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Register a new userControlled Neural Sentence-Level Reframing of News Articles
تسيطر الجملة العصبية على مستوى إعادة تصنيع المواد الأخبار
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إن تأطير مقالة إخبارية تعني تصوير الحدث المبلغ عنها من منظور محدد، على سبيل المثال، من منظور اقتصادي أو صحي. Reframing وسائل لتغيير هذا المنظور. اعتمادا على الجمهور أو الحضور، يمكن أن تصادف REFRIMING ضرورية لتحقيق التأثير المرغوب على القراء. يرتبط Reframing بتكييف الأسلوب والشاعر، والتي يمكن معالجة تقنيات توليد النص العصبي. ومع ذلك، فإن الأمر أكثر تحديا لأن تغيير الإطار يتطلب إعادة كتابة الجمل بأكملها بدلا من عبارات واحدة. في هذه الورقة، ندرس كيفية إعادة صياغة الجمل في مقالات إخبارية مع الحفاظ على تماسكها إلى السياق. نتعامل مع REMREMING كمركز ملء على مستوى الجملة الذي نربط النماذج العصبية على كوربوس موجود للإطار الوسائط. لتوجيه التدريب، نقترح ثلاث استراتيجيات: محاكمة اللغة المؤطرة، والحفاظ على الكيانات المسماة، والتعلم الخصم. نقوم بتقييم النماذج المعنية تلقائيا وتدويا من أجل اتساق الموضوع والتماسك والتعداد الناجح. تشير نتائجنا إلى أن إنشاء نص مؤطر بشكل صحيح يعمل بشكل جيد ولكن مع المفاضلات.
Framing a news article means to portray the reported event from a specific perspective, e.g., from an economic or a health perspective. Reframing means to change this perspective. Depending on the audience or the submessage, reframing can become necessary to achieve the desired effect on the readers. Reframing is related to adapting style and sentiment, which can be tackled with neural text generation techniques. However, it is more challenging since changing a frame requires rewriting entire sentences rather than single phrases. In this paper, we study how to computationally reframe sentences in news articles while maintaining their coherence to the context. We treat reframing as a sentence-level fill-in-the-blank task for which we train neural models on an existing media frame corpus. To guide the training, we propose three strategies: framed-language pretraining, named-entity preservation, and adversarial learning. We evaluate respective models automatically and manually for topic consistency, coherence, and successful reframing. Our results indicate that generating properly-framed text works well but with tradeoffs.
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