Computer Science ›› 2021, Vol. 48 ›› Issue (11): 319-326.doi: 10.11896/jsjkx.201000099
• Artificial Intelligence • Previous Articles Next Articles
YU Liang, WEI Yong-feng, LUO Guo-liang, WU Chang-xing
CLC Number:
| [1]LI Y,FENG W,SUN J,et al.Building Chinese Discourse Corpuswith Connective-driven Dependency Tree Structure[C]//Proceedings of EMNLP 2014.2014:2105-2114. [2]ZHANG L,XING Y,KONG F,et al.A Top-down Neural Architecture towards Text-level Parsing of Discourse Rhetorical Structure[C]//Proceedings of ACL 2020.2020:6386-6395. [3]HU C W,YANG Y L,WU C X.An Overview of Implicit Discourse Relation Recognition Based on Deep Learning[J].Computer Science,2020,47(4):157-163. [4]PITLER E,NENKOVA A.Using Syntax to Disambiguate Explicit Discourse Connectives in Text[C]//Proceedings of ACL-IJCNLP 2009.2009:13-16. [5]KISHIMOTO Y,MURAWAKI Y,KUROHASHI S.AdaptingBERT to Implicit Discourse Relation Classification with a Focus on Discourse Connectives[C]//Proceedings of the 12th Language Resources and Evaluation Conference.2020:1152-1158. [6]PRASAD R,DINESH N,LEE A,et al.The Penn DiscourseTreeBank 2.0[C]//Proceedings of the Sixth International Conference on Language Resources and Evaluation.2008. [7]ZHOU Z M,XU Y,NIU Z Y,et al.Predicting Discourse Connectives for Implicit Discourse Relation Recognition[C]//Proceedings of COLING 2010.2010:1507-1514. [8]QIN L,ZHANG Z,ZHAO H,et al.Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification[C]//Proceedings of ACL 2017.2017:1006-1017. [9]BAI H,ZHAO H.Deep Enhanced Representation for ImplicitDiscourse Relation Recognition[C]//Proceedings of COLING 2018.2018:571-583. [10]NGUYEN L T,NGO L V,THAN K,et al.Employing the Correspondence of Relations and Connectives to Identify Implicit Discourse Relations via Label Embeddings[C]//Proceedings of ACL 2019.2019:4201-4207. [11]WU C,HU C,LI R,et al.Hierarchical Multi-task Learning with CRF for Implicit Discourse Relation Recognition[J].Know-ledge-Based Systems,2020,195(5-6). [12]ZENG J,LIU Y,SU J,et al.Iterative Dual Domain Adaptation for Neural Machine Translation[C]//Proceedings of EMNLP 2019.2019:845-855. [13]LIU Y,CHEN K,LIU C,et al.Structured Knowledge Distillation for Semantic Segmentation[C]//Proceedings of CVPR 2019.2019:2604-2613. [14]HINTON G,VINYALS O,DEAN J.Distilling the Knowledgein a Neural Network[C]//Proceedings of NIPS 2014 Deep Learning Workshop.2015:1-9. [15]PITLER E,LOUIS A,NENKOVA A.Automatic Sense Prediction for Implicit Discourse Relations in Text[C]//Proceedings of ACL 2009.2009:683-691. [16]LI S,KONG F,ZHOU G D.Implicit Discourse Relation Recognition Based on PDTB System[J].Journal of Chinese Information Processing,2016,30(4):81-89. [17]LIN Z,KAN M Y,NG H T.Recognizing Implicit Discourse Relations in the Penn Discourse Treebank[C]//Proceedings of EMNLP 2009.2009:343-351. [18]LOUIS A,JOSHI A,PRASAD R,et al.Using Entity Features to Classify Implicit Discourse Relations[C]//Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue.2010:59-62. [19]RUTHERFORD A,XUE N.Discovering Implicit Discourse Relations through Brown Cluster Pair Representation andCorefe-rence Patterns[C]//Proceedings of EACL 2014.2014:645-654. [20]ZHANG B,SU J,XIONG D,et al.Shallow Convolutional Neural Network for Implicit Discourse Relation Recognition[C]//Proceedings of EMNLP 2015.2015:2230-2235. [21]JI Y,EISENSTEIN J.One Vector is Not Enough:Entity-Augmented Distributed Semantics for Discourse Relations[J].Transactions of the Association for Computational Linguistics,2015,3:329-344. [22]DAI Z,HUANG R.Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph[C]//Proceedings of NAACL 2018.2018:141-151. [23]FAN Z W,ZHANG M,LI Z H.Implicit Discourse RelationClassification Based on BiLSTM Combined with Self -Attention Mechanism and Syntactic Information[J].Computer Science,2019,46(5):221-227. [24]ZHANG B,XIONG D,SU J,et al.Learning Better Discourse Representation for Implicit Discourse Relation Recognition via Attention Networks[J].Neurocomputing,2018,275:1241-1249. [25]CHEN J,ZHANG Q,LIU P,et al.Implicit Discourse Relation Detection via a Deep Architecture with Gated Relevance Network[C]//Proceedings of ACL 2016.2016:1726-1735. [26]LIU Y,LI S.Recognizing Implicit Discourse Relations via Repeated Reading:Neural Networks with Multi-Level Attention[C]//Proceedings of EMNLP 2016.2016:1224-1233. [27]LEI W,WANG X,LIU M,et al.SWIM:A Simple Word Interaction Model for Implicit Discourse Relation Recognition[C]//Proceedings of IJCAI 2017.2017:4026-4032. [28]GUO F,HE R,JIN D,et al.Implicit Discourse Relation Recognition Using Neural Tensor Network with Interactive Attention and Sparse Learning[C]//Proceedings of COLING 2018.2018:547-558. [29]GUO F Y,HE R F,DANG J W.Implicit Discourse Relation Recognition Based on Context Interaction Perception and Pattern Selection[J].Chinese Journal of Computers,2020,43(5):901-915. [30]MARCU D,ECHIHABI A.An Unsupervised Approach to Re-cognizing Discourse Relations[C]//Proceedings of ACL 2002.2002:368-375. [31]SPORLEDER C,LASCARIDES A.Using Automatically La-belled Examples to Classify Rhetorical Relations:An Assessment[J].Natural Language Engineering,2008,14(3):369-416. [32]WU C,SHI X,CHEN Y,et al.Bilingually-constrained Synthetic Data for Implicit Discourse Relation Recognition[C]//Procee-dings of EMNLP 2016.2016:2306-2312. [33]LAN M,WANG J,WU Y,et al.Multi-task Attention-basedNeural Networks for Implicit Discourse Relationship Representation and Identification[C]//Proceedings of EMNLP 2017.2017:1310-1319. [34]WU C,SHI X,SU J,et al.Co-training for Implicit Discourse Relation Recognition Based on Manual and Distributed Features[J].Neural Processing Letters,2017,46(1):233-250. [35]XU Y,HONG Y,RUAN H,et al.Using Active Learning to Expand Training Data for Implicit Discourse Relation Recognition[C]//Proceedings of EMNLP 2018.2018:725-731. [36]BRAUD C,DENIS P.Learning Connective-based Word Representations for Implicit Discourse Relation Identification[C]//Proceedings of EMNLP 2016.2016:203-213. [37]WU C,SU J,CHEN Y,et al.Boosting Implicit Discourse Relation Recognition with Connective-based Word Embeddings[J].Neurocomputing,2019,369:39-49. [38]YIM J,JOO D,BAE J,et al.A Gift from Knowledge Distillation:Fast Optimization,Network Minimization and Transfer Learning[C]//Proceeding of CVPR 2017.2017:7130-7138. [39]ZHANG Y,XIANG T,HOSPEDALES T M,et al.Deep Mutual Learning[C]//Proceeding of CVPR 2018.2018:4320-4328. [40]LIU X,LIU K,LI X,et al.An Iterative Multi-Source MutualKnowledge Transfer Framework for Machine Reading Comprehension[C]//Proceedings of IJCAI 2020.2020:3794-3800. [42]PARIKH A,TÄCKSTRÖM O,DAS D,et al.A Decomposable Attention Model for Natural Language Inference[C]//Procee-dings of EMNLP 2016.2016:2249-2255. [43]SRIVASTAVA N,HINTON G,KRIZHEVSKY A,et al.Dropout:A Simple Way to Prevent Neural Networks from Overfitting[J].The Journal of Machine Learning Research,2014,15(1):1929-1958. [44]PENNINGTON J,SOCHER R,MANNING C.Glove:GlobalVectors for Word Representation[C]//Proceedings of EMNLP 2014.2014:1532-1543. [45]CAI D,ZHAO H.Pair-Aware Neural Sentence Modeling for Implicit Discourse Relation Classification[C]//Proceedings of Advances in Artificial Intelligence:From Theory to Practice 2017.2017:458-466. [46]GUO F,HE R,DANG J,et al.Working Memory-Driven Neural Networks with a Novel Knowledge Enhancement Paradigm for Implicit Discourse Relation Recognition[C]//Proceeding of AAAI 2020.2020:10-18. [47]PETERS M,NEUMANN M,IYYER M,et al.Deep Contextua-lized Word Representations[C]//Proceedings of NAACL 2018.2018:2227-2237. [48]DEVLIN J,CHANG M W,LEE K,et al.BERT:Pre-training of Deep Bidirectional Transformers for Language Understanding[C]//Proceedings of NAACL-HLT.2019:4171-4186. |
| [1] | XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai. Temporal Knowledge Graph Representation Learning [J]. Computer Science, 2022, 49(9): 162-171. |
| [2] | RAO Zhi-shuang, JIA Zhen, ZHANG Fan, LI Tian-rui. Key-Value Relational Memory Networks for Question Answering over Knowledge Graph [J]. Computer Science, 2022, 49(9): 202-207. |
| [3] | TANG Ling-tao, WANG Di, ZHANG Lu-fei, LIU Sheng-yun. Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy [J]. Computer Science, 2022, 49(9): 297-305. |
| [4] | WANG Jian, PENG Yu-qi, ZHAO Yu-fei, YANG Jian. Survey of Social Network Public Opinion Information Extraction Based on Deep Learning [J]. Computer Science, 2022, 49(8): 279-293. |
| [5] | HAO Zhi-rong, CHEN Long, HUANG Jia-cheng. Class Discriminative Universal Adversarial Attack for Text Classification [J]. Computer Science, 2022, 49(8): 323-329. |
| [6] | JIANG Meng-han, LI Shao-mei, ZHENG Hong-hao, ZHANG Jian-peng. Rumor Detection Model Based on Improved Position Embedding [J]. Computer Science, 2022, 49(8): 330-335. |
| [7] | SUN Qi, JI Gen-lin, ZHANG Jie. Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection [J]. Computer Science, 2022, 49(8): 172-177. |
| [8] | HOU Yu-tao, ABULIZI Abudukelimu, ABUDUKELIMU Halidanmu. Advances in Chinese Pre-training Models [J]. Computer Science, 2022, 49(7): 148-163. |
| [9] | ZHOU Hui, SHI Hao-chen, TU Yao-feng, HUANG Sheng-jun. Robust Deep Neural Network Learning Based on Active Sampling [J]. Computer Science, 2022, 49(7): 164-169. |
| [10] | SU Dan-ning, CAO Gui-tao, WANG Yan-nan, WANG Hong, REN He. Survey of Deep Learning for Radar Emitter Identification Based on Small Sample [J]. Computer Science, 2022, 49(7): 226-235. |
| [11] | HU Yan-yu, ZHAO Long, DONG Xiang-jun. Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification [J]. Computer Science, 2022, 49(7): 73-78. |
| [12] | CHENG Cheng, JIANG Ai-lian. Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction [J]. Computer Science, 2022, 49(7): 120-126. |
| [13] | WANG Jun-feng, LIU Fan, YANG Sai, LYU Tan-yue, CHEN Zhi-yu, XU Feng. Dam Crack Detection Based on Multi-source Transfer Learning [J]. Computer Science, 2022, 49(6A): 319-324. |
| [14] | CHU Yu-chun, GONG Hang, Wang Xue-fang, LIU Pei-shun. Study on Knowledge Distillation of Target Detection Algorithm Based on YOLOv4 [J]. Computer Science, 2022, 49(6A): 337-344. |
| [15] | ZHOU Zhi-hao, CHEN Lei, WU Xiang, QIU Dong-liang, LIANG Guang-sheng, ZENG Fan-qiao. SMOTE-SDSAE-SVM Based Vehicle CAN Bus Intrusion Detection Algorithm [J]. Computer Science, 2022, 49(6A): 562-570. |
|
||
Home