Reading wikipedia to answer opendomain questions, Proceedings of ACL 2017, pp.1870-1879, 2017. ,
DOI : 10.18653/v1/p17-1171
URL : http://arxiv.org/pdf/1704.00051
Efficient estimation of word representations in vector space (2013). arxiv preprint. arXiv preprint arXiv:1301, pp.1532-1543 ,
Results of the fifth edition of the BioASQ Challenge, BioNLP 2017, pp.48-57, 2017. ,
DOI : 10.18653/v1/W17-2306
Glove: Global Vectors for Word Representation, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp.1532-1543, 2014. ,
DOI : 10.3115/v1/D14-1162
URL : http://nlp.stanford.edu/projects/glove/glove.pdf
SQuAD: 100,000+ Questions for Machine Comprehension of Text, Proceedings of the 2016 Conference on Empirical Methods in Natural
Language Processing, pp.2383-2392, 2016. ,
DOI : 10.18653/v1/D16-1264
An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition, BMC Bioinformatics, vol.25, issue.22, p.138, 2015. ,
DOI : 10.1093/bioinformatics/btp535
URL : https://hal.archives-ouvertes.fr/hal-01156600
Making Neural QA as Simple as Possible but not Simpler, Proceedings of the 21st Conference on Computational Natural Language
Learning (CoNLL 2017), pp.271-280, 2017. ,
DOI : 10.18653/v1/K17-1028
URL : http://arxiv.org/pdf/1703.04816
Neural Domain Adaptation for Biomedical Question Answering, Proceedings of the 21st Conference on Computational Natural Language
Learning (CoNLL 2017), pp.281-289, 2017. ,
DOI : 10.18653/v1/K17-1029
URL : http://arxiv.org/pdf/1706.03610