{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:09:53Z","timestamp":1750219793123,"version":"3.41.0"},"reference-count":59,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2023,9,27]],"date-time":"2023-09-27T00:00:00Z","timestamp":1695772800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2019YFE0198200"],"award-info":[{"award-number":["2019YFE0198200"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Beijing Outstanding Young Scientist Program","award":["BJJWZYJH012019100020098"],"award-info":[{"award-number":["BJJWZYJH012019100020098"]}]},{"name":"Intelligent Social Governance Interdisciplinary Platform, Major Innovation & Planning Interdisciplinary Platform for the \u201cDouble-First Class\u201d Initiative, Renmin University of China"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2024,3,31]]},"abstract":"<jats:p>Matching two natural language sentences is a fundamental problem in both natural language processing and information retrieval. Preliminary studies have shown that the syntactic structures help improve the matching accuracy, and different syntactic structures in natural language are complementary to sentence semantic understanding. Ideally, a matching model would leverage all syntactic information. Existing models, however, are only able to combine limited (usually one) types of syntactic information due to the complex and heterogeneous nature of the syntactic information. To deal with the problem, we propose a novel matching model, which formulates sentence matching as a representation learning task on a syntactic-informed heterogeneous graph. The model, referred to as SIGN (Syntactic-Informed Graph Network), first constructs a heterogeneous matching graph based on the multiple syntactic structures of two input sentences. Then the graph attention network algorithm is applied to the matching graph to learn the high-level representations of the nodes. With the help of the graph learning framework, the multiple syntactic structures, as well as the word semantics, can be represented and interacted in the matching graph and therefore collectively enhance the matching accuracy. We conducted comprehensive experiments on three public datasets. The results demonstrate that SIGN outperforms the state of the art and also can discriminate the sentences in an interpretable way.<\/jats:p>","DOI":"10.1145\/3609795","type":"journal-article","created":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T12:10:06Z","timestamp":1689768606000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Syntactic-Informed Graph Networks for Sentence Matching"],"prefix":"10.1145","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3070-9358","authenticated-orcid":false,"given":"Chen","family":"Xu","sequence":"first","affiliation":[{"name":"Gaoling School of Artificial Intelligence, Renmin University of China, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7170-111X","authenticated-orcid":false,"given":"Jun","family":"Xu","sequence":"additional","affiliation":[{"name":"Gaoling School of Artificial Intelligence, Renmin University of China, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2231-4663","authenticated-orcid":false,"given":"Zhenhua","family":"Dong","sequence":"additional","affiliation":[{"name":"Noah\u2019s Ark Lab, Huawei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9777-9676","authenticated-orcid":false,"given":"Ji-Rong","family":"Wen","sequence":"additional","affiliation":[{"name":"Gaoling School of Artificial Intelligence, Renmin University of China, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,9,27]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1017\/S1351324997001599","article-title":"Partial parsing via finite-state cascades","author":"Abney Steven","year":"1996","unstructured":"Steven Abney. 1996. Partial parsing via finite-state cascades. Natural Language Engineering 2, 4 (1996), 337\u2013344.","journal-title":"Natural Language Engineering"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.eacl-main.262"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1209"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1075"},{"key":"e_1_3_2_6_2","unstructured":"Fan Bu Hang Li and Xiaoyan Zhu. 2013. An introduction to string re-writing kernel. In Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI\u201913) . 2982\u20132986. http:\/\/www.aaai.org\/ocs\/index.php\/IJCAI\/IJCAI13\/paper\/view\/6544"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219928"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.547"},{"key":"e_1_3_2_9_2","article-title":"Enhancing and combining sequential and tree lstm for natural language inference","volume":"1609","author":"Chen Qian","year":"2016","unstructured":"Qian Chen, Xiaodan Zhu, Zhenhua Ling, Si Wei, and Hui Jiang. 2016. Enhancing and combining sequential and tree lstm for natural language inference. ArXiv preprint abs\/1609.06038 (2016). https:\/\/arxiv.org\/abs\/1609.06038","journal-title":"ArXiv preprint"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1152"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.3115\/1687878.1687944"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N19-1423"},{"issue":"1","key":"e_1_3_2_13_2","first-page":"183","article-title":"QuillBot as an online tool: Students\u2019 alternative in paraphrasing and rewriting of English writing","volume":"9","author":"Fitria Tira Nur","year":"2021","unstructured":"Tira Nur Fitria. 2021. QuillBot as an online tool: Students\u2019 alternative in paraphrasing and rewriting of English writing. Englisia: Journal of Language, Education, and Humanities 9, 1 (2021), 183\u2013196.","journal-title":"Englisia: Journal of Language, Education, and Humanities"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.brainres.2006.08.038"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1002"},{"key":"e_1_3_2_16_2","unstructured":"Yichen Gong Heng Luo and Jian Zhang. 2018. Natural language inference over interaction space. In Proceedings of the 6th International Conference on Learning Representations: Conference Track Proceedings (ICLR\u201918) . https:\/\/openreview.net\/forum?id=r1dHXnH6-"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1080\/01690960902965951"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/2983323.2983769"},{"key":"e_1_3_2_19_2","unstructured":"Baotian Hu Zhengdong Lu Hang Li and Qingcai Chen. 2014. Convolutional neural network architectures for matching natural language sentences. In Proceedings of the 27th International Conference on Neural Information Processing Systems (NIPS\u201914) Vol. 2. 2042\u20132050. https:\/\/proceedings.neurips.cc\/paper\/2014\/hash\/b9d487a30398d42ecff55c228ed5652b-Abstract.html"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/2505515.2505665"},{"key":"e_1_3_2_21_2","doi-asserted-by":"crossref","unstructured":"Tushar Khot Ashish Sabharwal and Peter Clark. 2018. SciTaiL: A textual entailment dataset from science question answering. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI\u201918) the 30th Innovative Applications of Artificial Intelligence (IAAI\u201918) and the 8th AAAI Symposium on Education Advances in Artificial Intelligence (EAAI\u201918) . 5189\u20135198. https:\/\/www.aaai.org\/ocs\/index.php\/AAAI\/AAAI18\/paper\/view\/17368","DOI":"10.1609\/aaai.v32i1.12022"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33016586"},{"key":"e_1_3_2_23_2","volume-title":"Proceedings of the 3rd International Conference on Learning Representations (ICLR\u201915)","author":"Kingma Diederik P.","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In Proceedings of the 3rd International Conference on Learning Representations (ICLR\u201915). http:\/\/arxiv.org\/abs\/1412.6980"},{"key":"e_1_3_2_24_2","volume-title":"Proceedings of the 5th International Conference on Learning Representations: Conference Track Proceedings (ICLR\u201917)","author":"Kipf Thomas N.","year":"2017","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In Proceedings of the 5th International Conference on Learning Representations: Conference Track Proceedings (ICLR\u201917). https:\/\/openreview.net\/forum?id=SJU4ayYgl"},{"key":"e_1_3_2_25_2","first-page":"343","article-title":"Semantic matching in search","author":"Li Hang","year":"2014","unstructured":"Hang Li and Jun Xu. 2014. Semantic matching in search. Foundations and Trends in Information Retrieval 7, 5 (2014), 343\u2013469.","journal-title":"Foundations and Trends in Information Retrieval"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.coling-main.293"},{"key":"e_1_3_2_27_2","article-title":"Stochastic answer networks for natural language inference","volume":"1804","author":"Liu Xiaodong","year":"2018","unstructured":"Xiaodong Liu, Kevin Duh, and Jianfeng Gao. 2018. Stochastic answer networks for natural language inference. arXiv preprint abs\/1804.07888 (2018). https:\/\/arxiv.org\/abs\/1804.07888","journal-title":"arXiv preprint"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1184"},{"key":"e_1_3_2_29_2","article-title":"RoBERTa: A robustly optimized BERT pretraining approach","volume":"1907","author":"Liu Yinhan","year":"2019","unstructured":"Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. RoBERTa: A robustly optimized BERT pretraining approach. arXiv preprint abs\/1907.11692 (2019). https:\/\/arxiv.org\/abs\/1907.11692","journal-title":"arXiv preprint"},{"key":"e_1_3_2_30_2","unstructured":"Zhengdong Lu and Hang Li. 2013. A deep architecture for matching short texts. In Proceedings of the 26th International Conference on Neural Information Processing Systems Vol. 1 (NIPS\u201913) . 1367\u20131375. https:\/\/proceedings.neurips.cc\/paper\/2013\/hash\/8a0e1141fd37fa5b98d5bb769ba1a7cc-Abstract.html"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.512"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P14-5010"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052579"},{"key":"e_1_3_2_34_2","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1016\/j.ipm.2017.01.002","article-title":"Paraphrase identification and semantic text similarity analysis in Arabic news tweets using lexical, syntactic, and semantic features","author":"Mohammad Al-Smadi","year":"2017","unstructured":"Al-Smadi Mohammad, Zain Jaradat, Al-Ayyoub Mahmoud, and Yaser Jararweh. 2017. Paraphrase identification and semantic text similarity analysis in Arabic news tweets using lexical, syntactic, and semantic features. Information Processing & Management 53, 3 (2017), 640\u2013652.","journal-title":"Information Processing & Management"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-2022"},{"key":"e_1_3_2_36_2","doi-asserted-by":"crossref","unstructured":"Liang Pang Yanyan Lan Jiafeng Guo Jun Xu Shengxian Wan and Xueqi Cheng. 2016. Text matching as image recognition. In Proceedings of the 30th AAAI Conference on Artificial Intelligence . 2793\u20132799.. http:\/\/www.aaai.org\/ocs\/index.php\/AAAI\/AAAI16\/paper\/view\/11895","DOI":"10.1609\/aaai.v30i1.10341"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D16-1244"},{"key":"e_1_3_2_38_2","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s10579-009-9114-z","article-title":"Cross-language plagiarism detection","author":"Potthast Martin","year":"2011","unstructured":"Martin Potthast, Alberto Barr\u00f3n-Cede\u00f1o, Benno Stein, and Paolo Rosso. 2011. Cross-language plagiarism detection. Language Resources and Evaluation 45 (2011), 45\u201362.","journal-title":"Language Resources and Evaluation"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.eacl-main.228"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/2567948.2577348"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/613"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1479"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1185"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/615"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W17-4121"},{"key":"e_1_3_2_46_2","unstructured":"Petar Velickovic Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Li\u00f2 and Yoshua Bengio. 2018. Graph attention networks. In Proceedings of the 6th International Conference on Learning Representations: Conference Track Proceedings (ICLR\u201918) . https:\/\/openreview.net\/forum?id=rJXMpikCZ"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.5555\/2832415.2832438"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313562"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33017208"},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/579"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/579"},{"key":"e_1_3_2_52_2","article-title":"A comprehensive survey on graph neural networks","author":"Wu Zonghan","year":"2021","unstructured":"Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S. Yu Philip. 2021. A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems 32, 1 (2021), 4\u201324.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080809"},{"key":"e_1_3_2_54_2","first-page":"938","volume-title":"Proceedings of the 29th International Conference on Computational Linguistics","author":"Xu Chen","year":"2022","unstructured":"Chen Xu, Jun Xu, Zhenhua Dong, and Ji-Rong Wen. 2022. Semantic sentence matching via interacting syntax graphs. In Proceedings of the 29th International Conference on Computational Linguistics. 938\u2013949. https:\/\/aclanthology.org\/2022.coling-1.78"},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3291380"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1465"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33017370"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450115"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.297"},{"key":"e_1_3_2_60_2","first-page":"9628","volume-title":"Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI\u201920), the 32nd Innovative Applications of Artificial Intelligence Conference (IAAI\u201920), and the 10th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI\u201920)","author":"Zhang Zhuosheng","year":"2020","unstructured":"Zhuosheng Zhang, Yuwei Wu, Hai Zhao, Zuchao Li, Shuailiang Zhang, Xi Zhou, and Xiang Zhou. 2020. Semantics-aware BERT for language understanding. In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI\u201920), the 32nd Innovative Applications of Artificial Intelligence Conference (IAAI\u201920), and the 10th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI\u201920). 9628\u20139635. https:\/\/aaai.org\/ojs\/index.php\/AAAI\/article\/view\/6510"}],"container-title":["ACM Transactions on Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3609795","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3609795","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:38:01Z","timestamp":1750178281000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3609795"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,27]]},"references-count":59,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,3,31]]}},"alternative-id":["10.1145\/3609795"],"URL":"https:\/\/doi.org\/10.1145\/3609795","relation":{},"ISSN":["1046-8188","1558-2868"],"issn-type":[{"type":"print","value":"1046-8188"},{"type":"electronic","value":"1558-2868"}],"subject":[],"published":{"date-parts":[[2023,9,27]]},"assertion":[{"value":"2021-12-16","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-07-05","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-09-27","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}