Papers Toxic Comment Classification
“Toxic Comment Classification” 태그가 달린 논문 29편 · 필터 해제
CoGate-LSTM: Prototype-Guided Feature-Space Gating for Mitigating Gradient Dilution in Imbalanced Toxic Comment Classification
Toxic text classification for online moderation remains challenging under extreme class imbalance, where rare but high-risk labels such as threat and severe_toxic are consistently underdetected by conventional models. We…
Toxic Comment ClassificationText ClassificationApplying LLMs to Active Learning: Towards Cost-Efficient Cross-Task Text Classification without Manually Labeled Data
Machine learning-based classifiers have been used for text classification, such as sentiment analysis, news classification, and toxic comment classification. However, supervised machine learning models often require larg…
Active LearningClassificationNews ClassificationSentiment Analysis+3NLPGuard: A Framework for Mitigating the Use of Protected Attributes by NLP Classifiers
AI regulations are expected to prohibit machine learning models from using sensitive attributes during training. However, the latest Natural Language Processing (NLP) classifiers, which rely on deep learning, operate as …
Occupation predictionSentiment AnalysisToxic Comment ClassificationPyTorch Frame: A Modular Framework for Multi-Modal Tabular Learning
We present PyTorch Frame, a PyTorch-based framework for deep learning over multi-modal tabular data. PyTorch Frame makes tabular deep learning easy by providing a PyTorch-based data structure to handle complex tabular da…
Binary ClassificationDeep LearningToxic Comment ClassificationEvaluating The Effectiveness of Capsule Neural Network in Toxic Comment Classification using Pre-trained BERT Embeddings
Large language models (LLMs) have attracted considerable interest in the fields of natural language understanding (NLU) and natural language generation (NLG) since their introduction. In contrast, the legacy of Capsule N…
Multilingual NLPNatural Language UnderstandingText ClassificationText Generation+1CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing
Recent developments in large language models (LLMs) have been impressive. However, these models sometimes show inconsistencies and problematic behavior, such as hallucinating facts, generating flawed code, or creating of…
Fact CheckingNatural QuestionsProgram SynthesisQuestion Answering+2Beyond Toxic: Toxicity Detection Datasets are Not Enough for Brand Safety
The rapid growth in user generated content on social media has resulted in a significant rise in demand for automated content moderation. Various methods and frameworks have been proposed for the tasks of hate speech det…
Binary ClassificationClassificationHate Speech Detectiontext-classification+2A benchmark for toxic comment classification on Civil Comments dataset
Toxic comment detection on social media has proven to be essential for content moderation. This paper compares a wide set of different models on a highly skewed multi-label hate speech dataset. We consider inference time…
Toxic Comment ClassificationA New Generation of Perspective API: Efficient Multilingual Character-level Transformers
On the world wide web, toxic content detectors are a crucial line of defense against potentially hateful and offensive messages. As such, building highly effective classifiers that enable a safer internet is an important…
Toxic Comment ClassificationA Survey of Toxic Comment Classification Methods
While in real life everyone behaves themselves at least to some extent, it is much more difficult to expect people to behave themselves on the internet, because there are few checks or consequences for posting something …
ClassificationSurveyToxic Comment ClassificationIdentification of Bias Against People with Disabilities in Sentiment Analysis and Toxicity Detection Models
Sociodemographic biases are a common problem for natural language processing, affecting the fairness and integrity of its applications. Within sentiment analysis, these biases may undermine sentiment predictions for text…
FairnessSentiment AnalysisToxic Comment ClassificationRevisiting Contextual Toxicity Detection in Conversations
Understanding toxicity in user conversations is undoubtedly an important problem. Addressing "covert" or implicit cases of toxicity is particularly hard and requires context. Very few previous studies have analysed the i…
Data AugmentationToxic Comment ClassificationFHAC at GermEval 2021: Identifying German toxic, engaging, and fact-claiming comments with ensemble learning
The availability of language representations learned by large pretrained neural network models (such as BERT and ELECTRA) has led to improvements in many downstream Natural Language Processing tasks in recent years. Pret…
Classification of toxic, engaging, fact-claiming commentsEngaging Comment ClassificationEnsemble LearningFact-Claiming Comment Classification+1SS-BERT: Mitigating Identity Terms Bias in Toxic Comment Classification by Utilising the Notion of "Subjectivity" and "Identity Terms"
Toxic comment classification models are often found biased toward identity terms which are terms characterizing a specific group of people such as "Muslim" and "black". Such bias is commonly reflected in false-positive p…
Toxic Comment ClassificationIRCologne at GermEval 2021: Toxicity Classification
In this paper, we describe the TH Köln’s submission for the Shared Task on the Identification of Toxic Comments at GermEval 2021. Toxicity is a severe and latent problem in comments in online discussions. Complex languag…
ClassificationLanguage ModelingLanguage ModellingToxic Comment ClassificationDeTox at GermEval 2021: Toxic Comment Classification
In this work, we present our approaches on the toxic comment classification task (subtask 1) of the GermEval 2021 Shared Task. For this binary task, we propose three models: a German BERT transformer model; a multilayer …
ClassificationToxic Comment ClassificationUniversität Regensburg MaxS at GermEval 2021 Task 1: Synthetic Data in Toxic Comment Classification
We report on our submission to Task 1 of the GermEval 2021 challenge – toxic comment classification. We investigate different ways of bolstering scarce training data to improve off-the-shelf model performance on a toxic …
ClassificationToxic Comment ClassificationData Integration for Toxic Comment Classification: Making More Than 40 Datasets Easily Accessible in One Unified Format
With the rise of research on toxic comment classification, more and more annotated datasets have been released. The wide variety of the task (different languages, different labeling processes and schemes) has led to a la…
Data IntegrationToxic Comment ClassificationExplaining the Deep Natural Language Processing by Mining Textual Interpretable Features
Despite the high accuracy offered by state-of-the-art deep natural-language models (e.g. LSTM, BERT), their application in real-life settings is still widely limited, as they behave like a black-box to the end-user. Henc…
Decision MakingExplainable artificial intelligenceExplainable Artificial Intelligence (XAI)Sentiment Analysis+1Vietnamese Complaint Detection on E-Commerce Websites
Customer product reviews play a role in improving the quality of products and services for business organizations or their brands. Complaining is an attitude that expresses dissatisfaction with an event or a product not …
Complaint Comment ClassificationConstructive Comment ClassificationToxic Comment ClassificationVietnamese Datasets