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Register a new userUTNLP at SemEval-2021 Task 5: A Comparative Analysis of Toxic Span Detection using Attention-based, Named Entity Recognition, and Ensemble Models
UTNLP في مهمة Semeval-2021: تحليل مقارن للكشف عن المكشوف السامة باستخدام الاهتمام، والاعتراف الكياني المسمى، ونماذج الفرقة
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اكتشاف أجزاء من الجملة المساهمة في سمية الجملة - - بدلا من توفير حكم على مستوى الجملة من البهمة --- من شأنه أن يزيد من تفسير النماذج والسماح للمشرفين البشري بفهم نواتج النظام بشكل أفضل.تقدم هذه الورقة فريقنا، UTNLP، منهجية ونتائج المهمة المشتركة SEMEVAL-2021 5 على الكشف عن الأمور السامة.نقوم باختبار نماذج متعددة وإدارات سياقية وأبلغ عن أفضل الإعداد من الجميع.تبدأ التجارب بنماذج قائمة على الكلمات الرئيسية ويتبعها نماذج القائم على الكيانات المستندة إلى الكيانات المستندة إلى مجال الانتباه، والتي تستند إلى الكيانات المستندة إلى الكيان ومقرها المحولات.أفضل نهجنا، نموذج الفرقة، يحقق F1 من 0.684 في مرحلة تقييم المسابقة.
Detecting which parts of a sentence contribute to that sentence's toxicity---rather than providing a sentence-level verdict of hatefulness--- would increase the interpretability of models and allow human moderators to better understand the outputs of the system. This paper presents our team's, UTNLP, methodology and results in the SemEval-2021 shared task 5 on toxic spans detection. We test multiple models and contextual embeddings and report the best setting out of all. The experiments start with keyword-based models and are followed by attention-based, named entity- based, transformers-based, and ensemble models. Our best approach, an ensemble model, achieves an F1 of 0.684 in the competition's evaluation phase.
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This paper describes the participation of SINAI team at Task 5: Toxic Spans Detection which consists of identifying spans that make a text toxic. Although several resources and systems have been developed so far in the context of offensive language,
both annotation and tasks have mainly focused on classifying whether a text is offensive or not. However, detecting toxic spans is crucial to identify why a text is toxic and can assist human moderators to locate this type of content on social media. In order to accomplish the task, we follow a deep learning-based approach using a Bidirectional variant of a Long Short Term Memory network along with a stacked Conditional Random Field decoding layer (BiLSTM-CRF). Specifically, we test the performance of the combination of different pre-trained word embeddings for recognizing toxic entities in text. The results show that the combination of word embeddings helps in detecting offensive content. Our team ranks 29th out of 91 participants.
Toxic language is often present in online forums, especially when politics and other polarizing topics arise, and can lead to people becoming discouraged from joining or continuing conversations. In this paper, we use data consisting of comments with
the indices of toxic text labelled to train an RNN to deter-mine which parts of the comments make them toxic, which could aid online moderators. We compare results using both the original dataset and an augmented set, as well as GRU versus LSTM RNN models.
This paper presents a system used for SemEval-2021 Task 5: Toxic Spans Detection. Our system is an ensemble of BERT-based models for binary word classification, trained on a dataset extended by toxic comments modified and generated by two language mo
dels. For the toxic word classification, the prediction threshold value was optimized separately for every comment, in order to maximize the expected F1 value.
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.
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.
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