實驗結果表示，我們的系統能有效處理文章結構，相比只有問題生成模型的系統，在中文和英文的資料集都有更好的表現。;In recent years, question generation (QG) has developed rapidly. In the past, using rules that are based on syntactic structure to generate questions. Nowadays, the machine can understand semantic and automatically generate appropriate questions with a proven technique of deep learning.
Question generation aims to generate corresponding questions from a given passage and answer. It is similar to machine reading comprehension (RC) task. Therefore, reading comprehension dataset is often used to question generation task. The input of the previous question generation model is the sentence containing the answer rather than the whole article. However, the content of some questions and its answers are not in the same sentence. The question may be based on other information in sentences. Then, our paper proposed a new framework which consists of sentence pair model and question generation model. Using the sentence pair model to process article structure. Its method is matching each sentence and the sentence containing the answer to compute the respective degree of correlation to reweight sentences and then produce questions by question generation model. The main purpose of sentence pair model is to automatically find the content related to the answer from the article.
Experiment results show that our system can handle article structure. In contrast to a system with only question generation model, our system has better performance in Chinese and English dataset.
