Abstract
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 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, that is IR approach performs much better at entity questions(EQs) while NN approach shows its advantage on sentence questions(SQs). We achieve state-of-the-art performance and show that it’s indispensable to apply hybrid method when participating in the real-world tests.- Anthology ID:
- E17-1011
- Volume:
- Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Mirella Lapata, Phil Blunsom, Alexander Koller
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 111–120
- Language:
- URL:
- https://aclanthology.org/E17-1011
- DOI:
- Cite (ACL):
- Shangmin Guo, Xiangrong Zeng, Shizhu He, Kang Liu, and Jun Zhao. 2017. 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, pages 111–120, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Which is the Effective Way for Gaokao: Information Retrieval or Neural Networks? (Guo et al., EACL 2017)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/E17-1011.pdf
- Code
- IACASNLPIR/GKHMC