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A list of recent papers about Graph Neural Network methods applied in NLP areas.

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GNN4NLP-Papers

A list of recent papers about GNN methods applied in NLP areas.

Taxonomy

Fundamental NLP Tasks

  1. Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks. Shikhar Vashishth, Manik Bhandari, Prateek Yadav, Piyush Rai, Chiranjib Bhattacharyya and Partha Talukdar. ACL 2019 [pdf] [code]

  2. A Lexicon-Based Graph Neural Network for Chinese NER. Tao Gui, Yicheng Zou and Qi Zhang. EMNLP 2019 [pdf]

Text Classification

  1. Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks. Chen Zhang, Qiuchi Li and Dawei Song. EMNLP 2019 [pdf] [code]

  2. Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification. Linmei Hu, Tianchi Yang, Chuan Shi, Houye Ji, Xiaoli Li. EMNLP 2019 [pdf]

  3. Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks. Binxuan Huang and Kathleen M. Carley. EMNLP 2019 [pdf]

  4. Relational Graph Attention Network for Aspect-based Sentiment Analysis. Kai Wang, Weizhou Shen, Yunyi Yang, Xiaojun Quan, Rui Wang. ACL 2020 [pdf]

Question Answering

  1. BAG: Bi-directional Attention Entity Graph Convolutional Network for Multi-hop Reasoning Question Answering. Yu Cao, Meng Fang and Dacheng Tao. NAACL-HLT 2019. [pdf] [code]

  2. Question Answering by Reasoning Across Documents with Graph Convolutional Networks. Nicola De Cao, Wilker Aziz and Ivan Titov. NAACL-HLT 2019. [pdf]

  3. Cognitive Graph for Multi-Hop Reading Comprehension at Scale. Ming Ding, Chang Zhou, Qibin Chen, Hongxia Yang and Jie Tang. ACL 2019 [pdf] [code]

  4. Dynamically Fused Graph Network for Multi-hop Reasoning. Yunxuan Xiao, Yanru Qu, Lin Qiu, Hao Zhou, Lei Li, Weinan Zhang and Yong Yu. ACL 2019 [pdf]

  5. 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. ACL 2019 [pdf]

  6. DialogueGCN A Graph Convolutional Neural Network for Emotion Recognition in Conversation. Deepanway Ghosal, Navonil Majumder, Soujanya Poria, Niyati Chhaya and Alexander Gelbukh. EMNLP 2019 [pdf]

  7. 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. ACL 2019 [pdf] [code]

  8. Reasoning Over Semantic-Level Graph for Fact Checking. Wanjun Zhong, Jingjing Xu, Duyu Tang, Zenan Xu, Nan Duan, Ming Zhou, Jiahai Wang and Jian Yin. Arxiv 2019 [pdf]

  9. Message Passing for Complex Question Answering over Knowledge Graphs. Svitlana Vakulenko, Javier David Fernandez Garcia, Axel Polleres, Maarten de Rijke, Michael Cochez. CIKM 2019 [pdf]

  10. Knowledge-aware Textual Entailment with Graph Atention Network. Daoyuan Chen , Yaliang Li , Min Yang , Hai-Tao Zheng , Ying Shen. CIKM 2019 [pdf]

  11. Fine-grained Fact Verification with Kernel Graph Attention Network. Zhenghao Liu, Chenyan Xiong, Maosong Sun, Zhiyuan Liu. ACL 2020 [pdf] [code]

Information Extraction

  1. Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks. Ningyu Zhang, Shumin Deng, Zhanlin Sun, Guanying Wang, Xi Chen, Wei Zhang and Huajun Chen. NAACL-HLT 2019. [pdf]

  2. Attention Guided Graph Convolutional Networks for Relation Extraction. Zhijiang Guo, Yan Zhang and Wei Lu. ACL 2019 [pdf] [code]

  3. Graph Neural Networks with Generated Parameters for Relation Extraction. Hao Zhu, Yankai Li