09071947137926]
  Existing detoxification methods for large language models mainly focus on post-training stage or inference time, while few tackle the source of toxicity, namely, the dataset itself.<n>We attempt to detoxify directly on raw corpora with SoCD (Soft Contrastive Decoding), which guides an LLM to localize and rewrite toxic spans in raw data while preserving semantics.<n>On GPT2-XL, HSPD attains state-of-the-art detoxification, reducing Toxicity Probability (TP) from 0.42 to 0.18 and Maximum Expected Toxicity (EMT) from 0.43 to 0.20.
  arXiv  Detail & Related papers  (2026-04-21T06:12:29Z)
• ToxiTrace: Gradient-Aligned Training for Explainable Chinese Toxicity Detection [9.769395444384012]
  ToxiTrace is an explainability-oriented method for BERT-style encoders with three components.<n>ToxiTrace improves classification accuracy and toxic span extraction while preserving efficient encoder-based inference and producing more coherent, human-readable explanations.
  arXiv  Detail & Related papers  (2026-04-14T05:54:32Z)
• Concept-Based Interpretability for Toxicity Detection [3.66086430438787]
  disproportionate attribution of concepts towards the target class often results in classification errors.<n>Our work introduces an interpretability technique based on the Concept Gradient (CG) method.<n>We propose the curation of toxic words that contribute to misclassifications in text classification models.
  arXiv  Detail & Related papers  (2025-11-15T14:53:23Z)
• Rethinking Toxicity Evaluation in Large Language Models: A Multi-Label Perspective [104.09817371557476]
  Large language models (LLMs) have achieved impressive results across a range of natural language processing tasks.<n>Their potential to generate harmful content has raised serious safety concerns.<n>We introduce three novel multi-label benchmarks for toxicity detection.
  arXiv  Detail & Related papers  (2025-10-16T06:50:33Z)
• Small sample-based adaptive text classification through iterative and contrastive description refinement [0.0]
  Large language models (LLMs) often struggle to generalize in domains with evolving knowledge and ambiguous category boundaries.<n>We propose a classification framework that combines iterative topic refinement, contrastive prompting, and active learning.<n>The framework features a human-in-the-loop component, allowing users to introduce or revise category definitions in natural language.
  arXiv  Detail & Related papers  (2025-08-01T11:12:38Z)
• Ensemble of classifiers for speech evaluation [0.0]
  The article describes an attempt to apply an ensemble of binary classifiers to solve the problem of speech assessment in medicine. A dataset was compiled based on quantitative and expert assessments of syllable pronunciation quality.
  arXiv  Detail & Related papers  (2024-12-29T17:28:32Z)
• Determination of toxic comments and unintended model bias minimization using Deep learning approach [0.0]
  In this research, our aim is to detect toxic comment and reduce the unintended bias concerning identity features such as race, gender, sex, religion by fine-tuning an attention based model called BERT(Bidirectional Representation from Transformers) We apply weighted loss to address the issue of unbalanced data and compare the performance of a fine-tuned BERT model with a traditional Logistic Regression model in terms of classification and bias minimization.
  arXiv  Detail & Related papers  (2023-11-08T16:10:28Z)
• ToxiSpanSE: An Explainable Toxicity Detection in Code Review Comments [4.949881799107062]
  ToxiSpanSE is the first tool to detect toxic spans in the Software Engineering (SE) domain. Our model achieved the best score with 0.88 $F1$, 0.87 precision, and 0.93 recall for toxic class tokens.
  arXiv  Detail & Related papers  (2023-07-07T04:55:11Z)
• Interval Type-2 Fuzzy Neural Networks for Multi-Label Classification [14.20513951604573]
  We propose a multi-label classification model based on interval type-2 fuzzy logic. In the proposed model, we use a deep neural network to predict the type-1 fuzzy membership of an instance. We also propose a loss function to measure the similarities between binary labels in datasets and interval type-2 fuzzy memberships generated by our model.
  arXiv  Detail & Related papers  (2023-02-21T04:00:44Z)
• Weakly-supervised segmentation using inherently-explainable classification models and their application to brain tumour classification [0.46873264197900916]
  This paper introduces three inherently-explainable classifiers to tackle both of these problems as one. The models were employed for the task of multi-class brain tumour classification using two different datasets. The obtained accuracy on a subset of tumour-only images outperformed the state-of-the-art glioma tumour grading binary classifiers with the best model achieving 98.7% accuracy.
  arXiv  Detail & Related papers  (2022-06-10T14:44:05Z)
• Mitigating Biases in Toxic Language Detection through Invariant Rationalization [70.36701068616367]
  biases toward some attributes, including gender, race, and dialect, exist in most training datasets for toxicity detection. We propose to use invariant rationalization (InvRat), a game-theoretic framework consisting of a rationale generator and a predictor, to rule out the spurious correlation of certain syntactic patterns. Our method yields lower false positive rate in both lexical and dialectal attributes than previous debiasing methods.
  arXiv  Detail & Related papers  (2021-06-14T08:49:52Z)
• SCRIB: Set-classifier with Class-specific Risk Bounds for Blackbox Models [48.374678491735665]
  We introduce Set-classifier with Class-specific RIsk Bounds (SCRIB) to tackle this problem. SCRIB constructs a set-classifier that controls the class-specific prediction risks with a theoretical guarantee. We validated SCRIB on several medical applications, including sleep staging on electroencephalogram (EEG) data, X-ray COVID image classification, and atrial fibrillation detection based on electrocardiogram (ECG) data.
  arXiv  Detail & Related papers  (2021-03-05T21:06:12Z)
• Challenges in Automated Debiasing for Toxic Language Detection [81.04406231100323]
  Biased associations have been a challenge in the development of classifiers for detecting toxic language. We investigate recently introduced debiasing methods for text classification datasets and models, as applied to toxic language detection. Our focus is on lexical (e.g., swear words, slurs, identity mentions) and dialectal markers (specifically African American English)
  arXiv  Detail & Related papers  (2021-01-29T22:03:17Z)
• Poison Attacks against Text Datasets with Conditional Adversarially Regularized Autoencoder [78.01180944665089]
  This paper demonstrates a fatal vulnerability in natural language inference (NLI) and text classification systems. We present a 'backdoor poisoning' attack on NLP models.
  arXiv  Detail & Related papers  (2020-10-06T13:03:49Z)
• RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models [93.151822563361]
  Pretrained neural language models (LMs) are prone to generating racist, sexist, or otherwise toxic language which hinders their safe deployment. We investigate the extent to which pretrained LMs can be prompted to generate toxic language, and the effectiveness of controllable text generation algorithms at preventing such toxic degeneration.
  arXiv  Detail & Related papers  (2020-09-24T03:17:19Z)
• Solving Long-tailed Recognition with Deep Realistic Taxonomic Classifier [68.38233199030908]
  Long-tail recognition tackles the natural non-uniformly distributed data in realworld scenarios. While moderns perform well on populated classes, its performance degrades significantly on tail classes. Deep-RTC is proposed as a new solution to the long-tail problem, combining realism with hierarchical predictions.
  arXiv  Detail & Related papers  (2020-07-20T05:57:42Z)
• Utilizing Deep Learning to Identify Drug Use on Twitter Data [0.0]
  The classification power of multiple methods was compared including support vector machines (SVM), XGBoost, and convolutional neural network (CNN) based classifiers. The accuracy scores were 76.35% and 82.31%, with an AUC of 0.90 and 0.91. The synthetically generated set provided increased scores, improving the classification capability and proving the worth of this methodology.
  arXiv  Detail & Related papers  (2020-03-08T07:52:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.