{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T10:32:24Z","timestamp":1763202744974},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T00:00:00Z","timestamp":1683849600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T00:00:00Z","timestamp":1683849600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s11063-023-11292-3","type":"journal-article","created":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T19:01:58Z","timestamp":1683918118000},"page":"7967-7983","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Improving PLMs for Graph-to-Text Generation by Relational Orientation Attention"],"prefix":"10.1007","volume":"55","author":[{"given":"Tao","family":"Wang","sequence":"first","affiliation":[]},{"given":"Bo","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Jinglin","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yu","family":"Zhong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,5,12]]},"reference":[{"key":"11292_CR1","doi-asserted-by":"crossref","unstructured":"Huang X, Zhang J, Li D, et\u00a0al (2019) Knowledge graph embedding based question answering. In: Proceedings of the twelfth ACM international conference on web search and data mining, pp 105\u2013113","DOI":"10.1145\/3289600.3290956"},{"issue":"2","key":"11292_CR2","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1109\/TNNLS.2021.3070843","volume":"33","author":"S Ji","year":"2021","unstructured":"Ji S, Pan S, Cambria E et al (2021) A survey on knowledge graphs: representation, acquisition, and applications. IEEE Trans Neural Netw Learn Syst 33(2):494\u2013514","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"103","key":"11292_CR3","first-page":"627","volume":"302","author":"I Tiddi","year":"2022","unstructured":"Tiddi I, Schlobach S (2022) Knowledge graphs as tools for explainable machine learning: a survey. Artif Intell 302(103):627","journal-title":"Artif Intell"},{"key":"11292_CR4","doi-asserted-by":"crossref","unstructured":"Wang Y, Zhang H, Liu Y, et\u00a0al (2019) Kg-to-text generation with slot-attention and link-attention. In: CCF International conference on natural language processing and Chinese computing, Springer, pp 223\u2013234","DOI":"10.1007\/978-3-030-32233-5_18"},{"key":"11292_CR5","doi-asserted-by":"crossref","unstructured":"Zhou H, Young T, Huang M, et\u00a0al (2018) Commonsense knowledge aware conversation generation with graph attention. In: IJCAI, pp 4623\u20134629","DOI":"10.24963\/ijcai.2018\/643"},{"key":"11292_CR6","unstructured":"Koncel-Kedziorski R, Bekal D, Luan Y, et\u00a0al (2019) Text generation from knowledge graphs with graph transformers. In: 2019 annual conference of the north american chapter of the association for computational linguistics, association for computational linguistics (ACL), pp 2284\u20132293"},{"key":"11292_CR7","doi-asserted-by":"crossref","unstructured":"Ji H, Ke P, Huang S, et\u00a0al (2020) Language generation with multi-hop reasoning on commonsense knowledge graph. In: Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP), pp 725\u2013736","DOI":"10.18653\/v1\/2020.emnlp-main.54"},{"key":"11292_CR8","doi-asserted-by":"crossref","unstructured":"Gardent C, Shimorina A, Narayan S, et\u00a0al (2017) The webnlg challenge: generating text from RDF data. In: Proceedings of the 10th international conference on natural language generation, pp 124\u2013133","DOI":"10.18653\/v1\/W17-3518"},{"key":"11292_CR9","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1162\/tacl_a_00297","volume":"8","author":"T Wang","year":"2020","unstructured":"Wang T, Wan X, Jin H (2020) Amr-to-text generation with graph transformer. Trans Assoc Comput Linguist 8:19\u201333","journal-title":"Trans Assoc Comput Linguist"},{"key":"11292_CR10","doi-asserted-by":"crossref","unstructured":"Schmitt M, Ribeiro LF, Dufter P, et\u00a0al (2021) Modeling graph structure via relative position for text generation from knowledge graphs. In: Proceedings of the fifteenth workshop on graph-based methods for natural language processing (TextGraphs-15), pp 10\u201321","DOI":"10.18653\/v1\/2021.textgraphs-1.2"},{"key":"11292_CR11","unstructured":"Li L, Geng R, Li B, et\u00a0al (2022) Graph-to-text generation with dynamic structure pruning. In: Proceedings of the 29th international conference on computational linguistics, pp 6115\u20136127"},{"key":"11292_CR12","doi-asserted-by":"crossref","unstructured":"Wang Q, Yavuz S, Lin XV, et\u00a0al (2021) Stage-wise fine-tuning for graph-to-text generation. In: Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing: student research workshop, pp 16\u201322","DOI":"10.18653\/v1\/2021.acl-srw.2"},{"key":"11292_CR13","doi-asserted-by":"publisher","first-page":"944","DOI":"10.18653\/v1\/2021.findings-acl.82","volume":"2021","author":"AM Hoyle","year":"2021","unstructured":"Hoyle AM, Marasovi\u0107 A, Smith NA (2021) Promoting graph awareness in linearized graph-to-text generation. Find Assoc Comput Linguist ACL-IJCNLP 2021:944\u2013956","journal-title":"Find Assoc Comput Linguist ACL-IJCNLP"},{"issue":"8","key":"11292_CR14","first-page":"9","volume":"1","author":"A Radford","year":"2019","unstructured":"Radford A, Wu J, Child R et al (2019) Language models are unsupervised multitask learners. OpenAI Blog 1(8):9","journal-title":"OpenAI Blog"},{"key":"11292_CR15","doi-asserted-by":"crossref","unstructured":"Lewis M, Liu Y, Goyal N, et\u00a0al (2019) Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461","DOI":"10.18653\/v1\/2020.acl-main.703"},{"issue":"140","key":"11292_CR16","first-page":"1","volume":"21","author":"C Raffel","year":"2020","unstructured":"Raffel C, Shazeer N, Roberts A et al (2020) Exploring the limits of transfer learning with a unified text-to-text transformer. J Mach Learn Res 21(140):1\u201367","journal-title":"J Mach Learn Res"},{"key":"11292_CR17","doi-asserted-by":"crossref","unstructured":"Ribeiro LF, Schmitt M, Sch\u00fctze H, et\u00a0al (2020) Investigating pretrained language models for graph-to-text generation. arXiv preprint arXiv:2007.08426","DOI":"10.18653\/v1\/2021.nlp4convai-1.20"},{"issue":"10","key":"11292_CR18","doi-asserted-by":"publisher","first-page":"1872","DOI":"10.1007\/s11431-020-1647-3","volume":"63","author":"X Qiu","year":"2020","unstructured":"Qiu X, Sun T, Xu Y et al (2020) Pre-trained models for natural language processing: a survey. Sci China Technol Sci 63(10):1872\u20131897","journal-title":"Sci China Technol Sci"},{"key":"11292_CR19","doi-asserted-by":"crossref","unstructured":"Wang T, Wan X, Yao S (2021) Better amr-to-text generation with graph structure reconstruction. In: Proceedings of the twenty-ninth international conference on international joint conferences on artificial intelligence, pp 3919\u20133925","DOI":"10.24963\/ijcai.2020\/542"},{"key":"11292_CR20","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1162\/tacl_a_00332","volume":"8","author":"LF Ribeiro","year":"2020","unstructured":"Ribeiro LF, Zhang Y, Gardent C et al (2020) Modeling global and local node contexts for text generation from knowledge graphs. Trans Assoc Comput Linguist 8:589\u2013604","journal-title":"Trans Assoc Comput Linguist"},{"key":"11292_CR21","doi-asserted-by":"crossref","unstructured":"Ke P, Ji H, Ran Y, et\u00a0al (2021) Jointgt: Graph-text joint representation learning for text generation from knowledge graphs. arXiv preprint arXiv:2106.10502","DOI":"10.18653\/v1\/2021.findings-acl.223"},{"key":"11292_CR22","unstructured":"Berant J, Chou A, Frostig R, et\u00a0al (2013) Semantic parsing on freebase from question-answer pairs. In: Proceedings of the 2013 conference on empirical methods in natural language processing, pp 1533\u20131544"},{"key":"11292_CR23","unstructured":"Zhou M, Huang M, Zhu X (2018) An interpretable reasoning network for multi-relation question answering. In: Proceedings of the 27th international conference on computational linguistics, pp 2010\u20132022"},{"key":"11292_CR24","unstructured":"Vaswani A, Shazeer N, Parmar N, et\u00a0al (2017) Attention is all you need. In: Advances in neural information processing systems, p 30"},{"key":"11292_CR25","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, et\u00a0al (2017) Graph attention networks. arXiv preprint arXiv:1710.10903"},{"key":"11292_CR26","doi-asserted-by":"crossref","unstructured":"Cai D, Lam W (2020) Graph transformer for graph-to-sequence learning. In: Proceedings of the AAAI conference on artificial intelligence, pp 7464\u20137471","DOI":"10.1609\/aaai.v34i05.6243"},{"key":"11292_CR27","doi-asserted-by":"crossref","unstructured":"Edunov S, Baevski A, Auli M (2019) Pre-trained language model representations for language generation. In: Proceedings of NAACL-HLT, pp 4052\u20134059","DOI":"10.18653\/v1\/N19-1409"},{"key":"11292_CR28","doi-asserted-by":"crossref","unstructured":"Li J, Tang T, Zhao WX, et\u00a0al (2021) Few-shot knowledge graph-to-text generation with pretrained language models. arXiv preprint arXiv:2106.01623","DOI":"10.18653\/v1\/2021.findings-acl.136"},{"key":"11292_CR29","doi-asserted-by":"crossref","unstructured":"Schlichtkrull M, Kipf TN, Bloem P, et\u00a0al (2018) Modeling relational data with graph convolutional networks. In: European semantic web conference, Springer, pp 593\u2013607","DOI":"10.1007\/978-3-319-93417-4_38"},{"issue":"107","key":"11292_CR30","first-page":"861","volume":"238","author":"Q Zhang","year":"2022","unstructured":"Zhang Q, Wang R, Yang J et al (2022) Knowledge graph embedding by reflection transformation. Knowl Based Syst 238(107):861","journal-title":"Knowl Based Syst"},{"issue":"10","key":"11292_CR31","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1145\/2629489","volume":"57","author":"D Vrande\u010di\u0107","year":"2014","unstructured":"Vrande\u010di\u0107 D, Kr\u00f6tzsch M (2014) Wikidata: a free collaborative knowledgebase. Commun ACM 57(10):78\u201385","journal-title":"Commun ACM"},{"key":"11292_CR32","doi-asserted-by":"crossref","unstructured":"Chen W, Su Y, Yan X, et\u00a0al (2020) Kgpt: Knowledge-grounded pre-training for data-to-text generation. In: Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP), pp 8635\u20138648","DOI":"10.18653\/v1\/2020.emnlp-main.697"},{"key":"11292_CR33","doi-asserted-by":"crossref","unstructured":"Auer S, Bizer C, Kobilarov G, et\u00a0al (2007) Dbpedia: A nucleus for a web of open data. In: The semantic web. Springer, pp 722\u2013735","DOI":"10.1007\/978-3-540-76298-0_52"},{"key":"11292_CR34","doi-asserted-by":"crossref","unstructured":"Bollacker K, Evans C, Paritosh P, et\u00a0al (2008) Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD international conference on Management of data, pp 1247\u20131250","DOI":"10.1145\/1376616.1376746"},{"key":"11292_CR35","doi-asserted-by":"crossref","unstructured":"Papineni K, Roukos S, Ward T, et\u00a0al (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the association for computational linguistics, pp 311\u2013318","DOI":"10.3115\/1073083.1073135"},{"key":"11292_CR36","unstructured":"Lin CY (2004) Rouge: A package for automatic evaluation of summaries. In: Text summarization branches out, pp 74\u201381"},{"key":"11292_CR37","unstructured":"Banerjee S, Lavie A (2005) Meteor: An automatic metric for mt evaluation with improved correlation with human judgments. In: Proceedings of the acl workshop on intrinsic and extrinsic evaluation measures for machine translation and\/or summarization, pp 65\u201372"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-023-11292-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-023-11292-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-023-11292-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,28]],"date-time":"2023-10-28T19:15:49Z","timestamp":1698520549000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-023-11292-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,12]]},"references-count":37,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["11292"],"URL":"https:\/\/doi.org\/10.1007\/s11063-023-11292-3","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"value":"1370-4621","type":"print"},{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,12]]},"assertion":[{"value":"1 May 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 May 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}