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Which is the Effective Way for Gaokao: Information Retrieval or Neural Networks?

机译:哪个是高高调的有效方式:信息检索或神经网络?

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As one of the most important test of China, Gaokao is designed to be difficult enough to distinguish the excellent high school students. In this work, we detailed the Gaokao History Multiple Choice Ques-tions(GKHMC) and proposed two different approaches to address them using various resources. One approach is based on entity search technique (IR approach), the other is based on text entailment approach where we specifically employ deep neural networks(NN approach). The result of experiment on our collected real Gaokao questions showed that they are good at different categories of questions, i.e. IR approach performs much better at entity questions(EQs) while NN approach shows its advantage on sentence questions(SQs). Our new method achieves state-of-the-art performance and show that it's indispensable to apply hybrid method when participating in the real-world tests.
机译:作为中国最重要的考验之一,高崎旨在足以区分优秀的高中生。在这项工作中,我们详细介绍了高考历史多种选择QUES - TIOS(GKHMC),并提出了两种不同的方法来使用各种资源来解决它们。一种方法是基于实体搜索技术(IR方法),另一个是基于我们专门采用深神经网络(NN方法)的文本鉴别方法。实验结果对我们收集的真正的荷瓦问题的结果表明,他们擅长不同类别的问题,即IR方法在实体问题(EQS)上表现得更好,而NN方法则显示其在句子问题上的优势(SQS)。我们的新方法实现了最先进的性能,并表明在参与现实世界测试时应用混合方法是必不可少的。

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