Abstract
Multiple choice questions from university admission exams (Gaokao in Chinese) is a challenging AI task since it requires effective representation to capture complicated semantic relations between sentences in the article and strong ability to handle long text. Face the above challenges, we propose a key-elements graph to enhance context semantic representation and a comprehensive evidence extraction method inspired by existing methods. Our model first extracts evidence sentences from a passage according to the corresponding question and options to reduce the impact of noise. Then combines syntactic analysis techniques with graph neural network to construct the key-elements graph bases on the extracted sentences. Finally, fusing the learned graph nodes representation into context representation to enhancing syntactic information. Experiments on Gaokao Chinese multiple-choice dataset demonstrate the proposed model obtains substantial performance gains over various neural model baselines in terms of accuracy.
Supported by the National Key Research and Development Program of China (No. 2018YFB1005103) and the National Natural Science Foundation of China (No. 61772324) and the Postgraduate Education Innovation Project of Shanxi Province (No. 2020SY019).
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Notes
- 1.
Datasets and codes are available on https://github.com/jfzy-lab/GCRC.
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Wang, X., Ji, Y., Li, R. (2020). Key-Elements Graph Constructed with Evidence Sentence Extraction for Gaokao Chinese. In: Zhu, X., Zhang, M., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2020. Lecture Notes in Computer Science(), vol 12431. Springer, Cham. https://doi.org/10.1007/978-3-030-60457-8_33
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