高考是综合评估人类知识和能力水平的标准化考试,与传统的自动问答任务相比其挑战性更高。该文面向我国高考试题历史部分,基于深度神经网络技术,构建了历史科目试题自动答题系统。在答题系统中融合知识的一个主要挑战是知识的上下文相关性: 对于一个问题,在知识库存储的大量知识中,只有少数知识与回答该问题相关。针对这一挑战,该文设计了一种结合知识检索与机器阅读理解的知识融合自动答题系统。该系统利用知识检索的相关排序能力和机器阅读理解模型的知识定位能力,有效地发现问题相关的知识,从而增强自动答题的效果。实验结果显示,该系统可有效地作答高考历史科目试题。
Abstract
The National College Entrance Examination is a standardized test that comprehensively evaluates the level of human knowledge and abilities, which serves as a more challenging question answering task. This paper designs an automatic question answering system for the history subject of National College Entrance Examination based on deep neural networks. One of the challenges for knowledge-enhanced question answering is the contextual sensitivity of knowledge: among the large amount of knowledge stored in the knowledge base, only a few pieces of knowledge are relevant to answering a certain question. In response to this challenge, this paper designs a knowledge-enhanced question answering system that combines knowledge retrieval and machine reading comprehension. Through the relevance ranking ability of the knowledge retrieval system and the knowledge positioning ability of the machine reading comprehension model, knowledge related to the question can be effectively discovered, thereby enhancing the performance of the question answering system. The experimental results show that the system can effectively answer questions in the history subject of National College Entrance Examination.
关键词
自动答题 /
机器阅读理解 /
知识检索
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Key words
automatic question answering /
machine reading comprehension /
knowledge retrieval
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参考文献
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脚注
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基金
国家重点研究与发展计划项目(2018YFB1005100)
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