Semantics-based Question Generation and Implementation
Semantics-based Question Generation and Implementation
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DOI：
10.5087/dad.2012.202
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Xuchen Yao;G. Bouma;Yi Zhang
中科院分区：
文献类型：
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作者：
Xuchen Yao;G. Bouma;Yi Zhang
提出了一种基于语义重写方法的问题生成系统。使用最先进的深度语言解析和生成工具将自然语言句子以最小递归语义（mrs）的形式映射到它们的意义表示，反之亦然。通过对语义结构的仔细操作，我们获得了一种有原则的生成问题的方法，避免了对句法结构的特殊操作。基于对句子意义的（部分）理解，系统生成具有语义基础和目的的问题。由于生成器使用深度语言语法，生成结果的语法性受到语法的许可。通过专门的排序模型，将通用生成模型的语言实现进一步细化到问题生成任务。QGSTEC2010的评估结果显示了该方法的良好效果。
This paper presents a question generation system based on the approach of semantic rewriting. State-of-the-art deep linguistic parsing and generation tools are employed to map natural language sentences into their meaning representations in the form of Minimal Recursion Semantics ( mrs ) and vice versa. By carefully operating on the semantic structures, we obtain a principled way of generating questions which avoids ad-hoc manipulation of syntactic structures. Based on the (partial) understanding of the sentence meaning, the system generates questions that are semantically grounded and purposeful. As the generator uses a deep linguistic grammar, the grammaticality of the generation results is licensed by the grammar. With a specialized ranking model, the linguistic realizations from the general purpose generation model are further refined for the question generation task. The evaluation results from QGSTEC2010 show promising results for the proposed approach.