| 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 | 1175 | 2017 |
| 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 | 1019 | 2011 |
| Comparative study of cnn and rnn for natural language processing W Yin, K Kann, M Yu, H Schütze arXiv preprint arXiv:1702.01923, 2017 | 471 | 2017 |
| Improving lexical embeddings with semantic knowledge M Yu, M Dredze Proceedings of the 52nd Annual Meeting of the Association for Computational …, 2014 | 312 | 2014 |
| R3: Reinforced Ranker-Reader for Open-Domain Question Answering S Wang, M Yu, X Guo, Z Wang, T Klinger, W Zhang, S Chang, G Tesauro, ... Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 193 | 2018 |
| Factor-based compositional embedding models M Yu, M Gormley, M Dredze NIPS Workshop on Learning Semantics, 95-101, 2014 | 172* | 2014 |
| 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 | 159 | 2017 |
| 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 | 139 | 2017 |
| Simple question answering by attentive convolutional neural network W Yin, M Yu, B Xiang, B Zhou, H Schütze arXiv preprint arXiv:1606.03391, 2016 | 118 | 2016 |
| 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 | 103 | 2018 |
| 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 | 100 | 2018 |
| Leveraging sentencelevel information with encoder lstm for natural language understanding G Kurata, B Xiang, B Zhou, M Yu arXiv preprint arXiv:1601.01530, 2016 | 100* | 2016 |
| 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 | 82* | 2016 |
| Diverse few-shot text classification with multiple metrics M Yu, X Guo, J Yi, S Chang, S Potdar, Y Cheng, G Tesauro, H Wang, ... arXiv preprint arXiv:1805.07513, 2018 | 80 | 2018 |
| Learning composition models for phrase embeddings M Yu, M Dredze Transactions of the Association for Computational Linguistics 3, 227-242, 2015 | 73 | 2015 |
| Accelerated mini-batch randomized block coordinate descent method T Zhao, M Yu, Y Wang, R Arora, H Liu Advances in neural information processing systems 27, 3329-3337, 2014 | 63 | 2014 |
| A co-matching model for multi-choice reading comprehension S Wang, M Yu, S Chang, J Jiang arXiv preprint arXiv:1806.04068, 2018 | 54 | 2018 |
| Evidence Integration for Multi-hop Reading Comprehension with Graph Neural Networks L Song, Z Wang, M Yu, Y Zhang, R Florian, D Gildea IEEE Transactions on Knowledge and Data Engineering, 2020 | 51* | 2020 |
| Cross-lingual knowledge graph alignment via graph matching neural network K Xu, L Wang, M Yu, Y Feng, Y Song, Z Wang, D Yu arXiv preprint arXiv:1905.11605, 2019 | 47 | 2019 |
| Dag-gnn: Dag structure learning with graph neural networks Y Yu, J Chen, T Gao, M Yu arXiv preprint arXiv:1904.10098, 2019 | 44 | 2019 |
SHIYU CHANGMIT-IBM Watson AI LabVerified email at ibm.com
