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

Published in EACL-2017, 2017

Recommended citation: Guo, Shangmin, Xiangrong Zeng, Shizhu He, Kang Liu, and Jun Zhao. "Which is the Effective Way for Gaokao: Information Retrieval or Neural Networks?." In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pp. 111-120. 2017. https://www.aclweb.org/anthology/E17-1011

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 Questions(GKHMC) and proposed two different approaches to address them sing 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.

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