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Register a new userTransformer-based Multi-Task Learning for Adverse Effect Mention Analysis in Tweets
التعلم متعدد المهام المستندة إلى المحول بالنسبة للتأثير السلبي تذكر التحليل في تغريدات
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تقدم هذه الورقة مساهمتنا في تعدين وسائل التواصل الاجتماعي للتطبيقات الصحية المهمة المشتركة 2021. لقد عالجنا جميع المهام الفرعية الثلاثة للمهمة 1: الفرعية (تصنيف التغريدات التي تحتوي على آثار ضارة)، SubTask B (استخراج يمتد النص الذي يحتوي على آثار ضارة) وSubTask C (دقة الآثار الضارة).استكشفنا العديد من نماذج اللغة القائمة على المحولات المدربة مسبقا وركزنا على بنية تدريب متعددة المهام.بالنسبة للسبع الأول، طبقنا أيضا تقنيات تكبير الخصومة وتشكل مجموعة نموذجية من أجل تحسين متانة التنبؤ.مرتبة نظامنا في المرتبة الأولى في SubTask B مع 0.51 F1 درجة، 0.514 الدقة واستدعاء 0.514.للحصول على التراكج الفرعية، حصلنا على درجة 0.44 F1، 0.49 دقة و 0.39 استدعاء و For Subtask C حصلنا على 0.16 F1 درجة مع 0.16 دقة و 0.17 تذكر.
This paper presents our contribution to the Social Media Mining for Health Applications Shared Task 2021. We addressed all the three subtasks of Task 1: Subtask A (classification of tweets containing adverse effects), Subtask B (extraction of text spans containing adverse effects) and Subtask C (adverse effects resolution). We explored various pre-trained transformer-based language models and we focused on a multi-task training architecture. For the first subtask, we also applied adversarial augmentation techniques and we formed model ensembles in order to improve the robustness of the prediction. Our system ranked first at Subtask B with 0.51 F1 score, 0.514 precision and 0.514 recall. For Subtask A we obtained 0.44 F1 score, 0.49 precision and 0.39 recall and for Subtask C we obtained 0.16 F1 score with 0.16 precision and 0.17 recall.
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