Mo Yu
TitleCited byYear
Target-dependent twitter sentiment classification
L Jiang, M Yu, M Zhou, X Liu, T Zhao
Proceedings of the 49th Annual Meeting of the Association for Computational …, 2011
A structured self-attentive sentence embedding
Z Lin, M Feng, CN Santos, M Yu, B Xiang, B Zhou, Y Bengio
arXiv preprint arXiv:1703.03130, 2017
Comparative study of CNN and RNN for natural language processing
W Yin, K Kann, M Yu, H Schütze
arXiv preprint arXiv:1702.01923, 2017
Improving lexical embeddings with semantic knowledge
M Yu, M Dredze
Proceedings of the 52nd Annual Meeting of the Association for Computational …, 2014
Factor-based compositional embedding models
M Yu, M Gormley, M Dredze
NIPS Workshop on Learning Semantics, 95-101, 2014
R 3: Reinforced ranker-reader for open-domain question answering
S Wang, M Yu, X Guo, Z Wang, T Klinger, W Zhang, S Chang, G Tesauro, ...
Thirty-Second AAAI Conference on Artificial Intelligence, 2018
Simple question answering by attentive convolutional neural network
W Yin, M Yu, B Xiang, B Zhou, H Schütze
arXiv preprint arXiv:1606.03391, 2016
Dilated recurrent neural networks
S Chang, Y Zhang, W Han, M Yu, X Guo, W Tan, X Cui, M Witbrock, ...
Advances in Neural Information Processing Systems, 77-87, 2017
End-to-end answer chunk extraction and ranking for reading comprehension
Y Yu, W Zhang, K Hasan, M Yu, B Xiang, B Zhou
arXiv preprint arXiv:1610.09996, 2016
Leveraging sentencelevel information with encoder lstm for natural language understanding
G Kurata, B Xiang, B Zhou, M Yu
arXiv preprint arXiv:1601.01530, 2016
Improved neural relation detection for knowledge base question answering
M Yu, W Yin, KS Hasan, C Santos, B Xiang, B Zhou
arXiv preprint arXiv:1704.06194, 2017
Learning composition models for phrase embeddings
M Yu, M Dredze
Transactions of the Association for Computational Linguistics 3, 227-242, 2015
Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering
S Wang, M Yu, J Jiang, W Zhang, X Guo, S Chang, Z Wang, T Klinger, ...
International Conference on Learning Representations (ICLR), 2018
Accelerated mini-batch randomized block coordinate descent method
T Zhao, M Yu, Y Wang, R Arora, H Liu
Advances in neural information processing systems, 3329-3337, 2014
Image super-resolution via dual-state recurrent networks
W Han, S Chang, D Liu, M Yu, M Witbrock, TS Huang
Proceedings of the IEEE conference on computer vision and pattern …, 2018
Argo: intelligent advertising by mining a user's interest from his photo collections
XJ Wang, M Yu, L Zhang, R Cai, WY Ma
Proceedings of the third international workshop on data mining and audience …, 2009
Compound embedding features for semi-supervised learning
M Yu, T Zhao, D Dong, H Tian, D Yu
Proceedings of the 2013 Conference of the North American Chapter of the …, 2013
Combining Word Embeddings and Feature Embeddings for Fine-grained Relation Extraction
M Yu, M Gormley, M Dredze
North American Chapter of the Association for Computational Linguistics …, 2015
Locally training the log-linear model for SMT
L Liu, H Cao, T Watanabe, T Zhao, M Yu, C Zhu
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural …, 2012
Exploring graph-structured passage representation for multi-hop reading comprehension with graph neural networks
L Song, Z Wang, M Yu, Y Zhang, R Florian, D Gildea
arXiv preprint arXiv:1809.02040, 2018
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