基于文章和近答案句信息的问题生成模型

Question Generation Based on Paragraph and Close-Answer Context

自动问题生成任务旨在给文章中的一段文本生成相应的自然语言的问句,该研究在问答系统和语音助手的对话系统中有重要作用,可以帮助它们启动对话和继续对话。目前的神经网络问题生成模型主要是将包含答案的句子或者整篇文章作为模型的输入,而这些方法存在语义表示不能很好地结合句子和文章信息的问题。因此该文提出多输入层次注意力序列到序列的问题生成网络,能更好地利用文章和答案上下文的两重信息。模型通过关注对答案更有价值的句子信息和全文更丰富的语义信息来生成高质量的问题。在公开数据集SQuAD上的问题生成对比实验表明,该方法在BLEU4值上表现优越。通过训练好的问答系统来评估问题的可回答率,明显优于基准系统。

The automatic question generation task aims to generate natural language questions for a paragraph of text.Existing neural question generation models mainly focused on using one sentence or the whole paragraph with target answer as the input.To better utilize the context of the target answer,this paper proposes a multi-input hierarchical attention sequence to sequence network to capture more valuable sentence information and richer semantic information of paragraph to generate high-quality questions.Experiments on SQuAD show that our method is better than the state-of-the-art in terms of BLEU4,and the response rate of this method is obviously better than the baseline system.