Fetching the paper…
Reading the bibliography…
Recently Graph Neural Network (GNN) has been applied successfully to various NLP tasks that require reasoning, such as multi-hop machine reading comprehension.
Coarse-grain fine-grain coattention network for multi-evidence question answering
Victor Zhong, Caiming Xiong, Nitish Shirish Keskar, and Richard Socher. 2019 · 1901
Earlier work this paper cites.
Compositional questions do not necessitate multi-hop reasoning
Sewon Min, Eric Wallace, Sameer Singh, Matt Gardner, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2019 · 1906
Earlier work this paper cites.
Hierarchical graph network for multi-hop question answering
Yuwei Fang, Siqi Sun, Zhe Gan, Rohit Pillai, Shuohang Wang, and Jingjing Liu. 2019 · 1911
Earlier work this paper cites.
Select, answer and explain: Interpretable multi-hop reading comprehension over multiple documents
Ming Tu, Kevin Huang, Guangtao Wang, Jing Huang, Xiaodong He, and Bowen Zhou. 2019a · 1911
Earlier work this paper cites.
Graph convolutional encoders for syntax-aware neural machine translation
Joost Bastings, Ivan Titov, Wilker Aziz, Diego Marcheggiani, and Khalil Simaan. 2017 · 1967
Earlier work this paper cites.
Improved semantic representations from tree-structured long short-term memory networks
Kai Sheng Tai, Richard Socher, and Christopher D Manning. 2015 · 2015
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2016 · 2016
Earlier work this paper cites.
Bidirectional attention flow for machine comprehension
Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2016 · 2016
Earlier work this paper cites.
Dynamic coattention networks for question answering
Caiming Xiong, Victor Zhong, and Richard Socher. 2016 · 2016
Earlier work this paper cites.
Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017 · 2017
Earlier work this paper cites.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
Cited alongside, same era.
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017 · 2017
Cited alongside, same era.
Beyond grids: Learning graph representations for visual recognition
Yin Li and Abhinav Gupta. 2018 · 2018
Cited alongside, same era.
Exploiting semantics in neural machine translation with graph convolutional networks
Diego Marcheggiani, Joost Bastings, and Ivan Titov. 2018 · 2018
Cited alongside, same era.
Modeling relational data with graph convolutional networks
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling. 2018 · 2018
Cited alongside, same era.
Graph convolution over pruned dependency trees improves relation extraction
Yuhao Zhang, Peng Qi, and Christopher D Manning. 2018b · 2018
Later among the works it cites.
Modeling polypharmacy side effects with graph convolutional networks
Marinka Zitnik, Monica Agrawal, and Jure Leskovec. 2018 · 2018
Later among the works it cites.
Question answering by reasoning across documents with graph convolutional networks
Nicola De Cao, Wilker Aziz, and Ivan Titov. 2019 · 2019
Later among the works it cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Later among the works it cites.
Graph neural networks for social recommendation
Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin. 2019 · 2019
Later among the works it cites.
Incorporating syntactic and semantic information in word embeddings using graph convolutional networks
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018 · 2018
Cited alongside, same era.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2018 · 2018
Cited alongside, same era.
Hotpotqa: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D Manning. 2018 · 2018
Cited alongside, same era.
Graph convolutional networks for text classification
Liang Yao, Chengsheng Mao, and Yuan Luo. 2018 · 2018
Cited alongside, same era.
Multi-hop reading comprehension across multiple documents by reasoning over heterogeneous graphs
Ming Tu, Guangtao Wang, Jing Huang, Yun Tang, Xiaodong He, and Bowen Zhou. 2019b
Cited in the paper.
Sentence-state lstm for text representation
Yue Zhang, Qi Liu, and Linfeng Song. 2018a
Cited in the paper.
Shikhar Vashishth, Manik Bhandari, Prateek Yadav, Piyush Rai, Chiranjib Bhattacharyya, and Partha Talukdar. 2019 · 2019
Later among the works it cites.
Dynamically fused graph network for multi-hop reasoning
Yunxuan Xiao, Yanru Qu, Lin Qiu, Hao Zhou, Lei Li, Weinan Zhang, and Yong Yu. 2019 · 2019
Later among the works it cites.
Aspect-based sentiment classification with aspect-specific graph convolutional networks
Chen Zhang, Qiuchi Li, and Dawei Song. 2019 · 2019
Later among the works it cites.
Gear: Graph-based evidence aggregating and reasoning for fact verification
Jie Zhou, Xu Han, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun. 2019 · 2019
Later among the works it cites.