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Register a new userIterative GNN-based Decoder for Question Generation
وحدة فك الترميز القائم على GNN لتوليد السؤال
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تهدف توليد السؤال الطبيعي (QG) إلى توليد أسئلة من مقطع، ويتم الرد على الأسئلة التي تم إنشاؤها من المقطع.معظم النماذج مع نموذج الأداء الحديث النص الذي تم إنشاؤه سابقا في كل خطوة فك التشفير.ومع ذلك، (1) يتجاهلون معلومات الهيكل الغني المخفية في النص الذي تم إنشاؤه سابقا.(2) يتجاهلون تأثير الكلمات المنسوخة على مرور.ندرك أن المعلومات في الكلمات التي تم إنشاؤها مسبقا بمثابة معلومات مساعدة في الجيل اللاحق.لمعالجة هذه المشكلات، نقوم بتصميم وحدة فك الترميز المستندة إلى شبكة الرسم البياني للتكرار (IGND) لنموذج الجيل السابق باستخدام شبكة عصبية رسم بيانية في كل خطوة فك التشفير.علاوة على ذلك، يلتقط نموذج الرسم البياني لدينا علاقات التبعية في المقطع الذي يعزز الجيل.توضح النتائج التجريبية أن نموذجنا يتفوق على النماذج الحديثة مع مهام QG على مستوى الجملة على مجموعات بيانات الفريق وماركو.
Natural question generation (QG) aims to generate questions from a passage, and generated questions are answered from the passage. Most models with state-of-the-art performance model the previously generated text at each decoding step. However, (1) they ignore the rich structure information that is hidden in the previously generated text. (2) they ignore the impact of copied words on the passage. We perceive that information in previously generated words serves as auxiliary information in subsequent generation. To address these problems, we design the Iterative Graph Network-based Decoder (IGND) to model the previous generation using a Graph Neural Network at each decoding step. Moreover, our graph model captures dependency relations in the passage that boost the generation. Experimental results demonstrate that our model outperforms the state-of-the-art models with sentence-level QG tasks on SQuAD and MARCO datasets.
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