Papers Toxic Spans Detection
“Toxic Spans Detection” 태그가 달린 논문 38편 · 필터 해제
Cross-Domain Toxic Spans Detection
Given the dynamic nature of toxic language use, automated methods for detecting toxic spans are likely to encounter distributional shift. To explore this phenomenon, we evaluate three approaches for detecting toxic spans…
Toxic Spans DetectionViHOS: Hate Speech Spans Detection for Vietnamese
The rise in hateful and offensive language directed at other users is one of the adverse side effects of the increased use of social networking platforms. This could make it difficult for human moderators to review tagge…
Hate Span IdentificationSequence-to-sequence Language ModelingToxic Spans DetectionVietnamese Hate Speech Detection+2From the Detection of Toxic Spans in Online Discussions to the Analysis of Toxic-to-Civil Transfer
We study the task of toxic spans detection, which concerns the detection of the spans that make a text toxic, when detecting such spans is possible. We introduce a dataset for this task, ToxicSpans, which we release publ…
Toxic Spans DetectionFrom the Detection of Toxic Spans in Online Discussions to the Analysis of Toxic-to-Civil Transfer
We study the task of toxic spans detection, which concerns the detection of the spans that make a text toxic, when detecting such spans is possible. We introduce a dataset for this task, ToxicSpans, which we release publ…
Toxic Spans DetectionUoB at SemEval-2021 Task 5: Extending Pre-Trained Language Models to Include Task and Domain-Specific Information for Toxic Span Prediction
Toxicity is pervasive in social media and poses a major threat to the health of online communities. The recent introduction of pre-trained language models, which have achieved state-of-the-art results in many NLP tasks, …
token-classificationToken ClassificationToxic Spans DetectionSemEval-2021 Task 5: Toxic Spans Detection
The Toxic Spans Detection task of SemEval-2021 required participants to predict the spans of toxic posts that were responsible for the toxic label of the posts. The task could be addressed as supervised sequence labeling…
Toxic Spans DetectionMedAI at SemEval-2021 Task 5: Start-to-end Tagging Framework for Toxic Spans Detection
This paper describes the system submitted to SemEval 2021 Task 5: Toxic Spans Detection. The task concerns evaluating systems that detect the spans that make a text toxic when detecting such spans are possible. To addres…
Language ModelingLanguage ModellingToxic Spans DetectionHamiltonDinggg at SemEval-2021 Task 5: Investigating Toxic Span Detection using RoBERTa Pre-training
This paper presents our system submission to task 5: Toxic Spans Detection of the SemEval-2021 competition. The competition aims at detecting the spans that make a toxic span toxic. In this paper, we demonstrate our syst…
Toxic Spans DetectionHITSZ-HLT at SemEval-2021 Task 5: Ensemble Sequence Labeling and Span Boundary Detection for Toxic Span Detection
This paper presents the winning system that participated in SemEval-2021 Task 5: Toxic Spans Detection. This task aims to locate those spans that attribute to the text{'}s toxicity within a text, which is crucial for sem…
AttributeBoundary DetectionToxic Spans DetectionYNU-HPCC at SemEval-2021 Task 5: Using a Transformer-based Model with Auxiliary Information for Toxic Span Detection
Toxic span detection requires the detection of spans that make a text toxic instead of simply classifying the text. In this paper, a transformer-based model with auxiliary information is proposed for SemEval-2021 Task 5.…
Toxic Spans DetectionGHOST at SemEval-2021 Task 5: Is explanation all you need?
This paper discusses different approaches to the Toxic Spans Detection task. The problem posed by the task was to determine which words contribute mostly to recognising a document as toxic. As opposed to binary classific…
AllBinary ClassificationClassificationExplainable Artificial Intelligence (XAI)+3LISAC FSDM USMBA at SemEval-2021 Task 5: Tackling Toxic Spans Detection Challenge with Supervised SpanBERT-based Model and Unsupervised LIME-based Model
Toxic spans detection is an emerging challenge that aims to find toxic spans within a toxic text. In this paper, we describe our solutions to tackle toxic spans detection. The first solution, which follows a supervised a…
Toxic Spans DetectionHITMI\&T at SemEval-2021 Task 5: Integrating Transformer and CRF for Toxic Spans Detection
This paper introduces our system at SemEval-2021 Task 5: Toxic Spans Detection. The task aims to accurately locate toxic spans within a text. Using BIO tagging scheme, we model the task as a token-level sequence labeling…
Toxic Spans DetectionAStarTwice at SemEval-2021 Task 5: Toxic Span Detection Using RoBERTa-CRF, Domain Specific Pre-Training and Self-Training
This paper describes our contribution to SemEval-2021 Task 5: Toxic Spans Detection. Our solution is built upon RoBERTa language model and Conditional Random Fields (CRF). We pre-trained RoBERTa on Civil Comments dataset…
Language ModelingLanguage ModellingToxic Spans DetectionS-NLP at SemEval-2021 Task 5: An Analysis of Dual Networks for Sequence Tagging
The SemEval 2021 task 5: Toxic Spans Detection is a task of identifying considered-toxic spans in text, which provides a valuable, automatic tool for moderating online contents. This paper represents the second-place met…
Language ModelingLanguage ModellingToxic Spans Detectionhub at SemEval-2021 Task 5: Toxic Span Detection Based on Word-Level Classification
This article introduces the system description of the hub team, which explains the related work and experimental results of our team{'}s participation in SemEval 2021 Task 5: Toxic Spans Detection. The data for this shar…
Toxic Spans DetectionSefamerve ARGE at SemEval-2021 Task 5: Toxic Spans Detection Using Segmentation Based 1-D Convolutional Neural Network Model
This paper describes our contribution to SemEval-2021 Task 5: Toxic Spans Detection. Our approach considers toxic spans detection as a segmentation problem. The system, Waw-unet, consists of a 1-D convolutional neural ne…
SegmentationSemantic SegmentationText SegmentationToxic Spans DetectionSkoltechNLP at SemEval-2021 Task 5: Leveraging Sentence-level Pre-training for Toxic Span Detection
This work describes the participation of the Skoltech NLP group team (Sk) in the Toxic Spans Detection task at SemEval-2021. The goal of the task is to identify the most toxic fragments of a given sentence, which is a bi…
SentenceToxic Spans DetectionEntity at SemEval-2021 Task 5: Weakly Supervised Token Labelling for Toxic Spans Detection
Detection of toxic spans - detecting toxicity of contents in the granularity of tokens - is crucial for effective moderation of online discussions. The baseline approach for this problem using the transformer model is to…
ClassificationLanguage ModelingLanguage ModellingSentence+4BennettNLP at SemEval-2021 Task 5: Toxic Spans Detection using Stacked Embedding Powered Toxic Entity Recognizer
With the rapid growth in technology, social media activity has seen a boom across all age groups. It is humanly impossible to check all the tweets, comments and status manually whether they follow proper community guidel…
SentenceToxic Spans Detection