Fetching the paper…
Reading the bibliography…
Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the field of natural language processing and artificial intelligence, due to their emergent ability and generalizability.
W. Yu, C. Zhu, Y. Fang, D. Yu, S. Wang, Y. Xu, M. Zeng, and M. Jiang, “Dict-BERT: Enhancing language model pre-training with dictionary,” in ACL , 2022, pp. 1907–1918
1918
Earlier work this paper cites.
G. Wan, S. Pan, C. Gong, C. Zhou, and G. Haffari, “Reasoning like human: Hierarchical reinforcement learning for knowledge graph reasoning,” in AAAI , 2021, pp. 1926–1932
1932
Earlier work this paper cites.
Y. Ma, A. Wang, and N. Okazaki, “DREEAM: guiding attention with evidence for improving document-level relation extraction,” in EACL , 2023, pp. 1963–1975
1975
Earlier work this paper cites.
J. Shen, C. Wang, L. Gong, and D. Song, “Joint language semantic and structure embedding for knowledge graph completion,” in COLING , 2022, pp. 1965–1978
1978
Earlier work this paper cites.
W. Xiong, M. Yu, S. Chang, X. Guo, and W. Y. Wang, “One-shot relational learning for knowledge graphs,” in EMNLP , 2018, pp. 1980–1990
1990
Earlier work this paper cites.
O. Bodenreider, “The unified medical language system (umls): integrating biomedical terminology,” Nucleic acids research , vol. 32, no. suppl_1, pp. D267–D270, 2004
2004
Earlier work this paper cites.
F. M. Suchanek, G. Kasneci, and G. Weikum, “Yago: a core of semantic knowledge,” in WWW , 2007, pp. 697–706
2007
Earlier work this paper cites.
S. Auer, C. Bizer, G. Kobilarov, J. Lehmann, R. Cyganiak, and Z. Ives, “Dbpedia: A nucleus for a web of open data,” in The Semantic Web: 6th International Semantic Web Conference . Springer, 2007, pp. 722–735
2007
Earlier work this paper cites.
K. Bollacker, C. Evans, P. Paritosh, T. Sturge, and J. Taylor, “Freebase: A collaboratively created graph database for structuring human knowledge,” in SIGMOD , 2008, pp. 1247–1250
2008
Earlier work this paper cites.
M. Mintz, S. Bills, R. Snow, and D. Jurafsky, “Distant supervision for relation extraction without labeled data,” in ACL , 2009, pp. 1003–1011
2009
Earlier work this paper cites.
A. Carlson, J. Betteridge, B. Kisiel, B. Settles, E. Hruschka, and T. Mitchell, “Toward an architecture for never-ending language learning,” in Proceedings of the AAAI conference on artificial intelligence , vol. 24, no. 1, 2010, pp. 1306–1313
2010
Earlier work this paper cites.
A. Bordes, N. Usunier, A. Garcia-Duran, J. Weston, and O. Yakhnenko, “Translating embeddings for modeling multi-relational data,” NeurIPS , vol. 26, 2013
2013
Earlier work this paper cites.
X. Chen, A. Shrivastava, and A. Gupta, “NEIL: extracting visual knowledge from web data,” in IEEE International Conference on Computer Vision, ICCV 2013, Sydney, Australia, December 1-8, 2013 , 2013, pp. 1409–1416
2013
Earlier work this paper cites.
D. Vrandečić and M. Krötzsch, “Wikidata: a free collaborative knowledgebase,” Communications of the ACM , vol. 57, no. 10, pp. 78–85, 2014
2014
Earlier work this paper cites.
B. Yang, S. W.-t. Yih, X. He, J. Gao, and L. Deng, “Embedding entities and relations for learning and inference in knowledge bases,” in ICLR , 2015
2015
Earlier work this paper cites.
Y. Lin, Z. Liu, M. Sun, Y. Liu, and X. Zhu, “Learning entity and relation embeddings for knowledge graph completion,” in Proceedings of the AAAI conference on artificial intelligence , vol. 29, no. 1, 2015
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015 - 18th International Conference Munich, Germany, October 5 - 9, 2015, Proceedings, Part III , ser. Lecture Notes in Computer Science, vol. 9351, 2015, pp. 234–241
2015
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” NeurIPS , vol. 30, 2017
2017
Earlier work this paper cites.
B. Xu, Y. Xu, J. Liang, C. Xie, B. Liang, W. Cui, and Y. Xiao, “Cn-dbpedia: A never-ending chinese knowledge extraction system,” in 30th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems . Springer, 2017, pp. 428–438
2017
Earlier work this paper cites.
R. Speer, J. Chin, and C. Havasi, “Conceptnet 5.5: An open multilingual graph of general knowledge,” in Proceedings of the AAAI conference on artificial intelligence , vol. 31, no. 1, 2017
2017
Earlier work this paper cites.
Y. Zhu, W. Zhou, Y. Xu, J. Liu, Y. Tan et al. , “Intelligent learning for knowledge graph towards geological data,” Scientific Programming , vol. 2017, 2017
2017
Earlier work this paper cites.
S. Ferrada, B. Bustos, and A. Hogan, “Imgpedia: a linked dataset with content-based analysis of wikimedia images,” in The Semantic Web–ISWC 2017 . Springer, 2017, pp. 84–93
2017
Earlier work this paper cites.
C. Gardent, A. Shimorina, S. Narayan, and L. Perez-Beltrachini, “The WebNLG challenge: Generating text from RDF data,” in Proceedings of the 10th International Conference on Natural Language Generation , 2017, pp. 124–133
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
T. Mitchell, W. Cohen, E. Hruschka, P. Talukdar, B. Yang, J. Betteridge, A. Carlson, B. Dalvi, M. Gardner, B. Kisiel, K. Jayant, L. Ni, M. Kathryn, M. Thahir, N. Ndapandula, P. Emmanouil, R. Alan, S. Mehdi, S. Burr, W. Derry, G. Abhinav, C. Xi, S. Abulhair, and W. Joel, “Never-ending learning,” Communications of the ACM , vol. 61, no. 5, pp. 103–115, 2018
2018
Earlier work this paper cites.
M. E. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, and L. Zettlemoyer, “Deep contextualized word representations,” in NAACL , 2018, pp. 2227–2237
2018
Earlier work this paper cites.
H. Wang, F. Zhang, X. Xie, and M. Guo, “Dkn: Deep knowledge-aware network for news recommendation,” in WWW , 2018, pp. 1835–1844
2018
Earlier work this paper cites.
K. Lee, L. He, and L. Zettlemoyer, “Higher-order coreference resolution with coarse-to-fine inference,” in NAACL , 2018, pp. 687–692
2018
Earlier work this paper cites.
H. Zhou, T. Young, M. Huang, H. Zhao, J. Xu, and X. Zhu, “Commonsense knowledge aware conversation generation with graph attention,” in IJCAI , 2018, pp. 4623–4629
2018
Earlier work this paper cites.
M. Warren and P. J. Hayes, “Bounding ambiguity: Experiences with an image annotation system,” in Proceedings of the 1st Workshop on Subjectivity, Ambiguity and Disagreement in Crowdsourcing , ser. CEUR Workshop Proceedings, vol. 2276, 2018, pp. 41–54
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
D. Su, Y. Xu, G. I. Winata, P. Xu, H. Kim, Z. Liu, and P. Fung, “Generalizing question answering system with pre-trained language model fine-tuning,” in Proceedings of the 2nd Workshop on Machine Reading for Question Answering , 2019, pp. 203–211
2019
Earlier work this paper cites.
F. Petroni, T. Rocktäschel, S. Riedel, P. Lewis, A. Bakhtin, Y. Wu, and A. Miller, “Language models as knowledge bases?” in EMNLP-IJCNLP , 2019, pp. 2463–2473
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
Z. Zhang, X. Han, Z. Liu, X. Jiang, M. Sun, and Q. Liu, “ERNIE: Enhanced language representation with informative entities,” in ACL , 2019, pp. 1441–1451
2019
Earlier work this paper cites.
B. Y. Lin, X. Chen, J. Chen, and X. Ren, “KagNet: Knowledge-aware graph networks for commonsense reasoning,” in EMNLP-IJCNLP , 2019, pp. 2829–2839
2019
Earlier work this paper cites.
Z. Lan, M. Chen, S. Goodman, K. Gimpel, P. Sharma, and R. Soricut, “Albert: A lite bert for self-supervised learning of language representations,” in ICLR , 2019
2019
Earlier work this paper cites.
K. Hakala and S. Pyysalo, “Biomedical named entity recognition with multilingual bert,” in Proceedings of the 5th workshop on BioNLP open shared tasks , 2019, pp. 56–61
2019
Earlier work this paper cites.
Y. Liu, Q. Zeng, J. Ordieres Meré, and H. Yang, “Anticipating stock market of the renowned companies: a knowledge graph approach,” Complexity , vol. 2019, 2019
2019
Earlier work this paper cites.
W. Choi and H. Lee, “Inference of biomedical relations among chemicals, genes, diseases, and symptoms using knowledge representation learning,” IEEE Access , vol. 7, pp. 179 373–179 384, 2019
2019
Earlier work this paper cites.
Y. Liu, H. Li, A. Garcia-Duran, M. Niepert, D. Onoro-Rubio, and D. S. Rosenblum, “Mmkg: multi-modal knowledge graphs,” in The Semantic Web: 16th International Conference, ESWC 2019, Portorož, Slovenia, June 2–6, 2019, Proceedings 16 . Springer, 2019, pp. 459–474
2019
Earlier work this paper cites.
B. Shi, L. Ji, P. Lu, Z. Niu, and N. Duan, “Knowledge aware semantic concept expansion for image-text matching.” in IJCAI , vol. 1, 2019, p. 2
2019
Earlier work this paper cites.
S. Shah, A. Mishra, N. Yadati, and P. P. Talukdar, “Kvqa: Knowledge-aware visual question answering,” in AAAI , vol. 33, no. 01, 2019, pp. 8876–8884
2019
Earlier work this paper cites.
R. Logan, N. F. Liu, M. E. Peters, M. Gardner, and S. Singh, “Barack’s wife hillary: Using knowledge graphs for fact-aware language modeling,” in ACL , 2019, pp. 5962–5971
2019
Earlier work this paper cites.
T. McCoy, E. Pavlick, and T. Linzen, “Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference,” in ACL , 2019, pp. 3428–3448
2019
Earlier work this paper cites.
Y. Onoe and G. Durrett, “Learning to denoise distantly-labeled data for entity typing,” in NAACL , 2019, pp. 2407–2417
2019
Earlier work this paper cites.
M. Joshi, O. Levy, L. Zettlemoyer, and D. S. Weld, “BERT for coreference resolution: Baselines and analysis,” in EMNLP , 2019, pp. 5802–5807
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
A. Bosselut, H. Rashkin, M. Sap, C. Malaviya, A. Celikyilmaz, and Y. Choi, “Comet: Commonsense transformers for knowledge graph construction,” in ACL , 2019
2019
Earlier work this paper cites.
D. Lukovnikov, A. Fischer, and J. Lehmann, “Pretrained transformers for simple question answering over knowledge graphs,” in The Semantic Web–ISWC 2019: 18th International Semantic Web Conference, Auckland, New Zealand, October 26–30, 2019, Proceedings, Part I 18 . Springer, 2019, pp. 470–486
2019
Earlier work this paper cites.
X. Huang, J. Zhang, D. Li, and P. Li, “Knowledge graph embedding based question answering,” in WSDM , 2019, pp. 105–113
2019
Earlier work this paper cites.
P. Wang, J. Han, C. Li, and R. Pan, “Logic attention based neighborhood aggregation for inductive knowledge graph embedding,” in AAAI , vol. 33, no. 01, 2019, pp. 7152–7159
2019
Earlier work this paper cites.
C. Alt, M. Hübner, and L. Hennig, “Improving relation extraction by pre-trained language representations,” in 1st Conference on Automated Knowledge Base Construction, AKBC 2019, Amherst, MA, USA, May 20-22, 2019 , 2019
2019
Earlier work this paper cites.
L. B. Soares, N. FitzGerald, J. Ling, and T. Kwiatkowski, “Matching the blanks: Distributional similarity for relation learning,” in ACL , 2019, pp. 2895–2905
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
J. Guan, Y. Wang, and M. Huang, “Story ending generation with incremental encoding and commonsense knowledge,” in AAAI , 2019, pp. 6473–6480
2019
Earlier work this paper cites.
X. Wang, P. Kapanipathi, R. Musa, M. Yu, K. Talamadupula, I. Abdelaziz, M. Chang, A. Fokoue, B. Makni, N. Mattei, and M. Witbrock, “Improving natural language inference using external knowledge in the science questions domain,” in AAAI , 2019, pp. 7208–7215
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” The Journal of Machine Learning Research , vol. 21, no. 1, pp. 5485–5551, 2020
2020
Earlier work this paper cites.
M. Lewis, Y. Liu, N. Goyal, M. Ghazvininejad, A. Mohamed, O. Levy, V. Stoyanov, and L. Zettlemoyer, “Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension,” in ACL , 2020, pp. 7871–7880
2020
Earlier work this paper cites.
X. Qiu, T. Sun, Y. Xu, Y. Shao, N. Dai, and X. Huang, “Pre-trained models for natural language processing: A survey,” Science China Technological Sciences , vol. 63, no. 10, pp. 1872–1897, 2020
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
W. Liu, P. Zhou, Z. Zhao, Z. Wang, Q. Ju, H. Deng, and P. Wang, “K-BERT: enabling language representation with knowledge graph,” in AAAI , 2020, pp. 2901–2908
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020
2020
Earlier work this paper cites.
H. Ji, P. Ke, S. Huang, F. Wei, X. Zhu, and M. Huang, “Language generation with multi-hop reasoning on commonsense knowledge graph,” in EMNLP , 2020, pp. 725–736
2020
Earlier work this paper cites.
H. Zhang, X. Liu, H. Pan, Y. Song, and C. W.-K. Leung, “Aser: A large-scale eventuality knowledge graph,” in Proceedings of the web conference 2020 , 2020, pp. 201–211
2020
Earlier work this paper cites.
Z. Li, X. Ding, T. Liu, J. E. Hu, and B. Van Durme, “Guided generation of cause and effect,” in IJCAI , 2020
2020
Earlier work this paper cites.
F. Farazi, M. Salamanca, S. Mosbach, J. Akroyd, A. Eibeck, L. K. Aditya, A. Chadzynski, K. Pan, X. Zhou, S. Zhang et al. , “Knowledge graph approach to combustion chemistry and interoperability,” ACS omega , vol. 5, no. 29, pp. 18 342–18 348, 2020
2020
Earlier work this paper cites.
M. Wang, H. Wang, G. Qi, and Q. Zheng, “Richpedia: a large-scale, comprehensive multi-modal knowledge graph,” Big Data Research , vol. 22, p. 100159, 2020
2020
Earlier work this paper cites.
R. Sun, X. Cao, Y. Zhao, J. Wan, K. Zhou, F. Zhang, Z. Wang, and K. Zheng, “Multi-modal knowledge graphs for recommender systems,” in CIKM , 2020, pp. 1405–1414
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W.-t. Yih, T. Rocktäschel, S. Riedel, and D. Kiela, “Retrieval-augmented generation for knowledge-intensive nlp tasks,” in NeurIPS , vol. 33, 2020, pp. 9459–9474
2020
Earlier work this paper cites.
Z. Zhang, X. Liu, Y. Zhang, Q. Su, X. Sun, and B. He, “Pretrain-kge: learning knowledge representation from pretrained language models,” in EMNLP Finding , 2020, pp. 259–266
2020
Earlier work this paper cites.
A. Kumar, A. Pandey, R. Gadia, and M. Mishra, “Building knowledge graph using pre-trained language model for learning entity-aware relationships,” in 2020 IEEE International Conference on Computing, Power and Communication Technologies (GUCON) . IEEE, 2020, pp. 310–315
2020
Earlier work this paper cites.
T. Shen, Y. Mao, P. He, G. Long, A. Trischler, and W. Chen, “Exploiting structured knowledge in text via graph-guided representation learning,” in EMNLP , 2020, pp. 8980–8994
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
W. Xiong, J. Du, W. Y. Wang, and V. Stoyanov, “Pretrained encyclopedia: Weakly supervised knowledge-pretrained language model,” in ICLR , 2020
2020
Earlier work this paper cites.
T. Sun, Y. Shao, X. Qiu, Q. Guo, Y. Hu, X. Huang, and Z. Zhang, “CoLAKE: Contextualized language and knowledge embedding,” in Proceedings of the 28th International Conference on Computational Linguistics , 2020, pp. 3660–3670
2020
Earlier work this paper cites.
K. Guu, K. Lee, Z. Tung, P. Pasupat, and M.-W. Chang, “Realm: Retrieval-augmented language model pre-training,” in ICML , 2020
2020
Earlier work this paper cites.
Z. Jiang, F. F. Xu, J. Araki, and G. Neubig, “How can we know what language models know?” Transactions of the Association for Computational Linguistics , vol. 8, pp. 423–438, 2020
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
H. Tian, C. Gao, X. Xiao, H. Liu, B. He, H. Wu, H. Wang, and F. Wu, “SKEP: Sentiment knowledge enhanced pre-training for sentiment analysis,” in ACL , 2020, pp. 4067–4076
2020
Earlier work this paper cites.
B. Kim, T. Hong, Y. Ko, and J. Seo, “Multi-task learning for knowledge graph completion with pre-trained language models,” in COLING , 2020, pp. 1737–1743
2020
Earlier work this paper cites.
B. Z. Li, S. Min, S. Iyer, Y. Mehdad, and W. Yih, “Efficient one-pass end-to-end entity linking for questions,” in EMNLP , 2020, pp. 6433–6441
2020
Earlier work this paper cites.
M. Joshi, D. Chen, Y. Liu, D. S. Weld, L. Zettlemoyer, and O. Levy, “Spanbert: Improving pre-training by representing and predicting spans,” Trans. Assoc. Comput. Linguistics , vol. 8, pp. 64–77, 2020
2020
Earlier work this paper cites.
Z. Jin, Q. Guo, X. Qiu, and Z. Zhang, “GenWiki: A dataset of 1.3 million content-sharing text and graphs for unsupervised graph-to-text generation,” in Proceedings of the 28th International Conference on Computational Linguistics , 2020, pp. 2398–2409
2020
Earlier work this paper cites.
W. Chen, Y. Su, X. Yan, and W. Y. Wang, “KGPT: Knowledge-grounded pre-training for data-to-text generation,” in EMNLP , 2020, pp. 8635–8648
2020
Earlier work this paper cites.
D. Luo, J. Su, and S. Yu, “A bert-based approach with relation-aware attention for knowledge base question answering,” in IJCNN . IEEE, 2020, pp. 1–8
2020
Earlier work this paper cites.
J. Yu, B. Bohnet, and M. Poesio, “Named entity recognition as dependency parsing,” in ACL , 2020, pp. 6470–6476
2020
Cited alongside, same era.
C. Tan, W. Qiu, M. Chen, R. Wang, and F. Huang, “Boundary enhanced neural span classification for nested named entity recognition,” in The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020 , 2020, pp. 9016–9023
2020
Cited alongside, same era.
2020
Cited alongside, same era.
W. Wu, F. Wang, A. Yuan, F. Wu, and J. Li, “Corefqa: Coreference resolution as query-based span prediction,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020 , 2020, pp. 6953–6963
2022
Later among the works it cites.
2022
Later among the works it cites.
M. M. Alam, M. R. A. H. Rony, M. Nayyeri, K. Mohiuddin, M. M. Akter, S. Vahdati, and J. Lehmann, “Language model guided knowledge graph embeddings,” IEEE Access , vol. 10, pp. 76 008–76 020, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2020
Cited alongside, same era.
2020
Cited alongside, same era.
H. Tang, Y. Cao, Z. Zhang, J. Cao, F. Fang, S. Wang, and P. Yin, “HIN: hierarchical inference network for document-level relation extraction,” in PAKDD , ser. Lecture Notes in Computer Science, vol. 12084, 2020, pp. 197–209
2020
Cited alongside, same era.
D. Wang, W. Hu, E. Cao, and W. Sun, “Global-to-local neural networks for document-level relation extraction,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020 , 2020, pp. 3711–3721
2020
Cited alongside, same era.
G. Nan, Z. Guo, I. Sekulic, and W. Lu, “Reasoning with latent structure refinement for document-level relation extraction,” in ACL , 2020, pp. 1546–1557
2020
Cited alongside, same era.
S. Zeng, R. Xu, B. Chang, and L. Li, “Double graph based reasoning for document-level relation extraction,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020 , 2020, pp. 1630–1640
2020
Cited alongside, same era.
M. Kale and A. Rastogi, “Text-to-text pre-training for data-to-text tasks,” in Proceedings of the 13th International Conference on Natural Language Generation , 2020, pp. 97–102
2020
Cited alongside, same era.
A. Saxena, A. Tripathi, and P. Talukdar, “Improving multi-hop question answering over knowledge graphs using knowledge base embeddings,” in ACL , 2020, pp. 4498–4507
2020
Cited alongside, same era.
Y. Feng, X. Chen, B. Y. Lin, P. Wang, J. Yan, and X. Ren, “Scalable multi-hop relational reasoning for knowledge-aware question answering,” in EMNLP , 2020, pp. 1295–1309
2020
Cited alongside, same era.
2022
Later among the works it cites.
X. Xie, Z. Li, X. Wang, Y. Zhu, N. Zhang, J. Zhang, S. Cheng, B. Tian, S. Deng, F. Xiong, and H. Chen, “Lambdakg: A library for pre-trained language model-based knowledge graph embeddings,” 2022
2022
Later among the works it cites.
X. Lv, Y. Lin, Y. Cao, L. Hou, J. Li, Z. Liu, P. Li, and J. Zhou, “Do pre-trained models benefit knowledge graph completion? A reliable evaluation and a reasonable approach,” in ACL , 2022, pp. 3570–3581
2022
Later among the works it cites.
L. Wang, W. Zhao, Z. Wei, and J. Liu, “Simkgc: Simple contrastive knowledge graph completion with pre-trained language models,” in ACL , 2022, pp. 4281–4294
2022
Later among the works it cites.
2022
Later among the works it cites.
A. Saxena, A. Kochsiek, and R. Gemulla, “Sequence-to-sequence knowledge graph completion and question answering,” in ACL , 2022, pp. 2814–2828
2022
Later among the works it cites.
C. Chen, Y. Wang, B. Li, and K. Lam, “Knowledge is flat: A seq2seq generative framework for various knowledge graph completion,” in COLING , 2022, pp. 4005–4017
2022
Later among the works it cites.
T. Ayoola, S. Tyagi, J. Fisher, C. Christodoulopoulos, and A. Pierleoni, “Refined: An efficient zero-shot-capable approach to end-to-end entity linking,” in NAACL , 2022, pp. 209–220
2022
Later among the works it cites.
2022
Later among the works it cites.
P. West, C. Bhagavatula, J. Hessel, J. Hwang, L. Jiang, R. Le Bras, X. Lu, S. Welleck, and Y. Choi, “Symbolic knowledge distillation: from general language models to commonsense models,” in NAACL , 2022, pp. 4602–4625
2022
Later among the works it cites.
A. Colas, M. Alvandipour, and D. Z. Wang, “GAP: A graph-aware language model framework for knowledge graph-to-text generation,” in Proceedings of the 29th International Conference on Computational Linguistics , 2022, pp. 5755–5769
2022
Later among the works it cites.
M. Zhang, R. Dai, M. Dong, and T. He, “Drlk: Dynamic hierarchical reasoning with language model and knowledge graph for question answering,” in EMNLP , 2022, pp. 5123–5133
2022
Later among the works it cites.
Z. Hu, Y. Xu, W. Yu, S. Wang, Z. Yang, C. Zhu, K.-W. Chang, and Y. Sun, “Empowering language models with knowledge graph reasoning for open-domain question answering,” in EMNLP , 2022, pp. 9562–9581
2022
Later among the works it cites.
X. Zhang, A. Bosselut, M. Yasunaga, H. Ren, P. Liang, C. D. Manning, and J. Leskovec, “Greaselm: Graph reasoning enhanced language models,” in ICLR , 2022
2022
Later among the works it cites.
X. Cao and Y. Liu, “Relmkg: reasoning with pre-trained language models and knowledge graphs for complex question answering,” Applied Intelligence , pp. 1–15, 2022
2022
Later among the works it cites.
J. Lovelace and C. P. Rosé, “A framework for adapting pre-trained language models to knowledge graph completion,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022, Abu Dhabi, United Arab Emirates, December 7-11, 2022 , 2022, pp. 5937–5955
2022
Later among the works it cites.
J. Yu, B. Ji, S. Li, J. Ma, H. Liu, and H. Xu, “S-NER: A concise and efficient span-based model for named entity recognition,” Sensors , vol. 22, no. 8, p. 2852, 2022
2022
Later among the works it cites.
C. Lou, S. Yang, and K. Tu, “Nested named entity recognition as latent lexicalized constituency parsing,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2022, Dublin, Ireland, May 22-27, 2022 , 2022, pp. 6183–6198
2022
Later among the works it cites.
S. Yang and K. Tu, “Bottom-up constituency parsing and nested named entity recognition with pointer networks,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2022, Dublin, Ireland, May 22-27, 2022 , 2022, pp. 2403–2416
2022
Later among the works it cites.
N. Ding, Y. Chen, X. Han, G. Xu, X. Wang, P. Xie, H. Zheng, Z. Liu, J. Li, and H. Kim, “Prompt-learning for fine-grained entity typing,” in Findings of the Association for Computational Linguistics: EMNLP 2022, Abu Dhabi, United Arab Emirates, December 7-11, 2022 , 2022, pp. 6888–6901
2022
Later among the works it cites.
W. Pan, W. Wei, and F. Zhu, “Automatic noisy label correction for fine-grained entity typing,” in Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, Vienna, Austria, 23-29 July 2022 , 2022, pp. 4317–4323
2022
Later among the works it cites.
B. Li, W. Yin, and M. Chen, “Ultra-fine entity typing with indirect supervision from natural language inference,” Trans. Assoc. Comput. Linguistics , vol. 10, pp. 607–622, 2022
2022
Later among the works it cites.
N. D. Cao, L. Wu, K. Popat, M. Artetxe, N. Goyal, M. Plekhanov, L. Zettlemoyer, N. Cancedda, S. Riedel, and F. Petroni, “Multilingual autoregressive entity linking,” Trans. Assoc. Comput. Linguistics , vol. 10, pp. 274–290, 2022
2022
Later among the works it cites.
T. M. Lai, T. Bui, and D. S. Kim, “End-to-end neural coreference resolution revisited: A simple yet effective baseline,” in IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022, Virtual and Singapore, 23-27 May 2022 , 2022, pp. 8147–8151
2022
Later among the works it cites.
J. Zhang, X. Zhang, J. Yu, J. Tang, J. Tang, C. Li, and H. Chen, “Subgraph retrieval enhanced model for multi-hop knowledge base question answering,” in ACL (Volume 1: Long Papers) , 2022, pp. 5773–5784
2022
Later among the works it cites.
D. Yu, C. Zhu, Y. Yang, and M. Zeng, “JAKET: joint pre-training of knowledge graph and language understanding,” in AAAI , 2022, pp. 11 630–11 638
2022
Later among the works it cites.
Y. Sun, Q. Shi, L. Qi, and Y. Zhang, “JointLK: Joint reasoning with language models and knowledge graphs for commonsense question answering,” in NAACL , 2022, pp. 5049–5060
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
T. Sun, Y. Shao, H. Qian, X. Huang, and X. Qiu, “Black-box tuning for language-model-as-a-service,” in International Conference on Machine Learning . PMLR, 2022, pp. 20 841–20 855
2022
Later among the works it cites.
Z. Chen, Y. Huang, J. Chen, Y. Geng, Y. Fang, J. Z. Pan, N. Zhang, and W. Zhang, “Lako: Knowledge-driven visual estion answering via late knowledge-to-text injection,” 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
T. Wu, M. Caccia, Z. Li, Y.-F. Li, G. Qi, and G. Haffari, “Pretrained language model in continual learning: A comparative study,” in ICLR , 2022
2022
Later among the works it cites.
X. L. Li, A. Kuncoro, J. Hoffmann, C. de Masson d’Autume, P. Blunsom, and A. Nematzadeh, “A systematic investigation of commonsense knowledge in large language models,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing , 2022, pp. 11 838–11 855
2022
Later among the works it cites.
2023
Closest in time.
Z. Li, C. Wang, Z. Liu, H. Wang, S. Wang, and C. Gao, “Cctest: Testing and repairing code completion systems,” ICSE , 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Z. Ji, N. Lee, R. Frieske, T. Yu, D. Su, Y. Xu, E. Ishii, Y. J. Bang, A. Madotto, and P. Fung, “Survey of hallucination in natural language generation,” ACM Computing Surveys , vol. 55, no. 12, pp. 1–38, 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
L. Luo, Y.-F. Li, G. Haffari, and S. Pan, “Normalizing flow-based neural process for few-shot knowledge graph completion,” SIGIR , 2023
2023
Closest in time.
2023
Closest in time.
O. Golovneva, M. Chen, S. Poff, M. Corredor, L. Zettlemoyer, M. Fazel-Zarandi, and A. Celikyilmaz, “Roscoe: A suite of metrics for scoring step-by-step reasoning,” ICLR , 2023
2023
Closest in time.
J. Jiang, K. Zhou, W. X. Zhao, and J.-R. Wen, “Unikgqa: Unified retrieval and reasoning for solving multi-hop question answering over knowledge graph,” ICLR 2023 , 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
A. Zeng, X. Liu, Z. Du, Z. Wang, H. Lai, M. Ding, Z. Yang, Y. Xu, W. Zheng, X. Xia, W. L. Tam, Z. Ma, Y. Xue, J. Zhai, W. Chen, Z. Liu, P. Zhang, Y. Dong, and J. Tang, “GLM-130b: An open bilingual pre-trained model,” in ICLR , 2023
2023
Closest in time.
2023
Closest in time.
S. Li, Y. Gao, H. Jiang, Q. Yin, Z. Li, X. Yan, C. Zhang, and B. Yin, “Graph reasoning for question answering with triplet retrieval,” in ACL , 2023
2023
Closest in time.
2023
Closest in time.
X. Wu, T. Jiang, Y. Zhu, and C. Bu, “Knowledge graph for china’s genealogy,” IEEE TKDE , vol. 35, no. 1, pp. 634–646, 2023
2023
Closest in time.
S. Deng, C. Wang, Z. Li, N. Zhang, Z. Dai, H. Chen, F. Xiong, M. Yan, Q. Chen, M. Chen, J. Chen, J. Z. Pan, B. Hooi, and H. Chen, “Construction and applications of billion-scale pre-trained multimodal business knowledge graph,” in ICDE , 2023
2023
Closest in time.
2023
Closest in time.
Z. Chen, C. Xu, F. Su, Z. Huang, and Y. Dou, “Incorporating structured sentences with time-enhanced bert for fully-inductive temporal relation prediction,” SIGIR , 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
L. Luo, T.-T. Vu, D. Phung, and G. Haffari, “Systematic assessment of factual knowledge in large language models,” in EMNLP , 2023
2023
Closest in time.
B. Choi and Y. Ko, “Knowledge graph extension with a pre-trained language model via unified learning method,” Knowl. Based Syst. , vol. 262, p. 110245, 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
C. Chen, Y. Wang, A. Sun, B. Li, and L. Kwok-Yan, “Dipping plms sauce: Bridging structure and text for effective knowledge graph completion via conditional soft prompting,” in ACL , 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
H. Zhu, H. Peng, Z. Lyu, L. Hou, J. Li, and J. Xiao, “Pre-training language model incorporating domain-specific heterogeneous knowledge into a unified representation,” Expert Systems with Applications , vol. 215, p. 119369, 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
A. Zeng, M. Liu, R. Lu, B. Wang, X. Liu, Y. Dong, and J. Tang, “Agenttuning: Enabling generalized agent abilities for llms,” 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
R. Girdhar, A. El-Nouby, Z. Liu, M. Singh, K. V. Alwala, A. Joulin, and I. Misra, “Imagebind: One embedding space to bind them all,” in ICCV , 2023, pp. 15 180–15 190
2023
Closest in time.
2023
Closest in time.
B. Min, H. Ross, E. Sulem, A. P. B. Veyseh, T. H. Nguyen, O. Sainz, E. Agirre, I. Heintz, and D. Roth, “Recent advances in natural language processing via large pre-trained language models: A survey,” ACM Computing Surveys , vol. 56, no. 2, pp. 1–40, 2023
2023
Closest in time.
2023
Closest in time.
Y. Onoe, M. Boratko, A. McCallum, and G. Durrett, “Modeling fine-grained entity types with box embeddings,” in ACL , 2021, pp. 2051–2064
2064
Closest in time.
S. Hu, L. Zou, and X. Zhang, “A state-transition framework to answer complex questions over knowledge base,” in EMNLP , 2018, pp. 2098–2108
2098
Closest in time.