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Register a new userOn Generating Fact-Infused Question Variations
في توليد الاختلافات في الأسئلة
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لإمكانية النمط الكامل لقدرة الإنسان التي تشبه الإنسان على طرح الأسئلة، يجب أن تكون نماذج توليد السؤال التلقائي (QG) قادرة على إنتاج تعبيرات متعددة من نفس السؤال مع مستويات مختلفة من التفاصيل. لسوء الحظ، لا تتضمن مجموعات البيانات الحالية المتاحة لتعلم QG إعادة الصياغة أو الاختلافات السؤال التي تؤثر على قدرة النموذج على تعلم هذه القدرة. نقدم التنوب، مجموعة بيانات تحتوي على إعادة كتابة من الحقائق التي تم إنشاؤها من قبل الإنسان من بيانات الفريق المستخدمة على نطاق واسع لمعالجة هذا القيد. تم الحصول على أسئلة في التنوب عن طريق الجمع بين سؤال معين مع حقائق من الكيانات المشار إليها في السؤال. نحن ندرس نموذج فك التشفير المزدوج ومولد السؤال المحدد لحقيقة (FIQG)، لتعلم إنشاء أسئلة غير ضيقة من الواقع من سؤال معين. تظهر النتائج التجريبية أن FIQG يشتمل بفعالية على معلومات من الحقائق لإضافة المزيد من التفاصيل لسؤال معين. إلى حد علمنا، لدينا هي الدراسة الأولى لتقديم ضخ الحقائق كأشكال جديدة من إعادة صياغة الأسئلة.
To fully model human-like ability to ask questions, automatic question generation (QG) models must be able to produce multiple expressions of the same question with different levels of detail. Unfortunately, existing datasets available for learning QG do not include paraphrases or question variations affecting a model's ability to learn this capability. We present FIRS, a dataset containing human-generated fact-infused rewrites of questions from the widely-used SQuAD dataset to address this limitation. Questions in FIRS were obtained by combining a given question with facts of entities referenced in the question. We study a double encoder-decoder model, Fact-Infused Question Generator (FIQG), for learning to generate fact-infused questions from a given question. Experimental results show that FIQG effectively incorporates information from facts to add more detail to a given question. To the best of our knowledge, ours is the first study to present fact-infusion as a novel form of question paraphrasing.
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