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Register a new userLZ1904 at SemEval-2021 Task 5: Bi-LSTM-CRF for Toxic Span Detection using Pretrained Word Embedding
LZ1904 في مهمة SEMEVAL-2021 5: BI-LSTM-CRF للكشف عن الأمان السامة باستخدام كلمة إعادة التضمين
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تم استخدام الشبكات العصبية المتكررة على نطاق واسع في مهام معالجة اللغة الطبيعية المختلفة (NLP) مثل تصنيف النص وعلامات التسلسل والترجمة الآلية.ذاكرة طويلة الأجل طويلة الأجل (LSTM)، وهي وحدة خاصة من RNN، لديها فائدة من حفظ المعلومات السابقة وحتى المستقبل في جملة (خاصة بالنسبة ل LSTM ثنائي الاتجاه).في المهمة المشتركة المتمثلة في اكتشاف الممتد، مما يجعل النصوص سامة، نقوم أولا بتطبيق تضمين الكلمة المسبقة (القفازات) لتوليد مجاهاجر الكلمة بعد التوت.ثم نقوم ببناء نموذج عشوائي طويل الأجل طويل الأجل الطويل الأجل (BI-LSTM-CRF) نموذج بحوث بايدو للتنبؤ بما إذا كانت كل كلمة في الجملة سامة أم لا.نحن نغلق فرط HyperParameters من معدل التسرب، وعدد وحدات LSTM، وتضمين حجم مع 10 حفلات واختيار أفضل عصر مع استدعاء التحقق من الصحة.لدينا نموذج يحقق درجة F1 من 66.99 في المئة في Dataset اختبار.
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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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.
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.
This paper presents our submission to SemEval-2021 Task 5: Toxic Spans Detection. The purpose of this task is to detect the spans that make a text toxic, which is a complex labour for several reasons. Firstly, because of the intrinsic subjectivity of
toxicity, and secondly, due to toxicity not always coming from single words like insults or offends, but sometimes from whole expressions formed by words that may not be toxic individually. Following this idea of focusing on both single words and multi-word expressions, we study the impact of using a multi-depth DistilBERT model, which uses embeddings from different layers to estimate the final per-token toxicity. Our quantitative results show that using information from multiple depths boosts the performance of the model. Finally, we also analyze our best model qualitatively.
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.
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 add a token classification he
ad to the language model and fine-tune the layers with the token labeled dataset. One of the limitations of such a baseline approach is the scarcity of labeled data. To improve the results, We studied leveraging existing public datasets for a related but different task of entire comment/sentence classification. We propose two approaches: the first approach fine-tunes transformer models that are pre-trained on sentence classification samples. In the second approach, we perform weak supervision with soft attention to learn token level labels from sentence labels. Our experiments show improvements in the F1 score over the baseline approach. The implementation has been released publicly.
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