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Register a new userGenerative Adversarial Networks based on Mixed-Attentions for Citation Intent Classification in Scientific Publications
شبكات الخصومة التوليدية بناء على انتباه مختلطة لتصنيف نية الاقتباس في المنشورات العلمية
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نقترح شبكة الخصومة المولدة المخلوذة التي تعتمد على الانتباه (باسم Magan)، وتطبيقها على تصنيف نية الاقتباس في المنشور العلمي.نقوم باختيار بيانات التدريب الخاصة بالمجال، واقترح آلية اهتمامية مختلطة، وتوظيف بنية شبكة الخصومة التوليدية لنموذج لغة التدريب المسبق والضبط الجميل لمهمة التصنيف متعددة الطبقات المصب.أجريت التجارب على مجموعات البيانات SCICITE لمقارنة الأداء النموذجي.حقق نموذج Magan المقترح أفضل ماكرو - F1 من 0.8532.
We propose the mixed-attention-based Generative Adversarial Network (named maGAN), and apply it for citation intent classification in scientific publication. We select domain-specific training data, propose a mixed-attention mechanism, and employ generative adversarial network architecture for pre-training language model and fine-tuning to the downstream multi-class classification task. Experiments were conducted on the SciCite datasets to compare model performance. Our proposed maGAN model achieved the best Macro-F1 of 0.8532.
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This paper describes our system (IREL) for 3C-Citation Context Classification shared task of the Scholarly Document Processing Workshop at NAACL 2021. We participated in both subtask A and subtask B. Our best system achieved a Macro F1 score of 0.269
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