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Register a new userSelf-Contextualized Attention for Abusive Language Identification
اهتمام السياق الذاتي لتحديد اللغة المسيئة
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أصبح استخدام آليات الاهتمام في أساليب التعلم العميق شعبية في معالجة اللغة الطبيعية بسبب أدائه المعلقة. يسمح باستخدام هذه الآليات إلى إحدى الأهمية لإدارة أهمية عناصر التسلسل وفقا لسياقها، ومع ذلك، فقد تمت ملاحظتها هذه الأهمية بشكل مستقل بين أزواج عناصر التسلسل (اهتمام الذات) وبين مجال تطبيق التسلسل (الاهتمام السياقي)، مما يؤدي إلى فقد المعلومات ذات الصلة والحد من تمثيل التسلسلات. لمعالجة هذه القضايا الخاصة هذه نقترح آلية الاهتمام الذاتي الذاتي، والتي تتداول قبالة القيود السابقة، من خلال النظر في العلاقات الداخلية والسياقية بين عناصر التسلسل. تم تقييم الآلية المقترحة في أربع مجموعات قياسية لتحقيق مهمة تحديد اللغة المسيئة لتحقيق النتائج المشجعة. تفوقت على آليات الاهتمام الحالية وأظهرت أداء تنافسي فيما يتعلق بالنهج الحديثة من بين الفن.
The use of attention mechanisms in deep learning approaches has become popular in natural language processing due to its outstanding performance. The use of these mechanisms allows one managing the importance of the elements of a sequence in accordance to their context, however, this importance has been observed independently between the pairs of elements of a sequence (self-attention) and between the application domain of a sequence (contextual attention), leading to the loss of relevant information and limiting the representation of the sequences. To tackle these particular issues we propose the self-contextualized attention mechanism, which trades off the previous limitations, by considering the internal and contextual relationships between the elements of a sequence. The proposed mechanism was evaluated in four standard collections for the abusive language identification task achieving encouraging results. It outperformed the current attention mechanisms and showed a competitive performance with respect to state-of-the-art approaches.
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