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Register a new userPlan-then-Generate: Controlled Data-to-Text Generation via Planning
خطة - ثم توليد: جيل البيانات للتحكم في النص عبر التخطيط
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أدت التطورات الأخيرة في الشبكات العصبية إلى التقدم في توليد البيانات إلى النص.ومع ذلك، فإن الافتقار إلى قدرة النماذج العصبية للسيطرة على هيكل الإخراج الذي تم إنشاؤه يمكن أن يحد في بعض تطبيقات العالم الحقيقي.في هذه الدراسة، نقترح إطارا جديدا لخطة الرواية (Plangen) لتحسين قابلية تحكم نماذج البيانات النصية العصبية.يتم إجراء تجارب واسعة من التجارب والتحليلات على مجموعة من مجموعات البيانات القياسية، Totto و Webnlg.تظهر النتائج أن نموذجنا قادر على التحكم في كل من الجملة داخل الجملة وبنية الجملة بين الإخراج الناتج.علاوة على ذلك، تظهر المقارنات التجريبية ضد الأساليب السابقة من الأساليب السابقة أن نموذجنا يحسن جودة التوليد وكذلك تنوع الإخراج عند الحكم على التقييمات البشرية والآلية.
Recent developments in neural networks have led to the advance in data-to-text generation. However, the lack of ability of neural models to control the structure of generated output can be limiting in certain real-world applications. In this study, we propose a novel Plan-then-Generate (PlanGen) framework to improve the controllability of neural data-to-text models. Extensive experiments and analyses are conducted on two benchmark datasets, ToTTo and WebNLG. The results show that our model is able to control both the intra-sentence and inter-sentence structure of the generated output. Furthermore, empirical comparisons against previous state-of-the-art methods show that our model improves the generation quality as well as the output diversity as judged by human and automatic evaluations.
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Abstract Recent approaches to data-to-text generation have adopted the very successful encoder-decoder architecture or variants thereof. These models generate text that is fluent (but often imprecise) and perform quite poorly at selecting appropriate
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