{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:26:02Z","timestamp":1740122762245,"version":"3.37.3"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T00:00:00Z","timestamp":1625529600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T00:00:00Z","timestamp":1625529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61672263"],"award-info":[{"award-number":["61672263"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2021,10]]},"DOI":"10.1007\/s11063-021-10561-3","type":"journal-article","created":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T03:37:36Z","timestamp":1625542656000},"page":"3677-3692","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Novel Architecture with Separate Comparison and Interaction Modules for Chinese Semantic Sentence Matching"],"prefix":"10.1007","volume":"53","author":[{"given":"Qidong","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,7,6]]},"reference":[{"key":"10561_CR1","unstructured":"Wang ZG, Mi HT, Ittycheriah A (2016) Sentence similarity learning by lexical decomposition and composition. In: Proceedings of the 26th International Conference on Computational Linguistics, pp 1340\u20131349"},{"key":"10561_CR2","unstructured":"Yin WP, Schutze H, Xiang B, Zhou BW (2018) ABCNN: attention-based convolutional neural network for modeling sentence pairs. arXiv:1512.05193"},{"key":"10561_CR3","doi-asserted-by":"publisher","unstructured":"Bowman SR, Angeli G, Potts C, Manning CD (2015) A large annotated corpus for learning natural language inference.Computer Science. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp 632\u2013642. https:\/\/doi.org\/10.18653\/v1\/D15-1075","DOI":"10.18653\/v1\/D15-1075"},{"key":"10561_CR4","unstructured":"Bar D, Biemann C, Gurevych I, Zesch T (2012) Ukp: Computing semantic textual similarity by combining multiple content similarity measures. In: Proceedings of the First Joint Conference on Lexical and Computational Semantics-Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation, pp 435\u2013440"},{"key":"10561_CR5","unstructured":"Jimenez S, Becerra C, Gelbukh A (2012) Soft cardinality: a parameterized similarity function for text comparison. In: Proceedings of the First Joint Conference on Lexical and Computational Semantics-Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation, pp 449\u2013453"},{"key":"10561_CR6","first-page":"737","volume":"6","author":"B Bromley","year":"1993","unstructured":"Bromley B, Guyon I et al (1993) Signature verification using a siamese time delay neural network. Adv Neural Inform Process Syst 6:737\u2013744","journal-title":"Adv Neural Inform Process Syst"},{"key":"10561_CR7","doi-asserted-by":"publisher","unstructured":"Chen Q, Zhu XD, Ling ZH, Wei S, Jiang H, Inkpen D (2017) Enhanced LSTM for natural language inference. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 1657\u20131668. https:\/\/doi.org\/10.18653\/v1\/P17-1152","DOI":"10.18653\/v1\/P17-1152"},{"key":"10561_CR8","doi-asserted-by":"crossref","unstructured":"Pang L, Lan YY, Guo JF, Xu J, Wan SX, Cheng XQ (2016) Text matching as image recognition. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, pp 1145\u20131152","DOI":"10.1609\/aaai.v30i1.10341"},{"key":"10561_CR9","doi-asserted-by":"crossref","unstructured":"Kim S, Kang I, Kwak N (2018) Semantic sentence matching with densely-connected recurrent and co-attentive information. arXiv preprint arXiv:1805.11360","DOI":"10.1609\/aaai.v33i01.33016586"},{"key":"10561_CR10","unstructured":"Wan SX, Lan YY, Guo JF (2015) A deep architecture for semantic matching with multiple positional sentence representations. arXiv preprint arXiv:1511.08277"},{"issue":"4","key":"10561_CR11","doi-asserted-by":"publisher","first-page":"14609","DOI":"10.1007\/s11042-018-7063-5","volume":"79","author":"HY Lai","year":"2020","unstructured":"Lai HY, Tao YZ, Wang CL (2020) Bi-directional attention comparison for semantic sentence matching. Multimedia Tools Appl 79(4):14609\u201314624. https:\/\/doi.org\/10.1007\/s11042-018-7063-5","journal-title":"Multimedia Tools Appl"},{"key":"10561_CR12","unstructured":"Bill D, Chris Q, and Chris B (2004) Unsupervised construction of large paraphrase corpora: exploiting massively parallel news sources. In: Proceedings of COLING, pp 350\u2013356. https:\/\/www.aclweb.org\/anthology\/C04-1051"},{"key":"10561_CR13","doi-asserted-by":"crossref","unstructured":"Cer D, Diab M, Agirre E, LopezGazpio I, Specia L (2017) Semeval-2017 task 1: semantic textual similarity-multilingual and cross-lingual focused evaluation. In: Proceedings of the 10th International Workshop on Semantic Evaluation. arXiv:1708.00055","DOI":"10.18653\/v1\/S17-2001"},{"key":"10561_CR14","doi-asserted-by":"crossref","unstructured":"Nakov P, Hoogeveen D, MArquez L, Moschitti A, Mubarak H, Baldwin T, Verspoor K (2019) SemEval-2017 task 3: Community question answering. arXiv preprint arXiv:1912.00730","DOI":"10.18653\/v1\/S17-2003"},{"key":"10561_CR15","unstructured":"Csernai K (2017) Quora question pair dataset. https:\/\/www.kaggle.com\/c\/quora-question-pairs\/data"},{"key":"10561_CR16","doi-asserted-by":"crossref","unstructured":"Bowman SR, Angeli G, Potts C, Manning CD (2015) A large annotated corpus for learning natural language inference. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp 632\u2013642","DOI":"10.18653\/v1\/D15-1075"},{"key":"10561_CR17","doi-asserted-by":"crossref","unstructured":"Williams A, Nangia N, Bowman SR (2017) A broad-coverage challenge corpus for sentence understanding through inference. arXiv preprint arXiv:1704.05426","DOI":"10.18653\/v1\/N18-1101"},{"key":"10561_CR18","unstructured":"Ant Financial. Ant Financial Artificial Competition"},{"key":"10561_CR19","unstructured":"CCKS (2018) WeBank Intelligent Customer Service Question Matching Competition. https:\/\/biendata.com\/competition\/CCKS2018$$\\_$$3"},{"key":"10561_CR20","unstructured":"PPDAI 3rd Magic Mirror Data Application Contest. https:\/\/www.ppdai.ai\/mirror\/goToMirrorDetail?mirrorId=1"},{"key":"10561_CR21","unstructured":"CHIP 2018-4th China Health Information Processing Conference. https:\/\/biendata.com\/competition\/chip2018"},{"key":"10561_CR22","doi-asserted-by":"publisher","unstructured":"Wang ZG, Hamza W, Florian R (2017) Bilateral multi-perspective matching for natural language sentences. pp 4144\u20134150. https:\/\/doi.org\/10.24963\/ijcai.2017\/579","DOI":"10.24963\/ijcai.2017\/579"},{"key":"10561_CR23","doi-asserted-by":"publisher","unstructured":"Parikh AP, Tckstrm O, Das D, Uszkoreit J (2016) A decomposable attention model for natural language inference. pp 2249\u20132255. https:\/\/doi.org\/10.18653\/v1\/D16-1244","DOI":"10.18653\/v1\/D16-1244"},{"key":"10561_CR24","doi-asserted-by":"crossref","unstructured":"Li XY, Meng YX, Sun XF, Han QH, Yuan A, Li JW (2019) Is word segmentation necessary for deep learning of Chinese representation. arXiv preprint arXiv: 1905.05526","DOI":"10.18653\/v1\/P19-1314"},{"key":"10561_CR25","unstructured":"Junyi S. jieba. https:\/\/github.com\/fxsjy\/jieba"},{"key":"10561_CR26","unstructured":"Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representation in vector space. arXiv preprint arXiv:1301.3781"},{"key":"10561_CR27","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167"},{"issue":"1","key":"10561_CR28","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton GE, Krizhevsky A, Sutskever I, Salak-hutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929\u20131958","journal-title":"J Mach Learn Res"},{"key":"10561_CR29","doi-asserted-by":"crossref","unstructured":"Tompson J, Goroshin R, Jain A, LeCun Y, Bregler C (2015) Efficient object localization using convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 648\u2013656","DOI":"10.1109\/CVPR.2015.7298664"},{"key":"10561_CR30","unstructured":"Lei Ba J, Kiros JR, Hinton GE (2016) Layer normalization. arXiv preprint arXiv: 1607.06450"},{"key":"10561_CR31","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all your need. arXiv preprint arXiv:1706.03762"},{"key":"10561_CR32","unstructured":"Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. In: Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, pp 315\u2013323"},{"key":"10561_CR33","unstructured":"Timothy D (2016) Incorporating nesterov momentum into adam. In: Proceedings of Workshop Track International Conference on Learning Representations"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-021-10561-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-021-10561-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-021-10561-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T23:06:57Z","timestamp":1672700817000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-021-10561-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,6]]},"references-count":33,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2021,10]]}},"alternative-id":["10561"],"URL":"https:\/\/doi.org\/10.1007\/s11063-021-10561-3","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"type":"print","value":"1370-4621"},{"type":"electronic","value":"1573-773X"}],"subject":[],"published":{"date-parts":[[2021,7,6]]},"assertion":[{"value":"7 June 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 July 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}