200+篇Graph4NLP文献集：图深度学习在NLP任务的应用
大家好，我是kaiyuan
刷gayhub发现的好东西，当然第一时间分享给朋友们啦！一份图深度学习自然语言处理文献集，详细梳理了NLP十二项任务中，图神经网络的具体应用。
来看看具体的内容吧！记得收藏，顺手点赞 
内容目录
1. Knowledge Graph/Knowledge Base
Knowledge Graph Embedding
Year: 2020
[ACL-20] ReInceptionE: Relation-Aware Inception Network with Joint Local-Global Structural Information for Knowledge Graph Embedding[1]
Year: 2019
[IJCAI-19] A Vectorized Relational Graph Convolutional Network for Multi-Relational Network Alignment[2]
[AAAI-19] Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embedding[3]
[ACL-19] Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs[4]
[ACL-19] A2N: Attending to Neighbors for Knowledge Graph Inference[5]
[NAACL-19] Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks[6]
[EMNLP-19] CaRe: Open Knowledge Graph Embeddings[7]
Knowledge Base Completion
Year: 2020
[ICLR-20] DYNAMICALLY PRUNED MESSAGE PASSING NETWORKS FOR LARGE-SCALE KNOWLEDGE GRAPH REASONING[8]
[ICML-20] Inductive Relation Prediction by Subgraph Reasoning[9]
[ICML-20] Inductive Relation Prediction by Subgraph Reasoning[10]
[EMNLP-20] TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion[11]
[AAAI-20] Commonsense Knowledge Base Completion with Structural and Semantic Context[12]
[AAAI-20] Relational Graph Neural Network with Hierarchical Attention for Knowledge Graph Completion[13]
Year: 2019
[IJCAI-19] Robust Embedding with Multi-Level Structures for Link Prediction[14]
[AAAI-19] End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion[15]
[ACL-19] Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs[16]
[ACL-19] A2N: Attending to Neighbors for Knowledge Graph Inference[17]
[ACL-19] Multi-Channel Graph Neural Network for Entity Alignment[18]
[ACL-19] Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs[19]
[EMNLP-19] Incorporating Graph Attention Mechanism into Knowledge Graph Reasoning Based on Deep Reinforcement Learning[20]
Year: 2018
[ESWC-18] Modeling Relational Data with Graph Convolutional Networks[21]
Knowledge Graph Alignment
Year: 2020
[EMNLP-20] Knowledge Graph Alignment with Entity-Pair Embedding[22]
[AAAI-20] Knowledge Graph Alignment Network with Gated Multi-Hop Neighborhood Aggregation[23]
[AAAI-20] Coordinated Reasoning for Cross-Lingual Knowledge Graph Alignment[24]
Year: 2019
[IJCAI-19] A Vectorized Relational Graph Convolutional Network for Multi-Relational Network Alignment[25]
[IJCAI-19] Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs[26]
[ACL-19] Multi-Channel Graph Neural Network for Entity Alignment[27]
[ACL-19] Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network[28]
[EMNLP-19] Aligning Cross-Lingual Entities with Multi-Aspect Information[29]
[EMNLP-19] Semi-supervised Entity Alignment via Joint Knowledge Embedding Model and Cross-graph Model[30]
[EMNLP-19] Jointly Learning Entity and Relation Representations for Entity Alignment[31]
Year: 2018
[EMNLP-18] Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks[32]
2. Information Extraction
Named-entity Recognition
Year: 2020
[ACL-20] Bipartite Flat-Graph Network for Nested Named Entity Recognition[33]
Year: 2019
[ACL-19] GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction[34]
[ACL-19] A Neural Multi-digraph Model for Chinese NER with Gazetteers[35]
[NAACL-19] A General Framework for Information Extraction using Dynamic Span Graphs[36]
[EMNLP-19] Leverage Lexical Knowledge for Chinese Named Entity Recognition via Collaborative Graph Network[37]
[EMNLP-19] A Lexicon-Based Graph Neural Network for Chinese NER[38]
[EMNLP-19] Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks[39]
[EMNLP-19] Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks[40]
Relation Extraction
Year: 2020
[ICML-20] Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs[41]
[EMNLP-20] Double Graph Based Reasoning for Document-level Relation Extraction[42]
[ACL-20] Structural Information Preserving for Graph-to-Text Generation[43]
[ACL-20] Transition-based Directed Graph Construction for Emotion-Cause Pair Extraction[44]
Year: 2019
[ACL-19] GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction[45]
[ACL-19] Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network[46]
[ACL-19] Attention Guided Graph Convolutional Networks for Relation Extraction[47]
[ACL-19] Joint Type Inference on Entities and Relations via Graph Convolutional Networks[48]
[ACL-19] Graph Neural Networks with Generated Parameters for Relation Extraction[49]
[ACL-19] Attention Guided Graph Convolutional Networks for Relation Extraction[50]
[NAACL-19] GraphIE: A Graph-Based Framework for Information Extraction[51]
[NAACL-19] A General Framework for Information Extraction using Dynamic Span Graphs[52]
[NAACL-19] Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks[53]
[EMNLP-19] Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs[54]
Year: 2018
[EMNLP-18] Graph Convolution over Pruned Dependency Trees Improves Relation Extraction[55]
[EMNLP-18] RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information[56]
[EMNLP-18] N-ary Relation Extraction using Graph State LSTM[57]
Event Detection
Year: 2020
[EMNLP-20] Edge-Enhanced Graph Convolution Networks for Event Detection with Syntactic Relation[58]
[EMNLP-20] Event Detection: Gate Diversity and Syntactic Importance Scores for Graph Convolution Neural Networks[59]
Year: 2019
[ACL-19] Encoding Social Information with Graph Convolutional Networks for Political Perspective Detection in News Media[60]
[EMNLP-19] Event Detection with Multi-Order Graph Convolution and Aggregated Attention[61]
Year: 2018
[AAAI-18] Graph Convolutional Networks with Argument-Aware Pooling for Event Detection[62]
[AAAI-18] Graph Convolutional Networks with Argument-Aware Pooling for Event Detection[63]
[EMNLP-18] Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation[64]
Year: 2017
[RANLP-17] Graph-based Event Extraction from Twitter[65]
3. Sequence Labeling
Semantic Role Labeling
Year: 2020
[ArXiv-20] Cross-Lingual Semantic Role Labeling With Model Transfer[66]
[ArXiv-20] Semantic Role Labeling with Heterogeneous Syntactic Knowledge[67]
[EMNLP-20] Graph Convolutions over Constituent Trees for Syntax-Aware Semantic Role Labeling[68]
[ACL-20] Syntax-Aware Opinion Role Labeling with Dependency Graph Convolutional Networks[69]
Year: 2018
[EMNLP-18] A Unified Syntax-aware Framework for Semantic Role Labeling[70]
Year: 2017
[EMNLP-17] Encoding Sentences with Graph Convolutional Networks[71]
4. Natural Language Generation
Machine Translation
Year: 2021
[AAAI-21] Document Graph for Neural Machine Translation[72]
Year: 2020
[COLING-20] Knowledge Graph Enhanced Neural Machine Translation via Multi-task Learning on Sub-entity Granularity[73]
[IJCAI-20] Knowledge Graphs Enhanced Neural Machine Translation[74]
[TACL-20] AMR-To-Text Generation with Graph Transformer[75]
[ACL-20] Heterogeneous Graph Transformer for Graph-to-Sequence Learning[76]
[ACL-20] A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation[77]
[AAAI-20] Graph Transformer for Graph-to-Sequence Learning[78]
Year: 2019
[ACL-19] Lattice-Based Transformer Encoder for Neural Machine Translation[79]
[NAACL-19] Text Generation from Knowledge Graphs with Graph Transformers[80]
[TACL-19] Semantic neural machine translation using AMR[81]
[TACL-19] Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning[82]
Year: 2018
[AAAI-18] Graph Based Translation Memory for Neural Machine Translation[83]
[NAACL-18] Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks[84]
[ACL-18] Graph-to-Sequence Learning using Gated Graph Neural Networks[85]
Year: 2017
[EMNLP-17] Graph Convolutional Encoders for Syntax-aware Neural Machine Translation[86]
[EMNLP-17] Neural Machine Translation with Source-Side Latent Graph Parsing[87]
[EACL-17] Context-Aware Graph Segmentation for Graph-Based Translation[88]
Summarization
Year: 2020
[COLING-20] Improving Abstractive Dialogue Summarization with Graph Structures and Topic Words[89]
[COLING-20] Enhancing Extractive Text Summarization with Topic-Aware Graph Neural Networks[90]
[EMNLP-20] Neural Extractive Summarization with Hierarchical Attentive Heterogeneous Graph Network[91]
[EMNLP-20] Summarizing Chinese Medical Answer with Graph Convolution Networks and Question-focused Dual Attention[92]
[ACL-20] Discourse-Aware Neural Extractive Text Summarization[93]
[ACL-20] Discourse-Aware Neural Extractive Text Summarization[94]
[ACL-20] Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward[95]
[ACL-20] Leveraging Graph to Improve Abstractive Multi-Document Summarization[96]
[ACL-20] Heterogeneous Graph Neural Networks for Extractive Document Summarization[97]
[AAAI-20] SemSUM: Semantic Dependency Guided Neural Abstractive Summarization[98]
Year: 2018
[COLING-18] Abstract Meaning Representation for Multi-Document Summarization[99]
Year: 2017
[ACL-17] Abstractive document summarization with a graph-based attentional neural model[100]
[COLING-17] Graph-based Neural Multi-Document Summarization[101]
Code Summarization
Year: 2020
[ArXiv-20] Improved code summarization via a graph neural network[102]
Year: 2019
[ICLR-19] Structured Neural Summarization[103]
Year: 2018
[ICLR-18] learning to represent programs with graphs[104]
Question Generation
Year: 2020
[COLING-20] Answer-driven Deep Question Generation based on Reinforcement Learning[105]
[ICLR-20] Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation[106]
Year: 2019
[WWW-19] Learning to Generate Questions by Learning What not to Generate[107]
AMR2Text
Year: 2020
[COLING-20] Generalized Shortest-Paths Encoders for AMR-to-Text Generation[108]
[IJCAI-20] Better AMR-To-Text Generation with Graph Structure Reconstruction[109]
[TACL-20] AMR-To-Text Generation with Graph Transformer[110]
[EMNLP-20] Online Back-Parsing for AMR-to-Text Generation[111]
[EMNLP-20] Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation[112]
[ACL-20] Heterogeneous Graph Transformer for Graph-to-Sequence Learning[113]
[ACL-20] Structural Information Preserving for Graph-to-Text Generation[114]
[ACL-20] Line Graph Enhanced AMR-to-Text Generation with Mix-Order Graph Attention Networks[115]
[AAAI-20] Graph Transformer for Graph-to-Sequence Learning[116]
Year: 2019
[ACL-19] Modeling Graph Structure in Transformer for Better AMR-to-Text Generation[117]
[NAACL-19] Structural Neural Encoders for AMR-to-text Generation[118]
[EMNLP-19] Enhancing AMR-to-Text Generation with Dual Graph Representations[119]
[TACL-19] Semantic neural machine translation using AMR[120]
[TACL-19] Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning[121]
Year: 2018
[ACL-18] Graph-to-Sequence Learning using Gated Graph Neural Networks[122]
[ACL-18] A Graph-to-Sequence Model for AMR-to-Text Generation[123]
SQL2Text
Year: 2019
[EMNLP-19] Graph Enhanced Cross-Domain Text-to-SQL Generation[124]
Year: 2018
[EMNLP-18] SQL-to-Text Generation with Graph-to-Sequence Model[125]
[ArXiv-18] Graph2seq: Graph to sequence learning with attention-based neural networks[126]
5. Question Answering
Machine Reading Comprehension
Year: 2020
[IJCAI-20] GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension[127]
[IJCAI-20] Multi-hop Reading Comprehension across Documents with Path-based Graph Convolutional Network[128]
[EMNLP-20] Hierarchical Graph Network for Multi-hop Question Answering[129]
[EMNLP-20] SRLGRN: Semantic Role Labeling Graph Reasoning Network[130]
[ACL-20] Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings[131]
[ACL-20] Identifying Supporting Facts for Multi-hop Question Answering with Document Graph Networks[132]
[ACL-20] Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension[133]
Year: 2019
[ACL-19] Dynamically Fused Graph Network for Multi-hop Reasoning[134]
[ACL-19] Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs[135]
[ACL-19] Cognitive Graph for Multi-Hop Reading Comprehension at Scale[136]
[NAACL-19] BAG: Bi-directional Attention Entity Graph Convolutional Network for Multi-hop Reasoning Question Answering[137]
[NAACL-19] Question Answering by Reasoning Across Documents with Graph Convolutional Networks[138]
[EMNLP-19] NumNet: Machine Reading Comprehension with Numerical Reasoning[139]
Knowledge Base Question Answering
Year: 2020
[EMNLP-20] Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering[140]
Year: 2018
[COLING-18] Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering[141]
Open-domain Question Answering
Year: 2020
[EMNLP-20] Open Domain Question Answering based on Text Enhanced Knowledge Graph with Hyperedge Infusion[142]
Year: 2019
[EMNLP-19] PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text[143]
Year: 2018
[EMNLP-18] Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text[144]
Community Question Answering
Year: 2020
[MM-20] Multi-modal Attentive Graph Pooling Model for Community Question Answer Matching[145]
Year: 2019
[MM-19] Hierarchical Graph Semantic Pooling Network for Multi-modal Community Question Answer Matching[146]
6. Parsing
Dependency Parsing
Year: 2020
[ACL-20] Neural Reranking for Dependency Parsing: An Evaluation[147]
Year: 2019
[ACL-19] Graph-based Dependency Parsing with Graph Neural Networks[148]
AMR Parsing
Year: 2020
[EMNLP-20] Online Back-Parsing for AMR-to-Text Generation[149]
[ACL-20] AMR Parsing via Graph_x001C_Sequence Iterative Inference(to be deleted)[150]
[ACL-20] AMR Parsing with Latent Structural Information[151]
Year: 2019
[ACL-19] AMR Parsing as Sequence-to-Graph Transduction(to be deleted)[152]
Year: 2018
[ACL-18] AMR Parsing as Graph Prediction with Latent Alignment(to be deleted)[153]
Semantic Parsing
Year: 2020
[EMNLP-20] Graph-to-Tree Neural Networksfor Learning Structured Input-Output Translationwith Applications to Semantic Parsing and Math Word Problem[154]
[AAAI-20] Graph-Based Transformer with Cross-Candidate Verification for Semantic Parsing[155]
Year: 2019
[ACL-19] Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing[156]
[EMNLP-19] Global Reasoning over Database Structures for Text-to-SQL Parsing[157]
Year: 2018
[EMNLP-18] Exploiting Rich Syntactic Information for Semantic Parsingwith Graph-to-Sequence Mode[158]
Constituency parsing
Year: 2020
[NeurIPS-20] Strongly Incremental Constituency Parsing with Graph Neural Networks[159]
7. Reasoning
Natural Language Inference
Year: 2020
[AAAI-20] Infusing Knowledge into the Textual Entailment Task Using Graph Convolutional Networks[160]
Year: 2019
[AAAI-19] Improving Natural Language Inference Using External Knowledgein the Science Questions Domain[161]
[EMNLP-19] KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning[162]
Math Word Problem
Year: 2020
[ICLR-20] Mathematical Reasoning in Latent Space[163]
[EMNLP-20] Graph-to-Tree Neural Networksfor Learning Structured Input-Output Translationwith Applications to Semantic Parsing and Math Word Problem[164]
[ACL-20] A Knowledge-Aware Sequence-to-Tree Network for Math Word Problem Solving[165]
[ACL-20] Graph-to-Tree Learning for Solving Math Word Problems[166]
[ACL-20] Premise Selection in Natural Language Mathematical Texts[167]
Commonsense Reasoning
Year: 2018
[IJCAI-18] Commonsense Knowledge Aware Conversation Generation with Graph Attention[168]
8. Dialog Systems
Dialogue State Tracking
Year: 2020
[AAAI-20] Schema-Guided Multi-Domain Dialogue State Tracking with Graph Attention Neural Networks[169]
Year: 2019
[COLING-19] Structured Dialogue Policy with Graph Neural Networks[170]
Dialogue Generation
Year: 2019
[IJCAI-19] GSN: A Graph-Structured Network for Multi-Party Dialogues[171]
Next Utterance Prediction
Year: 2021
[AAAI-21] A Graph Reasoning Network for Multi-turn Response Selection via Customized Pre-training[172]
9. Text Classification
Text Classification
Year: 2020
[ACL-20] Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks[173]
[AAAI-20] Tensor Graph Convolutional Networks for Text Classification[174]
Year: 2019
[AAAI-19] Graph Convolutional Networks for Text Classification[175]
[EMNLP-19] Text Level Graph Neural Network for Text Classification[176]
[EMNLP-19] Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification[177]
10. Text Matching
Text Matching
Year: 2020
[ACL-20] Neural Graph Matching Networks for Chinese Short Text Matching[178]
[ACL-20] Matching Article Pairs with Graphical Decomposition and Convolutions[179]
11. Topic Modeling
Topic Modeling
Year: 2020
[KDD-20] Graph Structural-topic Neural Network[180]
[WWW-20] Graph Attention Topic Modeling Network[181]
[EMNLP-20] Neural Topic Modeling by Incorporating Document Relationship Graph[182]
[ACL-20] Tree-Structured Neural Topic Model[183]
Year: 2018
[EMNLP-18] GraphBTM: Graph Enhanced Autoencoded Variational Inference for Biterm Topic Model[184]
12. Sentiment Analysis
Sentiment Analysis
Year: 2020
[EMNLP-20] Convolution over Hierarchical Syntactic and Lexical Graphs for Aspect Level Sentiment Analysis[185]
[EMNLP-20] Improving Aspect-based Sentiment Analysis with Gated Graph Convolutional Networks and Syntax-based Regulation[186]
[EMNLP-20] Convolution over Hierarchical Syntactic and Lexical Graphs for Aspect Level Sentiment Analysis[187]
[ACL-20] KinGDOM: Knowledge-Guided DOMain Adaptation for Sentiment Analysis[188]
[ACL-20] Aspect Sentiment Classification with Document-level Sentiment Preference Modeling[189]
[ACL-20] Dependency Graph Enhanced Dual-transformer Structure for Aspect-based Sentiment Classification[190]
[ACL-20] Relational Graph Attention Network for Aspect-based Sentiment Analysis[191]
Year: 2019
[EMNLP-19] Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks[192]
[EMNLP-19] Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks[193]
一起交流
想和你一起学习进步！『NewBeeNLP』目前已经建立了多个不同方向交流群（机器学习 / 深度学习 / 自然语言处理 / 搜索推荐 / 图网络 / 面试交流 / 等），名额有限，赶紧添加下方微信加入一起讨论交流吧！（注意一定要备注信息才能通过）
参考资料
[1]
[ACL-20] ReInceptionE: Relation-Aware Inception Network with Joint Local-Global Structural Information for Knowledge Graph Embedding: https://www.aclweb.org/anthology/2020.acl-main.526.pdf
[2]
[IJCAI-19] A Vectorized Relational Graph Convolutional Network for Multi-Relational Network Alignment: https://www.ijcai.org/Proceedings/2019/0574.pdf
[3]
[AAAI-19] Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embedding: https://ojs.aaai.org//index.php/AAAI/article/view/4698
[4]
[ACL-19] Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs: https://www.aclweb.org/anthology/P19-1466.pdf
[5]
[ACL-19] A2N: Attending to Neighbors for Knowledge Graph Inference: https://www.aclweb.org/anthology/P19-1431.pdf
[6]
[NAACL-19] Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks: https://www.aclweb.org/anthology/N19-1306.pdf
[7]
[EMNLP-19] CaRe: Open Knowledge Graph Embeddings: https://www.aclweb.org/anthology/D19-1036.pdf
[8]
[ICLR-20] DYNAMICALLY PRUNED MESSAGE PASSING NETWORKS FOR LARGE-SCALE KNOWLEDGE GRAPH REASONING: https://openreview.net/pdf?id=rkeuAhVKvB
[9]
[ICML-20] Inductive Relation Prediction by Subgraph Reasoning: http://proceedings.mlr.press/v119/teru20a/teru20a.pdf
[10]
[ICML-20] Inductive Relation Prediction by Subgraph Reasoning: http://proceedings.mlr.press/v119/teru20a/teru20a.pdf
[11]
[EMNLP-20] TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion: https://www.aclweb.org/anthology/2020.emnlp-main.462.pdf
[12]
[AAAI-20] Commonsense Knowledge Base Completion with Structural and Semantic Context: https://ojs.aaai.org/index.php/AAAI/article/download/5684/5540
[13]
[AAAI-20] Relational Graph Neural Network with Hierarchical Attention for Knowledge Graph Completion: https://ojs.aaai.org//index.php/AAAI/article/view/6508
[14]
[IJCAI-19] Robust Embedding with Multi-Level Structures for Link Prediction: https://www.ijcai.org/Proceedings/2019/0728.pdf
[15]
[AAAI-19] End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion: https://ojs.aaai.org//index.php/AAAI/article/view/4164
[16]
[ACL-19] Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs: https://www.aclweb.org/anthology/P19-1466.pdf
[17]
[ACL-19] A2N: Attending to Neighbors for Knowledge Graph Inference: https://www.aclweb.org/anthology/P19-1431.pdf
[18]
[ACL-19] Multi-Channel Graph Neural Network for Entity Alignment: https://www.aclweb.org/anthology/P19-1140.pdf
[19]
[ACL-19] Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs: https://www.aclweb.org/anthology/P19-1466.pdf
[20]
[EMNLP-19] Incorporating Graph Attention Mechanism into Knowledge Graph Reasoning Based on Deep Reinforcement Learning: https://www.aclweb.org/anthology/D19-1264.pdf
[21]
[ESWC-18] Modeling Relational Data with Graph Convolutional Networks: https://link.springer.com/chapter/10.1007/978-3-319-93417-4_38
[22]
[EMNLP-20] Knowledge Graph Alignment with Entity-Pair Embedding: https://www.aclweb.org/anthology/2020.emnlp-main.130.pdf
[23]
[AAAI-20] Knowledge Graph Alignment Network with Gated Multi-Hop Neighborhood Aggregation: https://ojs.aaai.org//index.php/AAAI/article/view/5354
[24]
[AAAI-20] Coordinated Reasoning for Cross-Lingual Knowledge Graph Alignment: https://ojs.aaai.org//index.php/AAAI/article/view/6476
[25]
[IJCAI-19] A Vectorized Relational Graph Convolutional Network for Multi-Relational Network Alignment: https://www.ijcai.org/Proceedings/2019/0574.pdf
[26]
[IJCAI-19] Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs: https://www.ijcai.org/Proceedings/2019/0733.pdf
[27]
[ACL-19] Multi-Channel Graph Neural Network for Entity Alignment: https://www.aclweb.org/anthology/P19-1140.pdf
[28]
[ACL-19] Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network: https://www.aclweb.org/anthology/P19-1304.pdf
[29]
[EMNLP-19] Aligning Cross-Lingual Entities with Multi-Aspect Information: https://www.aclweb.org/anthology/D19-1451/
[30]
[EMNLP-19] Semi-supervised Entity Alignment via Joint Knowledge Embedding Model and Cross-graph Model: https://www.aclweb.org/anthology/D19-1274.pdf
[31]
[EMNLP-19] Jointly Learning Entity and Relation Representations for Entity Alignment: https://www.aclweb.org/anthology/D19-1023/
[32]
[EMNLP-18] Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks: https://www.aclweb.org/anthology/D18-1032/
[33]
[ACL-20] Bipartite Flat-Graph Network for Nested Named Entity Recognition: https://www.aclweb.org/anthology/2020.acl-main.571.pdf
[34]
[ACL-19] GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction: https://www.aclweb.org/anthology/P19-1136.pdf
[35]
[ACL-19] A Neural Multi-digraph Model for Chinese NER with Gazetteers: https://www.aclweb.org/anthology/P19-1141.pdf
[36]
[NAACL-19] A General Framework for Information Extraction using Dynamic Span Graphs: https://www.aclweb.org/anthology/N19-1308.pdf
[37]
[EMNLP-19] Leverage Lexical Knowledge for Chinese Named Entity Recognition via Collaborative Graph Network: https://www.aclweb.org/anthology/D19-1396/
[38]
[EMNLP-19] A Lexicon-Based Graph Neural Network for Chinese NER: https://www.aclweb.org/anthology/D19-1096.pdf
[39]
[EMNLP-19] Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks: https://www.aclweb.org/anthology/D19-1502.pdf
[40]
[EMNLP-19] Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks: https://www.aclweb.org/anthology/D19-1502.pdf
[41]
[ICML-20] Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs: http://proceedings.mlr.press/v119/qu20a/qu20a.pdf
[42]
[EMNLP-20] Double Graph Based Reasoning for Document-level Relation Extraction: https://www.aclweb.org/anthology/2020.emnlp-main.127.pdf
[43]
[ACL-20] Structural Information Preserving for Graph-to-Text Generation: https://www.aclweb.org/anthology/2020.acl-main.712/
[44]
[ACL-20] Transition-based Directed Graph Construction for Emotion-Cause Pair Extraction: https://www.aclweb.org/anthology/2020.acl-main.342.pdf
[45]
[ACL-19] GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction: https://www.aclweb.org/anthology/P19-1136.pdf
[46]
[ACL-19] Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network: https://www.aclweb.org/anthology/P19-1423/
[47]
[ACL-19] Attention Guided Graph Convolutional Networks for Relation Extraction: https://www.aclweb.org/anthology/P19-1024/
[48]
[ACL-19] Joint Type Inference on Entities and Relations via Graph Convolutional Networks: https://www.aclweb.org/anthology/P19-1131/
[49]
[ACL-19] Graph Neural Networks with Generated Parameters for Relation Extraction: https://www.aclweb.org/anthology/P19-1128/
[50]
[ACL-19] Attention Guided Graph Convolutional Networks for Relation Extraction: https://www.aclweb.org/anthology/P19-1024.pdf
[51]
[NAACL-19] GraphIE: A Graph-Based Framework for Information Extraction: https://www.aclweb.org/anthology/N19-1082/
[52]
[NAACL-19] A General Framework for Information Extraction using Dynamic Span Graphs: https://www.aclweb.org/anthology/N19-1308.pdf
[53]
[NAACL-19] Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks: https://www.aclweb.org/anthology/N19-1306.pdf
[54]
[EMNLP-19] Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs: https://www.aclweb.org/anthology/D19-1498/
[55]
[EMNLP-18] Graph Convolution over Pruned Dependency Trees Improves Relation Extraction: https://www.aclweb.org/anthology/D18-1244.pdf
[56]
[EMNLP-18] RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information: https://www.aclweb.org/anthology/D18-1157/
[57]
[EMNLP-18] N-ary Relation Extraction using Graph State LSTM: https://www.aclweb.org/anthology/D18-1246.pdf
[58]
[EMNLP-20] Edge-Enhanced Graph Convolution Networks for Event Detection with Syntactic Relation: https://www.aclweb.org/anthology/2020.findings-emnlp.211/
[59]
[EMNLP-20] Event Detection: Gate Diversity and Syntactic Importance Scores for Graph Convolution Neural Networks: https://www.aclweb.org/anthology/2020.emnlp-main.583/
[60]
[ACL-19] Encoding Social Information with Graph Convolutional Networks for Political Perspective Detection in News Media: https://www.aclweb.org/anthology/P19-1247.pdf
[61]
[EMNLP-19] Event Detection with Multi-Order Graph Convolution and Aggregated Attention: https://www.aclweb.org/anthology/D19-1582/
[62]
[AAAI-18] Graph Convolutional Networks with Argument-Aware Pooling for Event Detection: https://nyuscholars.nyu.edu/en/publications/graph-convolutional-networks-with-argument-aware-pooling-for-even
[63]
[AAAI-18] Graph Convolutional Networks with Argument-Aware Pooling for Event Detection: https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16329/16155
[64]
[EMNLP-18] Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation: https://www.aclweb.org/anthology/D18-1156/
[65]
[RANLP-17] Graph-based Event Extraction from Twitter: https://www.aclweb.org/anthology/R17-1031/
[66]
[ArXiv-20] Cross-Lingual Semantic Role Labeling With Model Transfer: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9165903
[67]
[ArXiv-20] Semantic Role Labeling with Heterogeneous Syntactic Knowledge: https://www.aclweb.org/anthology/2020.coling-main.266.pdf
[68]
[EMNLP-20] Graph Convolutions over Constituent Trees for Syntax-Aware Semantic Role Labeling: https://www.aclweb.org/anthology/2020.emnlp-main.322.pdf
[69]
[ACL-20] Syntax-Aware Opinion Role Labeling with Dependency Graph Convolutional Networks: https://www.aclweb.org/anthology/2020.acl-main.297.pdf
[70]
[EMNLP-18] A Unified Syntax-aware Framework for Semantic Role Labeling: https://www.aclweb.org/anthology/D18-1262.pdf
[71]
[EMNLP-17] Encoding Sentences with Graph Convolutional Networks: https://www.aclweb.org/anthology/D17-1159.pdf
[72]
[AAAI-21] Document Graph for Neural Machine Translation: https://arxiv.org/pdf/2012.03477.pdf
[73]
[COLING-20] Knowledge Graph Enhanced Neural Machine Translation via Multi-task Learning on Sub-entity Granularity: https://www.aclweb.org/anthology/2020.coling-main.397/
[74]
[IJCAI-20] Knowledge Graphs Enhanced Neural Machine Translation: https://www.ijcai.org/Proceedings/2020/559
[75]
[TACL-20] AMR-To-Text Generation with Graph Transformer: https://www.aclweb.org/anthology/2020.tacl-1.2.pdf
[76]
[ACL-20] Heterogeneous Graph Transformer for Graph-to-Sequence Learning: https://www.aclweb.org/anthology/2020.acl-main.640.pdf
[77]
[ACL-20] A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation: https://www.aclweb.org/anthology/2020.acl-main.273/
[78]
[AAAI-20] Graph Transformer for Graph-to-Sequence Learning: https://ojs.aaai.org//index.php/AAAI/article/view/6243
[79]
[ACL-19] Lattice-Based Transformer Encoder for Neural Machine Translation: https://www.aclweb.org/anthology/P19-1298/
[80]
[NAACL-19] Text Generation from Knowledge Graphs with Graph Transformers: https://www.aclweb.org/anthology/N19-1238/
[81]
[TACL-19] Semantic neural machine translation using AMR: https://www.aclweb.org/anthology/Q19-1002.pdf
[82]
[TACL-19] Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning: https://www.aclweb.org/anthology/Q19-1019/
[83]
[AAAI-18] Graph Based Translation Memory for Neural Machine Translation: https://ojs.aaai.org/index.php/AAAI/article/view/4716
[84]
[NAACL-18] Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks: https://www.aclweb.org/anthology/N18-2078/
[85]
[ACL-18] Graph-to-Sequence Learning using Gated Graph Neural Networks: https://www.aclweb.org/anthology/P18-1026/
[86]
[EMNLP-17] Graph Convolutional Encoders for Syntax-aware Neural Machine Translation: https://www.aclweb.org/anthology/D17-1209v2.pdf
[87]
[EMNLP-17] Neural Machine Translation with Source-Side Latent Graph Parsing: https://www.aclweb.org/anthology/D17-1012/
[88]
[EACL-17] Context-Aware Graph Segmentation for Graph-Based Translation: https://www.aclweb.org/anthology/E17-2095.pdf
[89]
[COLING-20] Improving Abstractive Dialogue Summarization with Graph Structures and Topic Words: https://www.aclweb.org/anthology/2020.coling-main.39/
[90]
[COLING-20] Enhancing Extractive Text Summarization with Topic-Aware Graph Neural Networks: https://www.aclweb.org/anthology/2020.coling-main.468.pdf
[91]
[EMNLP-20] Neural Extractive Summarization with Hierarchical Attentive Heterogeneous Graph Network: https://www.aclweb.org/anthology/2020.emnlp-main.295/
[92]
[EMNLP-20] Summarizing Chinese Medical Answer with Graph Convolution Networks and Question-focused Dual Attention: https://www.aclweb.org/anthology/2020.findings-emnlp.2/
[93]
[ACL-20] Discourse-Aware Neural Extractive Text Summarization: https://www.aclweb.org/anthology/2020.acl-main.451.pdf
[94]
[ACL-20] Discourse-Aware Neural Extractive Text Summarization: https://www.aclweb.org/anthology/2020.acl-main.451.pdf
[95]
[ACL-20] Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward: https://www.aclweb.org/anthology/2020.acl-main.457.pdf
[96]
[ACL-20] Leveraging Graph to Improve Abstractive Multi-Document Summarization: https://www.aclweb.org/anthology/2020.acl-main.555.pdf
[97]
[ACL-20] Heterogeneous Graph Neural Networks for Extractive Document Summarization: https://www.aclweb.org/anthology/2020.acl-main.553.pdf
[98]
[AAAI-20] SemSUM: Semantic Dependency Guided Neural Abstractive Summarization: https://ojs.aaai.org//index.php/AAAI/article/view/6312
[99]
[COLING-18] Abstract Meaning Representation for Multi-Document Summarization: https://www.aclweb.org/anthology/C18-1101.pdf
[100]
[ACL-17] Abstractive document summarization with a graph-based attentional neural model: https://www.aclweb.org/anthology/P17-1108/
[101]
[COLING-17] Graph-based Neural Multi-Document Summarization: https://www.aclweb.org/anthology/K17-1045/
[102]
[ArXiv-20] Improved code summarization via a graph neural network: https://arxiv.org/pdf/2004.02843.pdf
[103]
[ICLR-19] Structured Neural Summarization: https://openreview.net/pdf?id=H1ersoRqtm
[104]
[ICLR-18] learning to represent programs with graphs: https://openreview.net/pdf?id=BJOFETxR-
[105]
[COLING-20] Answer-driven Deep Question Generation based on Reinforcement Learning: https://www.aclweb.org/anthology/2020.coling-main.452/
[106]
[ICLR-20] Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation: https://openreview.net/pdf?id=HygnDhEtvr
[107]
[WWW-19] Learning to Generate Questions by Learning What not to Generate: https://dl.acm.org/doi/10.1145/3308558.3313737
[108]
[COLING-20] Generalized Shortest-Paths Encoders for AMR-to-Text Generation: https://www.aclweb.org/anthology/2020.coling-main.181.pdf
[109]
[IJCAI-20] Better AMR-To-Text Generation with Graph Structure Reconstruction: https://www.ijcai.org/Proceedings/2020/0542.pdf
[110]
[TACL-20] AMR-To-Text Generation with Graph Transformer: https://www.aclweb.org/anthology/2020.tacl-1.2.pdf
[111]
[EMNLP-20] Online Back-Parsing for AMR-to-Text Generation: https://www.aclweb.org/anthology/2020.emnlp-main.92/
[112]
[EMNLP-20] Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation: https://www.aclweb.org/anthology/2020.emnlp-main.169/
[113]
[ACL-20] Heterogeneous Graph Transformer for Graph-to-Sequence Learning: https://www.aclweb.org/anthology/2020.acl-main.640.pdf
[114]
[ACL-20] Structural Information Preserving for Graph-to-Text Generation: https://www.aclweb.org/anthology/2020.acl-main.712/
[115]
[ACL-20] Line Graph Enhanced AMR-to-Text Generation with Mix-Order Graph Attention Networks: https://www.aclweb.org/anthology/2020.acl-main.67/
[116]
[AAAI-20] Graph Transformer for Graph-to-Sequence Learning: https://ojs.aaai.org//index.php/AAAI/article/view/6243
[117]
[ACL-19] Modeling Graph Structure in Transformer for Better AMR-to-Text Generation: https://www.aclweb.org/anthology/D19-1548/
[118]
[NAACL-19] Structural Neural Encoders for AMR-to-text Generation: https://www.aclweb.org/anthology/N19-1366/
[119]
[EMNLP-19] Enhancing AMR-to-Text Generation with Dual Graph Representations: https://www.aclweb.org/anthology/D19-1314/
[120]
[TACL-19] Semantic neural machine translation using AMR: https://www.aclweb.org/anthology/Q19-1002.pdf
[121]
[TACL-19] Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning: https://www.aclweb.org/anthology/Q19-1019/
[122]
[ACL-18] Graph-to-Sequence Learning using Gated Graph Neural Networks: https://www.aclweb.org/anthology/P18-1026/
[123]
[ACL-18] A Graph-to-Sequence Model for AMR-to-Text Generation: https://www.aclweb.org/anthology/P18-1150/
[124]
[EMNLP-19] Graph Enhanced Cross-Domain Text-to-SQL Generation: https://www.aclweb.org/anthology/D19-5319/
[125]
[EMNLP-18] SQL-to-Text Generation with Graph-to-Sequence Model: https://www.aclweb.org/anthology/D18-1112/
[126]
[ArXiv-18] Graph2seq: Graph to sequence learning with attention-based neural networks: https://arxiv.org/pdf/1804.00823.pdf
[127]
[IJCAI-20] GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension: https://www.ijcai.org/Proceedings/2020/171
[128]
[IJCAI-20] Multi-hop Reading Comprehension across Documents with Path-based Graph Convolutional Network: https://www.ijcai.org/Proceedings/2020/540
[129]
[EMNLP-20] Hierarchical Graph Network for Multi-hop Question Answering: https://www.aclweb.org/anthology/2020.emnlp-main.710/
[130]
[EMNLP-20] SRLGRN: Semantic Role Labeling Graph Reasoning Network: https://www.aclweb.org/anthology/2020.emnlp-main.714.pdf
[131]
[ACL-20] Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings: https://www.aclweb.org/anthology/2020.acl-main.412/
[132]
[ACL-20] Identifying Supporting Facts for Multi-hop Question Answering with Document Graph Networks: https://www.aclweb.org/anthology/D19-5306/
[133]
[ACL-20] Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension: https://www.aclweb.org/anthology/2020.acl-main.599/
[134]
[ACL-19] Dynamically Fused Graph Network for Multi-hop Reasoning: https://www.aclweb.org/anthology/P19-1617/
[135]
[ACL-19] Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs: https://www.aclweb.org/anthology/P19-1260/
[136]
[ACL-19] Cognitive Graph for Multi-Hop Reading Comprehension at Scale: https://www.aclweb.org/anthology/P19-1259/
[137]
[NAACL-19] BAG: Bi-directional Attention Entity Graph Convolutional Network for Multi-hop Reasoning Question Answering: https://www.aclweb.org/anthology/N19-1032/
[138]
[NAACL-19] Question Answering by Reasoning Across Documents with Graph Convolutional Networks: https://www.aclweb.org/anthology/N19-1240/
[139]
[EMNLP-19] NumNet: Machine Reading Comprehension with Numerical Reasoning: https://www.aclweb.org/anthology/D19-1251/
[140]
[EMNLP-20] Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering: https://www.aclweb.org/anthology/2020.emnlp-main.99/
[141]
[COLING-18] Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering: https://www.aclweb.org/anthology/C18-1280/
[142]
[EMNLP-20] Open Domain Question Answering based on Text Enhanced Knowledge Graph with Hyperedge Infusion: https://www.aclweb.org/anthology/2020.findings-emnlp.133/
[143]
[EMNLP-19] PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text: https://www.aclweb.org/anthology/D19-1242/
[144]
[EMNLP-18] Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text: https://www.aclweb.org/anthology/D18-1455/
[145]
[MM-20] Multi-modal Attentive Graph Pooling Model for Community Question Answer Matching: https://dl.acm.org/doi/pdf/10.1145/3394171.3413711
[146]
[MM-19] Hierarchical Graph Semantic Pooling Network for Multi-modal Community Question Answer Matching: https://dl.acm.org/doi/10.1145/3343031.3350966
[147]
[ACL-20] Neural Reranking for Dependency Parsing: An Evaluation: https://www.aclweb.org/anthology/2020.acl-main.379/
[148]
[ACL-19] Graph-based Dependency Parsing with Graph Neural Networks: https://www.aclweb.org/anthology/P19-1237/
[149]
[EMNLP-20] Online Back-Parsing for AMR-to-Text Generation: https://www.aclweb.org/anthology/2020.emnlp-main.92/
[150]
[ACL-20] AMR Parsing via Graph_x001C_Sequence Iterative Inference(to be deleted): https://www.aclweb.org/anthology/2020.acl-main.119/
[151]
[ACL-20] AMR Parsing with Latent Structural Information: https://www.aclweb.org/anthology/2020.acl-main.397/
[152]
[ACL-19] AMR Parsing as Sequence-to-Graph Transduction(to be deleted): https://www.aclweb.org/anthology/P19-1009/
[153]
[ACL-18] AMR Parsing as Graph Prediction with Latent Alignment(to be deleted): https://www.aclweb.org/anthology/P18-1037/
[154]
[EMNLP-20] Graph-to-Tree Neural Networksfor Learning Structured Input-Output Translationwith Applications to Semantic Parsing and Math Word Problem: https://www.aclweb.org/anthology/2020.findings-emnlp.255/
[155]
[AAAI-20] Graph-Based Transformer with Cross-Candidate Verification for Semantic Parsing: https://ojs.aaai.org//index.php/AAAI/article/view/6408
[156]
[ACL-19] Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing: https://www.aclweb.org/anthology/P19-1448/
[157]
[EMNLP-19] Global Reasoning over Database Structures for Text-to-SQL Parsing: https://www.aclweb.org/anthology/D19-1378/
[158]
[EMNLP-18] Exploiting Rich Syntactic Information for Semantic Parsingwith Graph-to-Sequence Mode: https://www.aclweb.org/anthology/D18-1110/
[159]
[NeurIPS-20] Strongly Incremental Constituency Parsing with Graph Neural Networks: https://proceedings.neurips.cc/paper/2020/hash/f7177163c833dff4b38fc8d2872f1ec6-Abstract.html
[160]
[AAAI-20] Infusing Knowledge into the Textual Entailment Task Using Graph Convolutional Networks: https://www.semanticscholar.org/paper/Infusing-Knowledge-into-the-Textual-Entailment-Task-Kapanipathi-Thost/4f8e1a4247ce06a15760fc2692c6849601d41b6f
[161]
[AAAI-19] Improving Natural Language Inference Using External Knowledgein the Science Questions Domain: https://ojs.aaai.org//index.php/AAAI/article/view/4705
[162]
[EMNLP-19] KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning: https://www.aclweb.org/anthology/D19-1282/
[163]
[ICLR-20] Mathematical Reasoning in Latent Space: https://openreview.net/forum?id=Ske31kBtPr
[164]
[EMNLP-20] Graph-to-Tree Neural Networksfor Learning Structured Input-Output Translationwith Applications to Semantic Parsing and Math Word Problem: https://www.aclweb.org/anthology/2020.findings-emnlp.255/
[165]
[ACL-20] A Knowledge-Aware Sequence-to-Tree Network for Math Word Problem Solving: https://www.aclweb.org/anthology/2020.emnlp-main.579/
[166]
[ACL-20] Graph-to-Tree Learning for Solving Math Word Problems: https://www.aclweb.org/anthology/2020.acl-main.362/
[167]
[ACL-20] Premise Selection in Natural Language Mathematical Texts: https://www.aclweb.org/anthology/2020.acl-main.657/
[168]
[IJCAI-18] Commonsense Knowledge Aware Conversation Generation with Graph Attention: https://www.ijcai.org/Proceedings/2018/643
[169]
[AAAI-20] Schema-Guided Multi-Domain Dialogue State Tracking with Graph Attention Neural Networks: https://ojs.aaai.org//index.php/AAAI/article/view/6250
[170]
[COLING-19] Structured Dialogue Policy with Graph Neural Networks: https://www.aclweb.org/anthology/C18-1107/
[171]
[IJCAI-19] GSN: A Graph-Structured Network for Multi-Party Dialogues: https://www.ijcai.org/Proceedings/2019/696
[172]
[AAAI-21] A Graph Reasoning Network for Multi-turn Response Selection via Customized Pre-training: https://arxiv.org/abs/2012.11099
[173]
[ACL-20] Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks: https://www.aclweb.org/anthology/2020.acl-main.31.pdf
[174]
[AAAI-20] Tensor Graph Convolutional Networks for Text Classification: https://ojs.aaai.org//index.php/AAAI/article/view/6359
[175]
[AAAI-19] Graph Convolutional Networks for Text Classification: https://ojs.aaai.org//index.php/AAAI/article/view/4725
[176]
[EMNLP-19] Text Level Graph Neural Network for Text Classification: https://www.aclweb.org/anthology/D19-1345/
[177]
[EMNLP-19] Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification: https://www.aclweb.org/anthology/D19-1488/
[178]
[ACL-20] Neural Graph Matching Networks for Chinese Short Text Matching: https://www.aclweb.org/anthology/2020.acl-main.547/
[179]
[ACL-20] Matching Article Pairs with Graphical Decomposition and Convolutions: https://arxiv.org/abs/1802.07459
[180]
[KDD-20] Graph Structural-topic Neural Network: http://doi.org/10.1145/3394486.3403150
[181]
[WWW-20] Graph Attention Topic Modeling Network: http://doi.org/10.1145/3366423.3380102
[182]
[EMNLP-20] Neural Topic Modeling by Incorporating Document Relationship Graph: https://www.aclweb.org/anthology/2020.emnlp-main.310
[183]
[ACL-20] Tree-Structured Neural Topic Model: https://www.aclweb.org/anthology/2020.acl-main.73/
[184]
[EMNLP-18] GraphBTM: Graph Enhanced Autoencoded Variational Inference for Biterm Topic Model: https://www.aclweb.org/anthology/D18-1495
[185]
[EMNLP-20] Convolution over Hierarchical Syntactic and Lexical Graphs for Aspect Level Sentiment Analysis: https://www.aclweb.org/anthology/2020.emnlp-main.286/
[186]
[EMNLP-20] Improving Aspect-based Sentiment Analysis with Gated Graph Convolutional Networks and Syntax-based Regulation: https://www.aclweb.org/anthology/2020.findings-emnlp.407
[187]
[EMNLP-20] Convolution over Hierarchical Syntactic and Lexical Graphs for Aspect Level Sentiment Analysis: https://www.aclweb.org/anthology/2020.emnlp-main.286
[188]
[ACL-20] KinGDOM: Knowledge-Guided DOMain Adaptation for Sentiment Analysis: https://www.aclweb.org/anthology/2020.acl-main.292/
[189]
[ACL-20] Aspect Sentiment Classification with Document-level Sentiment Preference Modeling: https://www.aclweb.org/anthology/2020.acl-main.338/
[190]
[ACL-20] Dependency Graph Enhanced Dual-transformer Structure for Aspect-based Sentiment Classification: https://www.aclweb.org/anthology/2020.acl-main.588/
[191]
[ACL-20] Relational Graph Attention Network for Aspect-based Sentiment Analysis: https://www.aclweb.org/anthology/2020.acl-main.295
[192]
[EMNLP-19] Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks: https://www.aclweb.org/anthology/D19-1549
[193]
[EMNLP-19] Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks: https://www.aclweb.org/anthology/D19-1464
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图深度学习除了可以应用于标准图推理任务以外,还广泛应用于推荐.疾病或药物预测.自然语言处理.计算机视觉.交通预测等领域. 可见,基于图的深度学习不仅有助于挖掘现有图数据背后的丰富价值,而且还通过将关系 ...
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