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Register a new userCisco at SemEval-2021 Task 5: What's Toxic?: Leveraging Transformers for Multiple Toxic Span Extraction from Online Comments
سيسكو في مهمة Semeval-2021 5: ما هو السامة؟: محولات الاستفادة من استخراج سباحة سامة متعددة من التعليقات عبر الإنترنت
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تستخدم منصات الشبكة الاجتماعية عموما لمشاركة المحتوى الإيجابي والبناء والرائعة. ومع ذلك، في الآونة الأخيرة، غالبا ما يتعرض الناس على المحتوى المرفوض مثل التهديد وهجمات الهوية أو خطاب الكراهية أو الإهانات أو النصوص الفاحشة أو الملاحظات الهجومية أو البلطجة. يركز العمل الحالي على كشف الكلام السام على التصنيف الثنائي أو على التمييز الخطاب السام بين مجموعة صغيرة من الفئات. تصف هذه الورقة النظام الذي اقترحه فريق Cisco for Semeval-2021 المهمة 5: الكشف عن الأمور السامة، أول مهمة مشتركة تركز على اكتشاف المواقف في النص الذي يعزى إلى سميته، باللغة الإنجليزية. نحن نقترب من هذه المشكلة في المقام الأول بطريقتين: نهج علامات التسلسل ونهج تحليل التبعية. في نهج علامات التسلسل لدينا، نعلم كل رمز رمزي في جملة تحت مخطط وضع علامات معينة. أثبتت بنية الأداء الخاصة بنا في هذا النهج أيضا أنها أفضل بنية أداء لدينا بشكل عام مع درجة F1 من 0.6922، وبالتالي وضع 7 لنا في مرحلة التقييم النهائية المتصدرين. نستكشف أيضا نهج تحليل التبعية حيث استخرفنا يمتد من عقوبة الإدخال تحت إشراف حدود المستهدفة المستهدفة وترتيب تمديدنا باستخدام نموذج بيافين. أخيرا، نقدم أيضا تحليلا مفصلا لنتائجنا وأداء النموذج في ورقنا.
Social network platforms are generally used to share positive, constructive, and insightful content. However, in recent times, people often get exposed to objectionable content like threat, identity attacks, hate speech, insults, obscene texts, offensive remarks or bullying. Existing work on toxic speech detection focuses on binary classification or on differentiating toxic speech among a small set of categories. This paper describes the system proposed by team Cisco for SemEval-2021 Task 5: Toxic Spans Detection, the first shared task focusing on detecting the spans in the text that attribute to its toxicity, in English language. We approach this problem primarily in two ways: a sequence tagging approach and a dependency parsing approach. In our sequence tagging approach we tag each token in a sentence under a particular tagging scheme. Our best performing architecture in this approach also proved to be our best performing architecture overall with an F1 score of 0.6922, thereby placing us 7th on the final evaluation phase leaderboard. We also explore a dependency parsing approach where we extract spans from the input sentence under the supervision of target span boundaries and rank our spans using a biaffine model. Finally, we also provide a detailed analysis of our results and model performance in our paper.
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We leverage a BLSTM with attention to identify toxic spans in texts. We explore different dimensions which affect the model's performance. The first dimension explored is the toxic set the model is trained on. Besides the provided dataset, we explore
the transferability of 5 different toxic related sets, including offensive, toxic, abusive, and hate sets. We find that the solely offensive set shows the highest promise of transferability. The second dimension we explore is methodology, including leveraging attention, employing a greedy remove method, using a frequency ratio, and examining hybrid combinations of multiple methods. We conduct an error analysis to examine which types of toxic spans were missed and which were wrongly inferred as toxic along with the main reasons why they occurred. Finally, we extend our method via ensembles, which achieves our highest F1 score of 55.1.
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, using training data with gol
d toxic spans provided by the organisers. It could also be treated as rationale extraction, using classifiers trained on potentially larger external datasets of posts manually annotated as toxic or not, without toxic span annotations. For the supervised sequence labeling approach and evaluation purposes, posts previously labeled as toxic were crowd-annotated for toxic spans. Participants submitted their predicted spans for a held-out test set and were scored using character-based F1. This overview summarises the work of the 36 teams that provided system descriptions.
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 shared task comes from some posts on
the Internet. The task goal is to identify the toxic content contained in these text data. We need to find the span of the toxic text in the text data as accurately as possible. In the same post, the toxic text may be one paragraph or multiple paragraphs. Our team uses a classification scheme based on word-level to accomplish this task. The system we used to submit the results is ALBERT+BILSTM+CRF. The result evaluation index of the task submission is the F1 score, and the final score of the prediction result of the test set submitted by our team is 0.6640226029.
Recurrent Neural Networks (RNN) have been widely used in various Natural Language Processing (NLP) tasks such as text classification, sequence tagging, and machine translation. Long Short Term Memory (LSTM), a special unit of RNN, has the benefit of
memorizing past and even future information in a sentence (especially for bidirectional LSTM). In the shared task of detecting spans which make texts toxic, we first apply pretrained word embedding (GloVe) to generate the word vectors after tokenization. And then we construct Bidirectional Long Short Term Memory-Conditional Random Field (Bi-LSTM-CRF) model by Baidu research to predict whether each word in the sentence is toxic or not. We tune hyperparameters of dropout rate, number of LSTM units, embedding size with 10 epochs and choose the best epoch with validation recall. Our model achieves an F1 score of 66.99 percent in test dataset.
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 guidelines. A lot of toxicity is reg
ularly posted on these social media platforms. This research aims to find toxic words in a sentence so that a healthy social community is built across the globe and the users receive censored content with specific warnings and facts. To solve this challenging problem, authors have combined concepts of Linked List for pre-processing and then used the idea of stacked embeddings like BERT Embeddings, Flair Embeddings and Word2Vec on the flairNLP framework to get the desired results. F1 metric was used to evaluate the model. The authors were able to produce a 0.74 F1 score on their test set.
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