{"type":"ScholarlyArticle","title":"Exploring Effective Inter-Encoder Semantic Interaction for Document-Level Relation Extraction","authors":[{"name":"Liang Zhang","slug":"liang-zhang","given":"Liang","family":"Zhang","affiliation":"School of Informatics , Xiamen University , China; Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan (Xiamen University) , Ministry of Culture and Tourism , China"},{"name":"Zijun Min","slug":"zijun-min","given":"Zijun","family":"Min","affiliation":"School of Informatics , Xiamen University , China; Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan (Xiamen University) , Ministry of Culture and Tourism , China"},{"name":"Jinsong Su","slug":"jinsong-su","given":"Jinsong","family":"Su","affiliation":"School of Informatics , Xiamen University , China; Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan (Xiamen University) , Ministry of Culture and Tourism , China"},{"name":"Pei Yu","slug":"pei-yu","given":"Pei","family":"Yu","affiliation":"School of Informatics , Xiamen University , China; Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan (Xiamen University) , Ministry of Culture and Tourism , China"},{"name":"Ante Wang","slug":"ante-wang","given":"Ante","family":"Wang","affiliation":"School of Informatics , Xiamen University , China; Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan (Xiamen University) , Ministry of Culture and Tourism , China"},{"name":"Yidong Chen","slug":"yidong-chen","given":"Yidong","family":"Chen","affiliation":"School of Informatics , Xiamen University , China; Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan (Xiamen University) , Ministry of Culture and Tourism , China"}],"abstract":"In document-level relation extraction (RE), the models are required to correctly predict implicit relations in documents via relational reasoning. To this end, many graph-based methods have been proposed  for this task. Despite their success, these methods still suffer from several drawbacks: 1) their interaction between document encoder and graph encoder is usually unidirectional and insufficient; 2) their graph encoders often fail to capture the global context of nodes in document graph. In this paper, we propose a document-level RE model with a Graph-Transformer Network (GTN). The GTN includes two core sublayers: 1) the graph-attention sublayer that simultaneously models global and local contexts of nodes in the document graph; 2) the cross-attention sublayer, enabling GTN to capture the non-entity clue information from the document encoder. Furthermore, we introduce two auxiliary training tasks to enhance the bidirectional semantic interaction between the document encoder and GTN: 1) the graph node reconstruction that can effectively train our cross-attention sublayer to enhance the semantic transition from the document encoder to GTN; 2) the structure-aware adversarial knowledge distillation, by which we can effectively transfer the structural information of GTN to the document encoder. Experimental results on four benchmark datasets prove the effectiveness of our model. Our source code is available at https://github.com/DeepLearnXMU/DocRE-BSI.","doi":"10.24963/ijcai.2023/586","url":"https://ijcai.org/proceedings/2023/586","pdf":"https://ijcai.org/proceedings/2023/0586.pdf","markdown":"https://ijcai.org/proceedings/2023/586.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/586","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Natural Language Processing","track":"Main Track","number":586,"pages":{"first":5278,"last":5286},"keywords":["Natural Language Processing: NLP: Information extraction","Natural Language Processing: NLP: Information retrieval and text mining"],"videos":[],"references":[{"position":1,"raw":"[Baldini Soares et al., 2019] Livio Baldini Soares, Nicholas FitzGerald, Jeffrey Ling, and Tom Kwiatkowski. Match- ing the blanks: Distributional similarity for relation learn- ing. In ACL, 2019.","title":"Matching the blanks: Distributional similarity for relation learning","authors":"Baldini Livio, Nicholas Soares, Jeffrey Fitzgerald, Tom Ling, Kwiatkowski","year":"2019","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Christopoulou et al., 2019] Fenia Christopoulou, Makoto Miwa, Sophia Ananiadou, and Sophia Ananiadou. Con- necting the dots: Document-level neural relation extrac- tion with edge-oriented graphs. In EMNLP, 2019.","title":"Connecting the dots: Document-level neural relation extraction with edge-oriented graphs","authors":"Fenia Christopoulou, Makoto Miwa, Sophia Ananiadou, Sophia Ananiadou","year":"2019","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chung et al., 2020] Inseop Chung, SeongUk Park, Jangho Kim, and Nojun Kwak. Feature-map-level online adver- sarial knowledge distillation. In ICML, 2020.","title":"Feature-map-level online adversarial knowledge distillation","authors":"Chung","year":"2020","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Devlin et al., 2019] Jacob Devlin, Ming-Wei Chang, Ken- ton Lee, and Kristina Toutanova. BERT: Pre-training of deep bidirectional transformers for language understand- ing. In NAACL, 2019.","title":"BERT: Pre-training of deep bidirectional transformers for language understanding","authors":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova","year":"2019","venue":"NAACL","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Goyal et al., 2017] Priya Goyal, Piotr Dollár, Ross Gir- shick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Ky- rola, Andrew Tulloch, Yangqing Jia, and Kaiming He. Ac- curate, large minibatch sgd: Training imagenet in 1 hour. In arXiv preprint arXiv:1706.02677, 2017.","title":"Accurate, large minibatch sgd","authors":"Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, Kaiming He","year":"2017","venue":"Training imagenet in 1 hour","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[He et al., 2022] Huarui He, Jie Wang, Zhanqiu Zhang, and Feng Wu. Compressing deep graph neural networks via adversarial knowledge distillation. In KDD, 2022.","title":"Compressing deep graph neural networks via adversarial knowledge distillation","authors":"He","year":"2022","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Hu et al., 2019] Fenyu Hu, Yanqiao Zhu, Shu Wu, Liang Wang, and Tieniu Tan. Hierarchical graph convolutional networks for semi-supervised node classification. In IJ- CAI, 2019.","title":"Hierarchical graph convolutional networks for semi-supervised node classification","authors":"Fenyu Hu, Yanqiao Zhu, Shu Wu, Liang Wang, Tieniu Tan","year":"2019","venue":"IJ-CAI","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Huang et al., 2022] Quzhe Huang, Shibo Hao, Yuan Ye, Shengqi Zhu, Yansong Feng, and Dongyan Zhao. Does recommend-revise produce reliable annotations? an anal- ysis on missing instances in docred. In ACL, 2022.","title":"Does recommend-revise produce reliable annotations? an analysis on missing instances in docred","authors":"Huang","year":"2022","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Jia et al., 2019] Robin Jia, Cliff Wong, and Hoifung Poon. Document-level n-ary relation extraction with multiscale representation learning. In NAACL, 2019.","title":"Document-level n-ary relation extraction with multiscale representation learning","authors":"Robin Jia, Cliff Wong, Hoifung Poon","year":"2019","venue":"NAACL","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Jiang et al., 2022] Feng Jiang, Jianwei Niu, Shasha Mo, and Shengda Fan. Key mention pairs guided document-level relation extraction. In COLING, 2022.","title":"Key mention pairs guided document-level relation extraction","authors":"Feng Jiang, Jianwei Niu, Shasha Mo, Shengda Fan","year":"2022","venue":"COLING","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Lai et al., 2021] Shaopeng Lai, Ante Wang, Fandong Meng, Jie Zhou, Yubin Ge, Jiali Zeng, Junfeng Yao, Degen Huang, and Jinsong Su. Improving graph-based sentence ordering with iteratively predicted pairwise orderings. In EMNLP, 2021.","title":"Improving graph-based sentence ordering with iteratively predicted pairwise orderings","authors":"Lai","year":"2021","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Li et al., 2020] Bo Li, Wei Ye, Zhonghao Sheng, Rui Xie, Xiangyu Xi, and Shikun Zhang. Graph enhanced dual at- tention network for document-level relation extraction. In COLING, 2020.","title":"Graph enhanced dual attention network for document-level relation extraction","authors":"Bo Li, Wei Ye, Zhonghao Sheng, Rui Xie, Xiangyu Xi, Shikun Zhang","year":"2020","venue":"COLING","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Liu et al., 2019] Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. Roberta: A robustly optimized bert pretraining approach. In arXiv preprint arXiv:1907.11692, 2019.","title":"Roberta: A robustly optimized bert pretraining approach","authors":"Liu","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Loshchilov and Hutter, 2019] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In ICLR, 2019.","title":"Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization","authors":"Hutter Loshchilov","year":"2019","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Nan et al., 2020] Guoshun Nan, Zhijiang Guo, Ivan Sekulic, and Wei Lu. Reasoning with latent structure refinement for document-level relation extraction. In ACL, 2020.","title":"Reasoning with latent structure refinement for document-level relation extraction","authors":"Nan","year":"2020","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Peng et al., 2017] Nanyun Peng, Hoifung Poon, Chris Quirk, Kristina Toutanova, and Wen-tau Yih. Cross- sentence n-ary relation extraction with graph lstms. Trans. Assoc. Comput. Linguistics, 2017.","title":"Crosssentence n-ary relation extraction with graph lstms","authors":"Peng","year":"2017","venue":"Trans. Assoc. Comput. Linguistics","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Peng et al., 2022] Xingyu Peng, Chong Zhang, and Ke Xu. Document-level relation extraction via subgraph reason- ing. In IJCAI, 2022.","title":"Document-level relation extraction via subgraph reasoning","authors":"Xingyu Peng, Chong Zhang, Ke Xu","year":"2022","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2022/601"},{"position":18,"raw":"[Ru et al., 2021] Dongyu Ru, Changzhi Sun, Jiangtao Feng, Lin Qiu, Hao Zhou, Weinan Zhang, Yong Yu, and Lei Li. Learning logic rules for document-level relation ex- traction. In EMNLP, 2021.","title":"Learning logic rules for document-level relation extraction","authors":"Dongyu Ru, Changzhi Sun, Jiangtao Feng, Lin Qiu, Hao Zhou, Weinan Zhang, Yong Yu, Lei Li","year":"2021","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Song et al., 2020] Linfeng Song, Ante Wang, Jinsong Su, Yue Zhang, Kun Xu, Yubin Ge, and Dong Yu. Struc- tural information preserving for graph-to-text generation. In ACL, 2020.","title":"Structural information preserving for graph-to-text generation","authors":"Song","year":"2020","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Tan et al., 2022a] Qingyu Tan, Ruidan He, Lidong Bing, and Hwee Tou Ng. Document-level relation extraction with adaptive focal loss and knowledge distillation. In ACL Findings, 2022.","title":"Document-level relation extraction with adaptive focal loss and knowledge distillation","authors":"Tan","year":"2022","venue":"ACL Findings","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Tan et al., 2022b] Qingyu Tan, Lu Xu, Lidong Bing, and Hwee Tou Ng. Revisiting docred-addressing the over- looked false negative problem in relation extraction. In EMNLP, 2022.","title":"Revisiting docred-addressing the overlooked false negative problem in relation extraction","authors":"Tan","year":"2022","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Tang et al., 2020] Hengzhu Tang, Yanan Cao, Zhenyu Zhang, Jiangxia Cao, Fang Fang, Shi Wang, and Pengfei Yin. Hin: Hierarchical inference network for document- level relation extraction. In KDD, 2020.","title":"Hin: Hierarchical inference network for documentlevel relation extraction","authors":"Hengzhu Tang, Yanan Cao, Zhenyu Zhang, Jiangxia Cao, Fang Fang, Wang Shi, Pengfei Yin","year":"2020","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Wan et al., 2021] Sheng Wan, Shirui Pan, Jian Yang, and Chen Gong. Contrastive and generative graph convolu- tional networks for graph-based semi-supervised learning. In AAAI, 2021.","title":"Contrastive and generative graph convolutional networks for graph-based semi-supervised learning","authors":"Sheng Wan, Shirui Pan, Jian Yang, Chen Gong","year":"2021","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Wang et al., 2019] Hong Wang, Christfried Focke, Rob Sylvester, Nilesh Mishra, and William Wang. Fine-tune bert for docred with two-step process. In arXiv preprint arXiv:1909.11898, 2019.","title":"Fine-tune bert for docred with two-step process","authors":"Wang","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Wang et al., 2020] Difeng Wang, Wei Hu, Ermei Cao, and Weijian Sun. Global-to-local neural networks for document-level relation extraction. In EMNLP, 2020.","title":"Global-to-local neural networks for document-level relation extraction","authors":"Wang","year":"2020","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Wang et al., 2021] Guangtao Wang, Rex Ying, Jing Huang, and Jure Leskovec. Multi-hop attention graph neural net- work. In IJCAI, 2021.","title":"Multi-hop attention graph neural network","authors":"Wang","year":"2021","venue":"IJCAI","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Wolf et al., 2020] Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Fun- towicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. Transformers: State-of-the- art natural language processing. In EMNLP, 2020.","title":"Transformers: State-of-theart natural language processing","authors":"Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Clara Patrick Von Platen, Yacine Ma, Julien Jernite, Canwen Plu, Teven Xu, Sylvain Le Scao, Mariama Gugger, Quentin Drame, Alexander Lhoest, Rush","year":"2020","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Xu et al., 2021a] Benfeng Xu, Quan Wang, Yajuan Lyu, Yong Zhu, and Zhendong Mao. Entity structure within and throughout: Modeling mention dependencies for document-level relation extraction. In AAAI, 2021.","title":"Entity structure within and throughout: Modeling mention dependencies for document-level relation extraction","authors":"Xu","year":"2021","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Xu et al., 2021b] Wang Xu, Kehai Chen, Tiejun Zhao, and Tiejun Zhao. Document-level relation extraction with re- construction. In AAAI, 2021.","title":"Document-level relation extraction with reconstruction","authors":"Xu","year":"2021","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Yao et al., 2019] Yuan Yao, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Lixin Huang, Jie Zhou, and Maosong Sun. DocRED: A large-scale document-level relation extraction dataset. In ACL, 2019.","title":"DocRED: A large-scale document-level relation extraction dataset","authors":"Yuan Yao, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Lixin Huang, Jie Zhou, Maosong Sun","year":"2019","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Ye et al., 2020] Deming Ye, Yankai Lin, Jiaju Du, Zheng- hao Liu, Peng Li, Maosong Sun, and Zhiyuan Liu. Coref- erential Reasoning Learning for Language Representation. In EMNLP, 2020.","title":"Coreferential Reasoning Learning for Language Representation","authors":"Deming Ye, Yankai Lin, Jiaju Du, Zhenghao Liu, Peng Li, Maosong Sun, Zhiyuan Liu","year":"2020","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Yin et al., 2019] Yongjing Yin, Linfeng Song, Jinsong Su, Jiali Zeng, Chulun Zhou, and Jiebo Luo. Graph-based neu- ral sentence ordering. In IJCAI, 2019.","title":"Graph-based neural sentence ordering","authors":"Yin","year":"2019","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2019/748"},{"position":33,"raw":"[Yin et al., 2020a] Yongjing Yin, Shaopeng Lai, Linfeng Song, Chulun Zhou, Xianpei Han, Junfeng Yao, and Jin- song Su. An external knowledge enhanced graph-based neural network for sentence ordering. Journal of Artificial Intelligence Research, 2020.","title":"An external knowledge enhanced graph-based neural network for sentence ordering","authors":"Yin","year":"2020","venue":"Journal of Artificial Intelligence Research","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Yin et al., 2020b] Yongjing Yin, Fandong Meng, Jinsong Su, Chulun Zhou, Zhengyuan Yang, Jie Zhou, and Jiebo Luo. Novel graph-based multi-modal fusion encoder for neural machine translation. In ACL, 2020.","title":"Novel graph-based multi-modal fusion encoder for neural machine translation","authors":"Yin","year":"2020","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Zaporojets et al., 2021] Klim Zaporojets, Johannes Deleu, Chris Develder, and Thomas Demeester. Dwie: An entity- centric dataset for multi-task document-level information extraction. Information Processing & Management, 2021.","title":"Dwie: An entitycentric dataset for multi-task document-level information extraction","authors":"Klim Zaporojets, Johannes Deleu, Chris Develder, Thomas Demeester","year":"2021","venue":"Information Processing & Management","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Zeng et al., 2015] Daojian Zeng, Kang Liu, Yubo Chen, and Jun Zhao. Distant supervision for relation extraction via piecewise convolutional neural networks. In EMNLP, 2015.","title":"Distant supervision for relation extraction via piecewise convolutional neural networks","authors":"Daojian Zeng, Kang Liu, Yubo Chen, Jun Zhao","year":"2015","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Zeng et al., 2020] Shuang Zeng, Runxin Xu, Baobao Chang, and Lei Li. Double graph based reasoning for document-level relation extraction. In EMNLP, 2020.","title":"Double graph based reasoning for document-level relation extraction","authors":"Shuang Zeng, Runxin Xu, Baobao Chang, Lei Li","year":"2020","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Zeng et al., 2021] Shuang Zeng, Yuting Wu, Baobao Chang, and Baobao Chang. SIRE: Separate intra-and inter-sentential reasoning for document-level relation ex- traction. In ACL Findings, 2021.","title":"SIRE: Separate intra-and inter-sentential reasoning for document-level relation extraction","authors":"Shuang Zeng, Yuting Wu, Baobao Chang, Baobao Chang","year":"2021","venue":"ACL Findings","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Zhang et al., 2018] Yuhao Zhang, Peng Qi, and Christo- pher D. Manning. Graph convolution over pruned depen- dency trees improves relation extraction. In EMNLP, 2018.","title":"Graph convolution over pruned dependency trees improves relation extraction","authors":"Yuhao Zhang, Peng Qi, Christopher D Manning","year":"2018","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Zhang et al., 2020] Zhenyu Zhang, Bowen Yu, Xiaobo Shu, Tingwen Liu, Hengzhu Tang, Wang Yubin, and Li Guo. Document-level relation extraction with dual-tier hetero- geneous graph. In COLING, 2020.","title":"Document-level relation extraction with dual-tier heterogeneous graph","authors":"Zhenyu Zhang, Bowen Yu, Xiaobo Shu, Tingwen Liu, Hengzhu Tang, Wang Yubin, Li Guo","year":"2020","venue":"COLING","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Zhang et al., 2021] Ningyu Zhang, Xiang Chen, Xin Xie, Shumin Deng, Chuanqi Tan, Mosha Chen, Fei Huang, Luo Si, and Huajun Chen. Document-level relation extraction as semantic segmentation. In IJCAI, 2021.","title":"Document-level relation extraction as semantic segmentation","authors":"Ningyu Zhang, Xiang Chen, Xin Xie, Shumin Deng, Chuanqi Tan, Mosha Chen, Fei Huang, Luo Si, Huajun Chen","year":"2021","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2021/551"},{"position":42,"raw":"[Zhang et al., 2022] Liang Zhang, Jinsong Su, Yidong Chen, Zhongjian Miao, Min Zijun, Qingguo Hu, and Xiaodong Shi. Towards better document-level relation extraction via iterative inference. In EMNLP, 2022.","title":"Towards better document-level relation extraction via iterative inference","authors":"Liang Zhang, Jinsong Su, Yidong Chen, Zhongjian Miao, Min Zijun, Qingguo Hu, Xiaodong Shi","year":"2022","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Zhang et al., 2023] Liang Zhang, Jinsong Su, Min Zijun, Zhongjian Miao, Qingguo Hu, Biao Fu, Xiaodong Shi, and Yidong Chen. Exploring self-distillation based relational reasoning training for document-level relation extraction. In AAAI, 2023.","title":"Exploring self-distillation based relational reasoning training for document-level relation extraction","authors":"Liang Zhang, Jinsong Su, Min Zijun, Zhongjian Miao, Qingguo Hu, Biao Fu, Xiaodong Shi, Yidong Chen","year":"2023","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Zhou et al., 2021] Wenxuan Zhou, Kevin Huang, Tengyu Ma, and Jing Huang. Document-level relation extraction with adaptive thresholding and localized context pooling. In AAAI, 2021.","title":"Document-level relation extraction with adaptive thresholding and localized context pooling","authors":"Wenxuan Zhou, Kevin Huang, Tengyu Ma, Jing Huang","year":"2021","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Zhuang et al., 2022] Yuanxin Zhuang, Lingjuan Lyu, Chuan Shi, Carl Yang, and Lichao Sun. Data-free adver- sarial knowledge distillation for graph neural networks. In IJCAI, 2022.","title":"Data-free adversarial knowledge distillation for graph neural networks","authors":"Zhuang","year":"2022","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2022/339"}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}