{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T17:03:05Z","timestamp":1742922185683,"version":"3.40.3"},"publisher-location":"Cham","reference-count":45,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031183140"},{"type":"electronic","value":"9783031183157"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-18315-7_3","type":"book-chapter","created":{"date-parts":[[2022,10,5]],"date-time":"2022-10-05T23:03:52Z","timestamp":1665011032000},"page":"31-47","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["ConIsI: A Contrastive Framework with Inter-sentence Interaction for Self-supervised Sentence Representation"],"prefix":"10.1007","author":[{"given":"Meng","family":"Sun","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Degen","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,6]]},"reference":[{"key":"3_CR1","doi-asserted-by":"crossref","unstructured":"Agirre, E., et al.: SemEval-2015 task 2: semantic textual similarity, English, Spanish and pilot on interpretability. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 252\u2013263 (2015)","DOI":"10.18653\/v1\/S15-2045"},{"key":"3_CR2","doi-asserted-by":"crossref","unstructured":"Agirre, E., et al.: SemEval-2014 task 10: multilingual semantic textual similarity. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 81\u201391 (2014)","DOI":"10.3115\/v1\/S14-2010"},{"key":"3_CR3","doi-asserted-by":"crossref","unstructured":"Agirre, E., et al.: SemEval-2016 task 1: semantic textual similarity, monolingual and cross-lingual evaluation. In: SemEval-2016. 10th International Workshop on Semantic Evaluation, 16\u201317 June 2016, San Diego, CA, pp. 497\u2013511. ACL (Association for Computational Linguistics), Stroudsburg (2016)","DOI":"10.18653\/v1\/S16-1081"},{"key":"3_CR4","unstructured":"Agirre, E., Cer, D., Diab, M., Gonzalez-Agirre, A.: SemEval-2012 task 6: a pilot on semantic textual similarity. In: * SEM 2012: 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 (SemEval 2012), pp. 385\u2013393 (2012)"},{"key":"3_CR5","unstructured":"Agirre, E., Cer, D., Diab, M., Gonzalez-Agirre, A., Guo, W.: * SEM 2013 shared task: semantic textual similarity. In: Second Joint Conference on Lexical and Computational Semantics (* SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity, pp. 32\u201343 (2013)"},{"key":"3_CR6","doi-asserted-by":"crossref","unstructured":"Bowman, S., Angeli, G., Potts, C., Manning, C.D.: 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 (2015)","DOI":"10.18653\/v1\/D15-1075"},{"key":"3_CR7","unstructured":"Carlsson, F., Gyllensten, A.C., Gogoulou, E., Hellqvist, E.Y., Sahlgren, M.: Semantic re-tuning with contrastive tension. In: International Conference on Learning Representations (2020)"},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"Cer, D., Diab, M., Agirre, E., Lopez-Gazpio, I., Specia, L.: SemEval-2017 task 1: semantic textual similarity-multilingual and cross-lingual focused evaluation. arXiv preprint arXiv:1708.00055 (2017)","DOI":"10.18653\/v1\/S17-2001"},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"Cer, D., et al.: Universal sentence encoder for English. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pp. 169\u2013174 (2018)","DOI":"10.18653\/v1\/D18-2029"},{"key":"3_CR10","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597\u20131607. PMLR (2020)"},{"key":"3_CR11","unstructured":"Chen, T., Luo, C., Li, L.: Intriguing properties of contrastive losses. In: Advances in Neural Information Processing Systems, vol. 34 (2021)"},{"key":"3_CR12","doi-asserted-by":"crossref","unstructured":"Chen, T., Sun, Y., Shi, Y., Hong, L.: On sampling strategies for neural network-based collaborative filtering. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 767\u2013776 (2017)","DOI":"10.1145\/3097983.3098202"},{"key":"3_CR13","doi-asserted-by":"crossref","unstructured":"Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 539\u2013546. IEEE (2005)","DOI":"10.1109\/CVPR.2005.202"},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Conneau, A., Kiela, D., Schwenk, H., Barrault, L., Bordes, A.: Supervised learning of universal sentence representations from natural language inference data. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 670\u2013680. Association for Computational Linguistics (2017)","DOI":"10.18653\/v1\/D17-1070"},{"key":"3_CR15","unstructured":"Conneau, A., Kiela, D.: SentEval: an evaluation toolkit for universal sentence representations. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) (2018)"},{"key":"3_CR16","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL-HLT (1) (2019)"},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"Gao, T., Yao, X., Chen, D.: SimCSE: simple contrastive learning of sentence embeddings. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 6894\u20136910 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.552"},{"key":"3_CR18","doi-asserted-by":"crossref","unstructured":"Giorgi, J., Nitski, O., Wang, B., Bader, G.: DeCLUTR: deep contrastive learning for unsupervised textual representations. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 879\u2013895 (2021)","DOI":"10.18653\/v1\/2021.acl-long.72"},{"key":"3_CR19","unstructured":"Grill, J.B., et al.: Bootstrap your own latent-a new approach to self-supervised learning. In: Advances in Neural Information Processing Systems, vol. 33, pp. 21271\u201321284 (2020)"},{"key":"3_CR20","doi-asserted-by":"crossref","unstructured":"Hill, F., Cho, K., Korhonen, A.: Learning distributed representations of sentences from unlabelled data. In: Proceedings of NAACL-HLT, pp. 1367\u20131377 (2016)","DOI":"10.18653\/v1\/N16-1162"},{"key":"3_CR21","doi-asserted-by":"crossref","unstructured":"Kim, T., Yoo, K.M., Lee, S.G.: Self-guided contrastive learning for BERT sentence representations. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 2528\u20132540 (2021)","DOI":"10.18653\/v1\/2021.acl-long.197"},{"key":"3_CR22","unstructured":"Kiros, R., et al.: Skip-thought vectors. In: Advances in Neural Information Processing Systems, pp. 3294\u20133302 (2015)"},{"key":"3_CR23","doi-asserted-by":"crossref","unstructured":"Lee, H., Hudson, D.A., Lee, K., Manning, C.D.: SLM: learning a discourse language representation with sentence unshuffling. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1551\u20131562 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.120"},{"key":"3_CR24","doi-asserted-by":"crossref","unstructured":"Li, B., Zhou, H., He, J., Wang, M., Yang, Y., Li, L.: On the sentence embeddings from pre-trained language models. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9119\u20139130 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.733"},{"key":"3_CR25","doi-asserted-by":"crossref","unstructured":"Li, D., et al.: VIRT: improving representation-based models for text matching through virtual interaction. arXiv preprint arXiv:2112.04195 (2021)","DOI":"10.18653\/v1\/2022.emnlp-main.59"},{"key":"3_CR26","doi-asserted-by":"crossref","unstructured":"Liu, F., Vuli\u0107, I., Korhonen, A., Collier, N.: Fast, effective, and self-supervised: transforming masked language models into universal lexical and sentence encoders. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 1442\u20131459 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.109"},{"key":"3_CR27","unstructured":"Liu, Y., et al.: RoBERTa: a robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019)"},{"key":"3_CR28","unstructured":"Logeswaran, L., Lee, H.: An efficient framework for learning sentence representations. In: International Conference on Learning Representations (2018)"},{"key":"3_CR29","unstructured":"Lu, Y., et al.: Ernie-search: bridging cross-encoder with dual-encoder via self on-the-fly distillation for dense passage retrieval. arXiv preprint arXiv:2205.09153 (2022)"},{"key":"3_CR30","unstructured":"Marelli, M., et al.: A sick cure for the evaluation of compositional distributional semantic models. In: Lrec, pp. 216\u2013223. Reykjavik (2014)"},{"key":"3_CR31","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111\u20133119 (2013)"},{"key":"3_CR32","doi-asserted-by":"crossref","unstructured":"Miller, G.A.: WordNet: a lexical database for English. In: Human Language Technology: Proceedings of a Workshop Held at Plainsboro, New Jersey, 21\u201324 March 1993 (1993)","DOI":"10.3115\/1075671.1075788"},{"key":"3_CR33","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532\u20131543 (2014)","DOI":"10.3115\/v1\/D14-1162"},{"key":"3_CR34","doi-asserted-by":"crossref","unstructured":"Reimers, N., et al.: Sentence-BERT: sentence embeddings using Siamese BERT-networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, pp. 671\u2013688. Association for Computational Linguistics (2019)","DOI":"10.18653\/v1\/D19-1410"},{"key":"3_CR35","unstructured":"Robinson, J., Sun, L., Yu, K., Batmanghelich, K., Jegelka, S., Sra, S.: Can contrastive learning avoid shortcut solutions? In: Advances in Neural Information Processing Systems, vol. 34 (2021)"},{"key":"3_CR36","unstructured":"Su, J., Cao, J., Liu, W., Ou, Y.: Whitening sentence representations for better semantics and faster retrieval. arXiv preprint arXiv:2103.15316 (2021)"},{"key":"3_CR37","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"3_CR38","doi-asserted-by":"crossref","unstructured":"Wang, S., et al.: Cross-thought for sentence encoder pre-training. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 412\u2013421 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.30"},{"key":"3_CR39","doi-asserted-by":"crossref","unstructured":"Wang, Z., Wang, W., Zhu, H., Liu, M., Qin, B., Wei, F.: Distilled dual-encoder model for vision-language understanding. arXiv preprint arXiv:2112.08723 (2021)","DOI":"10.18653\/v1\/2022.emnlp-main.608"},{"key":"3_CR40","doi-asserted-by":"crossref","unstructured":"Williams, A., Nangia, N., Bowman, S.: A broad-coverage challenge corpus for sentence understanding through inference. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp. 1112\u20131122 (2018)","DOI":"10.18653\/v1\/N18-1101"},{"key":"3_CR41","unstructured":"Wu, Z., Wang, S., Gu, J., Khabsa, M., Sun, F., Ma, H.: CLEAR: contrastive learning for sentence representation. arXiv preprint arXiv:2012.15466 (2020)"},{"key":"3_CR42","doi-asserted-by":"crossref","unstructured":"Yan, Y., Li, R., Wang, S., Zhang, F., Wu, W., Xu, W.: ConSERT: a contrastive framework for self-supervised sentence representation transfer. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 5065\u20135075 (2021)","DOI":"10.18653\/v1\/2021.acl-long.393"},{"key":"3_CR43","doi-asserted-by":"crossref","unstructured":"Yang, Z., Yang, Y., Cer, D., Law, J., Darve, E.: Universal sentence representation learning with conditional masked language model. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 6216\u20136228 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.502"},{"key":"3_CR44","doi-asserted-by":"crossref","unstructured":"Zhang, Y., He, R., Liu, Z., Bing, L., Li, H.: Bootstrapped unsupervised sentence representation learning. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 5168\u20135180 (2021)","DOI":"10.18653\/v1\/2021.acl-long.402"},{"key":"3_CR45","doi-asserted-by":"crossref","unstructured":"Zhang, Y., He, R., Liu, Z., Lim, K.H., Bing, L.: An unsupervised sentence embedding method by mutual information maximization. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1601\u20131610 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.124"}],"container-title":["Lecture Notes in Computer Science","Chinese Computational Linguistics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-18315-7_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,5]],"date-time":"2024-10-05T07:40:19Z","timestamp":1728114019000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-18315-7_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031183140","9783031183157"],"references-count":45,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-18315-7_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"6 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CCL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China National Conference on Chinese Computational Linguistics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nanchang","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cncl2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/cips-cl.org\/static\/CCL2022\/en\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"www.softconf.com","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"293","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"22","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"8% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"293 submissions included both Chinese and English papers.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}