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Register a new userCorrecting Texts Generated by Transformers using Discourse Features and Web Mining
تصحيح النصوص الناتجة عن المحولات باستخدام ميزات الخطاب وتعدين الويب
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يمكن أن تولد الأساليب الحديثة القائمة على المحولات إلى NLG مثل GPT-2 إنشاء نصوص أصلية متماسكة بشكل ملائم.ومع ذلك، فإن هذه النصوص التي تم إنشاؤها لها عيوب خطيرة: خطاب عالمي يتعارض مع الجمل من حيث قيم الكيان.نحن نتناول كل من هذه العيوب: أنها مستقلة ولكن يمكن دمجها لتوليد النصوص الأصلية التي ستكون متسقة وصادقة.تقدم هذه الورقة نهجا لتقدير جودة هيكل الخطاب.تؤكد النتائج التجريبية أن هيكل الخطاب للنصوص التي تم إنشاؤها حاليا غير دقيق.نقترح اتجاهات البحث لتصحيحه باستخدام ميزات الخطاب أثناء إجراء ضبط الدقيقة.النهج المقترح عالمي ويمكن تطبيقه على لغات مختلفة.بصرف النظر عن ذلك، نقترح طريقة لتصحيح قيم الكيان الخاطئة استنادا إلى تعدين الويب ومحاذاة النص.
Recent transformer-based approaches to NLG like GPT-2 can generate syntactically coherent original texts. However, these generated texts have serious flaws: global discourse incoherence and meaninglessness of sentences in terms of entity values. We address both of these flaws: they are independent but can be combined to generate original texts that will be both consistent and truthful. This paper presents an approach to estimate the quality of discourse structure. Empirical results confirm that the discourse structure of currently generated texts is inaccurate. We propose the research directions to correct it using discourse features during the fine-tuning procedure. The suggested approach is universal and can be applied to different languages. Apart from that, we suggest a method to correct wrong entity values based on Web Mining and text alignment.
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