Multi-task joint training model for machine reading comprehension

Author:

Li Fangfang,Shan Youran,Mao Xingliang,Ren Xingkai,Liu Xiyao,Zhang Shichao

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference42 articles.

1. Gated self-matching networks for reading comprehension and question answering;Wang,2017

2. X. Liu, W. Li, Y. Fang, A. Kim, K. Duh, J. Gao, Stochastic answer networks for squad 2.0, arXiv preprint arXiv:1809.09194.

3. M. Hu, F. Wei, Y. Peng, Z. Huang, N. Yang, D. Li, Read+ verify: Machine reading comprehension with unanswerable questions, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33, 2019, pp. 6529–6537.

4. Cjrc: A reliable human-annotated benchmark dataset for chinese judicial reading comprehension;Duan;China National Conference on Chinese Computational Linguistics, Springer,2019

5. Natural questions: a benchmark for question answering research;Kwiatkowski;Trans. Association Computat. Linguist.,2019

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