Question Answering


Here we include the list of available datasets as well as important publications on question answering via artificial neural networks.


Publications


Architecture:Albert
Year:2019
Paper:Lan, Zhenzhong, et al. “Albert: A lite bert for self-supervised learning of language representations.” arXiv preprint arXiv:1909.11942 (2019).
Architecture:XLNet
Year:2019
Paper:Yang, Zhilin, et al. “Xlnet: Generalized autoregressive pretraining for language understanding.” Advances in neural information processing systems. 2019.
Architecture:BERT
Year:2018
Paper:Devlin, Jacob, et al. “Bert: Pre-training of deep bidirectional transformers for language understanding.” arXiv preprint arXiv:1810.04805 (2018).
Architecture:RM-Reader
Year:2018
Paper:Hu, Minghao, et al. “Reinforced Mnemonic Reader for Machine Reading Comprehension.” arXiv preprint arXiv:1705.02798 (2017).
Architecture:FusionNet
Year:2017
Paper:Huang, Hsin-Yuan, et al. “Fusionnet: Fusing via fully-aware attention with application to machine comprehension.” arXiv preprint arXiv:1711.07341 (2017).
Architecture:DocumentReader
Year:2017
Paper:Chen, Danqi, et al. “Reading wikipedia to answer open-domain questions.” arXiv preprint arXiv:1704.00051 (2017).
Architecture:ReasonNet
Year:2017
Paper:Shen, Yelong, et al. “Reasonet: Learning to stop reading in machine comprehension.” Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2017.
Architecture:Match-LSTM
Year:2016
Paper:Wang, Shuohang, and Jing Jiang. “Machine comprehension using match-lstm and answer pointer.” arXiv preprint arXiv:1608.07905 (2016).