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التحرير النصي حسب الأمر
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نموذج سائد في جيل النص العصبي هو جيل واحد لقطة واحدة، حيث يتم إنتاج النص في خطوة واحدة.ومع ذلك، فإن إعداد طلقة من طلقة غير كافية عندما تكون القيود التي يرغب المستخدم في فرضها على النص الذي تم إنشاؤه ديناميكية، خاصة عند تأليف مستندات أطول.نحن نتطلع إلى هذا القيد مع إعداد جيل نص تفاعلي يتفاعل فيه المستخدم مع النظام عن طريق إصدار الأوامر لتعديل النص الموجود.تحقيقا لهذه الغاية، نقترح مهمة تحرير نصية جديدة، وإدخال Wikidocedits، ومجموعة بيانات تحرير جملة واحدة من Wikipedia.نظرا لأن محررنا التفاعلي، وهو نموذج يستند إلى المحولات التي تم تدريبها على مجموعة البيانات هذه، تتفوق على خطوط الأساس والحصول على نتائج إيجابية في كل من التقييمات التلقائية والإنسانية.نقدم تحليلات تجريبية ونوعية لأداء هذا النموذج.
A prevailing paradigm in neural text generation is one-shot generation, where text is produced in a single step. The one-shot setting is inadequate, however, when the constraints the user wishes to impose on the generated text are dynamic, especially when authoring longer documents. We address this limitation with an interactive text generation setting in which the user interacts with the system by issuing commands to edit existing text. To this end, we propose a novel text editing task, and introduce WikiDocEdits, a dataset of single-sentence edits crawled from Wikipedia. We show that our Interactive Editor, a transformer-based model trained on this dataset, outperforms baselines and obtains positive results in both automatic and human evaluations. We present empirical and qualitative analyses of this model's performance.
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