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S-NLP في مهمة Semeval-2021 5: تحليل الشبكات المزدوجة لعلامات التسلسل
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المهمة Sereval 2021 Semeval 5: الكشف عن الأمور السامة هي مهمة تحديد المواقف المسيح السامة في النص، والتي توفر أداة أوتوماتيكية قيمة للمحتويات عبر الإنترنت المعتدلة.هذه الورقة تمثل طريقة المركز الثاني للمهمة، وفريق مناهضين.في حين يعتمد نهج واحد على الجمع بين أساليب التضمين المختلفة لاستخراج التمثيلات الدلالية والمنظمات المختلفة للكلمات في السياق؛يستخدم الآخر بيانات إضافية مع التدريب الذاتي المخصص قليلا، وهي تقنية تعليمية شبه إشراف، لمشاكل علامات التسلسل.يستفيد كل من بهيئاتنا نموذجا قويا لغة قوية، والتي تم ضبطها بشكل جيد على مهمة تصنيف سامة.على الرغم من أن الأدلة التجريبية تشير إلى فعالية أعلى من النهج الأول من المرتبة الثانية، فإن الجمع بينها يؤدي إلى أفضل النتائج لدينا من 70.77 F1 النتيجة على اختبار DataSet.
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 method for the task, an ensemble of two approaches. While one approach relies on combining different embedding methods to extract diverse semantic and syntactic representations of words in context; the other utilizes extra data with a slightly customized Self-training, a semi-supervised learning technique, for sequence tagging problems. Both of our architectures take advantage of a strong language model, which was fine-tuned on a toxic classification task. Although experimental evidence indicates higher effectiveness of the first approach than the second one, combining them leads to our best results of 70.77 F1-score on the test dataset.
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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
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