{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T17:41:58Z","timestamp":1743010918522,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":43,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819962068"},{"type":"electronic","value":"9789819962075"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-981-99-6207-5_28","type":"book-chapter","created":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T08:45:34Z","timestamp":1695113134000},"page":"449-463","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SentBench: Comprehensive Evaluation of\u00a0Self-Supervised Sentence Representation with\u00a0Benchmark Construction"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-7177-5500","authenticated-orcid":false,"given":"Xiaoming","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5857-9663","authenticated-orcid":false,"given":"Hongyu","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1304-6302","authenticated-orcid":false,"given":"Xianpei","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8750-6295","authenticated-orcid":false,"given":"Le","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,20]]},"reference":[{"key":"28_CR1","unstructured":"Adi, Y., Kermany, E., Belinkov, Y., Lavi, O., Goldberg, Y.: Fine-grained analysis of sentence embeddings using auxiliary prediction tasks. arXiv preprint arXiv:1608.04207 (2016)"},{"key":"28_CR2","doi-asserted-by":"publisher","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. Association for Computational Linguistics, Denver, Colorado (2015). https:\/\/doi.org\/10.18653\/v1\/S15-2045","DOI":"10.18653\/v1\/S15-2045"},{"key":"28_CR3","doi-asserted-by":"publisher","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. Association for Computational Linguistics, Dublin, Ireland (2014). https:\/\/doi.org\/10.3115\/v1\/S14-2010","DOI":"10.3115\/v1\/S14-2010"},{"key":"28_CR4","doi-asserted-by":"publisher","unstructured":"Agirre, E., et al.: SemEval-2016 task 1: Semantic textual similarity, monolingual and cross-lingual evaluation. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016). pp. 497\u2013511. Association for Computational Linguistics, San Diego, California (2016). https:\/\/doi.org\/10.18653\/v1\/S16-1081","DOI":"10.18653\/v1\/S16-1081"},{"key":"28_CR5","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 \u2013 Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (2012). pp. 385\u2013393. Association for Computational Linguistics, Montr\u00e9al, Canada (2012), https:\/\/aclanthology.org\/S12-1051"},{"key":"28_CR6","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. Association for Computational Linguistics, Atlanta, Georgia, USA (2013), https:\/\/aclanthology.org\/S13-1004"},{"key":"28_CR7","doi-asserted-by":"publisher","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. Association for Computational Linguistics, Brussels, Belgium (2018). https:\/\/doi.org\/10.18653\/v1\/D18-2029","DOI":"10.18653\/v1\/D18-2029"},{"key":"28_CR8","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). European Language Resources Association (ELRA), Miyazaki, Japan (2018), https:\/\/aclanthology.org\/L18-1269"},{"key":"28_CR9","doi-asserted-by":"publisher","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, Copenhagen, Denmark (2017). https:\/\/doi.org\/10.18653\/v1\/D17-1070","DOI":"10.18653\/v1\/D17-1070"},{"key":"28_CR10","doi-asserted-by":"publisher","unstructured":"Conneau, A., Kruszewski, G., Lample, G., Barrault, L., Baroni, M.: What you can cram into a single vector: probing sentence embeddings for linguistic properties. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 2126\u20132136. Association for Computational Linguistics, Melbourne, Australia (2018). https:\/\/doi.org\/10.18653\/v1\/P18-1198","DOI":"10.18653\/v1\/P18-1198"},{"key":"28_CR11","doi-asserted-by":"publisher","unstructured":"Demszky, D., Movshovitz-Attias, D., Ko, J., Cowen, A., Nemade, G., Ravi, S.: GoEmotions: A dataset of fine-grained emotions. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. pp. 4040\u20134054. Association for Computational Linguistics, Online (2020). https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.372","DOI":"10.18653\/v1\/2020.acl-main.372"},{"key":"28_CR12","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). pp. 4171\u20134186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https:\/\/doi.org\/10.18653\/v1\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"28_CR13","doi-asserted-by":"publisher","unstructured":"Feng, S.Y., et al.: A survey of data augmentation approaches for NLP. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. pp. 968\u2013988. Association for Computational Linguistics, Online (2021). https:\/\/doi.org\/10.18653\/v1\/2021.findings-acl.84","DOI":"10.18653\/v1\/2021.findings-acl.84"},{"key":"28_CR14","doi-asserted-by":"publisher","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. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic (2021). https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.552","DOI":"10.18653\/v1\/2021.emnlp-main.552"},{"key":"28_CR15","unstructured":"Ho, C.H., Nvasconcelos, N.: Contrastive learning with adversarial examples. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems. vol.\u00a033, pp. 17081\u201317093. Curran Associates, Inc. (2020), https:\/\/proceedings.neurips.cc\/paper\/2020\/file\/c68c9c8258ea7d85472dd6fd0015f047-Paper.pdf"},{"key":"28_CR16","doi-asserted-by":"publisher","unstructured":"Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. p. 168\u2013177. KDD \u201904, Association for Computing Machinery, New York, NY, USA (2004). https:\/\/doi.org\/10.1145\/1014052.1014073","DOI":"10.1145\/1014052.1014073"},{"key":"28_CR17","doi-asserted-by":"publisher","unstructured":"Jhamtani, H., Gangal, V., Hovy, E., Nyberg, E.: Shakespearizing modern language using copy-enriched sequence to sequence models. In: Proceedings of the Workshop on Stylistic Variation. pp. 10\u201319. Association for Computational Linguistics, Copenhagen, Denmark (Sep 2017). https:\/\/doi.org\/10.18653\/v1\/W17-4902","DOI":"10.18653\/v1\/W17-4902"},{"key":"28_CR18","unstructured":"Kiros, R., Zhu, Y., Salakhutdinov, R.R., Zemel, R., Urtasun, R., Torralba, A., Fidler, S.: Skip-thought vectors. In: Cortes, C., Lawrence, N., Lee, D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems. vol. 28. Curran Associates, Inc. (2015), https:\/\/proceedings.neurips.cc\/paper\/2015\/file\/f442d33fa06832082290ad8544a8da27-Paper.pdf"},{"key":"28_CR19","unstructured":"Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., Soricut, R.: Albert: A lite bert for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942 (2019)"},{"key":"28_CR20","doi-asserted-by":"publisher","unstructured":"Lewis, M., et al.: BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. pp. 7871\u20137880. Association for Computational Linguistics, Online (2020). https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.703","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"28_CR21","unstructured":"Liu, Y., et al.: Roberta: A robustly optimized Bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019)"},{"key":"28_CR22","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: Bengio, Y., LeCun, Y. (eds.) 1st International Conference on Learning Representations, ICLR 2013, Scottsdale, Arizona, USA, May 2-4, 2013, Workshop Track Proceedings (2013), http:\/\/arxiv.org\/abs\/1301.3781"},{"key":"28_CR23","doi-asserted-by":"publisher","unstructured":"Mostafazadeh, N., et al.: A corpus and cloze evaluation for deeper understanding of commonsense stories. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. pp. 839\u2013849. Association for Computational Linguistics, San Diego, California (2016). https:\/\/doi.org\/10.18653\/v1\/N16-1098","DOI":"10.18653\/v1\/N16-1098"},{"key":"28_CR24","doi-asserted-by":"crossref","unstructured":"Ni, J., et al.: Sentence-t5: scalable sentence encoders from pre-trained text-to-text models. In: Findings of the Association for Computational Linguistics: ACL 2022. pp. 1864\u20131874. Association for Computational Linguistics, Dublin, Ireland (2022), https:\/\/aclanthology.org\/2022.findings-acl.146","DOI":"10.18653\/v1\/2022.findings-acl.146"},{"key":"28_CR25","unstructured":"Van den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)"},{"key":"28_CR26","doi-asserted-by":"publisher","unstructured":"Pang, B., Lee, L.: A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04). pp. 271\u2013278. Barcelona, Spain (2004). https:\/\/doi.org\/10.3115\/1218955.1218990","DOI":"10.3115\/1218955.1218990"},{"key":"28_CR27","doi-asserted-by":"publisher","unstructured":"Pang, B., Lee, L.: Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. In: Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL\u201905). pp. 115\u2013124. Association for Computational Linguistics, Ann Arbor, Michigan (2005). https:\/\/doi.org\/10.3115\/1219840.1219855","DOI":"10.3115\/1219840.1219855"},{"key":"28_CR28","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":"28_CR29","doi-asserted-by":"publisher","unstructured":"Peters, M.E., et al.: Deep contextualized word representations. 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. 2227\u20132237. Association for Computational Linguistics, New Orleans, Louisiana (2018). https:\/\/doi.org\/10.18653\/v1\/N18-1202","DOI":"10.18653\/v1\/N18-1202"},{"key":"28_CR30","unstructured":"Reimers, N., Beyer, P., Gurevych, I.: Task-oriented intrinsic evaluation of semantic textual similarity. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers. pp. 87\u201396. The COLING 2016 Organizing Committee, Osaka, Japan (2016), https:\/\/aclanthology.org\/C16-1009"},{"key":"28_CR31","unstructured":"Socher, R., et al.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. pp. 1631\u20131642. Association for Computational Linguistics, Seattle, Washington, USA (2013), https:\/\/aclanthology.org\/D13-1170"},{"key":"28_CR32","doi-asserted-by":"publisher","unstructured":"Talmor, A., Herzig, J., Lourie, N., Berant, J.: CommonsenseQA: a question answering challenge targeting commonsense knowledge. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). pp. 4149\u20134158. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https:\/\/doi.org\/10.18653\/v1\/N19-1421","DOI":"10.18653\/v1\/N19-1421"},{"key":"28_CR33","doi-asserted-by":"crossref","unstructured":"Voorhees, E.M., Tice, D.M.: Building a question answering test collection. In: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 200\u2013207 (2000)","DOI":"10.1145\/345508.345577"},{"key":"28_CR34","doi-asserted-by":"publisher","unstructured":"Wang, B., Kuo, C.C.J., Li, H.: Just rank: rethinking evaluation with word and sentence similarities. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 6060\u20136077. Association for Computational Linguistics, Dublin, Ireland (May 2022). https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.419","DOI":"10.18653\/v1\/2022.acl-long.419"},{"key":"28_CR35","unstructured":"Wang, T., Isola, P.: Understanding contrastive representation learning through alignment and uniformity on the hypersphere. In: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13\u201318 July 2020, Virtual Event. Proceedings of Machine Learning Research, vol.\u00a0119, pp. 9929\u20139939. PMLR (2020), http:\/\/proceedings.mlr.press\/v119\/wang20k.html"},{"issue":"2","key":"28_CR36","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1007\/s10579-005-7880-9","volume":"39","author":"J Wiebe","year":"2005","unstructured":"Wiebe, J., Wilson, T., Cardie, C.: Annotating expressions of opinions and emotions in language. Lang. Resour. Eval. 39(2), 165\u2013210 (2005)","journal-title":"Lang. Resour. Eval."},{"key":"28_CR37","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":"28_CR38","doi-asserted-by":"publisher","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. Association for Computational Linguistics, Online (Aug 2021). https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.393","DOI":"10.18653\/v1\/2021.acl-long.393"},{"key":"28_CR39","doi-asserted-by":"publisher","unstructured":"Zellers, R., Bisk, Y., Schwartz, R., Choi, Y.: SWAG: a large-scale adversarial dataset for grounded commonsense inference. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. pp. 93\u2013104. Association for Computational Linguistics, Brussels, Belgium (2018). https:\/\/doi.org\/10.18653\/v1\/D18-1009","DOI":"10.18653\/v1\/D18-1009"},{"key":"28_CR40","unstructured":"Zhang, X., Zhao, J., LeCun, Y.: Character-level convolutional networks for text classification. In: Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1. p. 649\u2013657. NIPS\u201915, MIT Press, Cambridge, MA, USA (2015)"},{"key":"28_CR41","doi-asserted-by":"publisher","unstructured":"Zhelezniak, V., Savkov, A., Shen, A., Hammerla, N.: Correlation coefficients and semantic textual similarity. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). pp. 951\u2013962. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https:\/\/doi.org\/10.18653\/v1\/N19-1100","DOI":"10.18653\/v1\/N19-1100"},{"key":"28_CR42","doi-asserted-by":"publisher","unstructured":"Zhu, X., Li, T., de\u00a0Melo, G.: Exploring semantic properties of sentence embeddings. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). pp. 632\u2013637. Association for Computational Linguistics, Melbourne, Australia (2018). https:\/\/doi.org\/10.18653\/v1\/P18-2100","DOI":"10.18653\/v1\/P18-2100"},{"key":"28_CR43","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Kiros, R., Zemel, R., Salakhutdinov, R., Urtasun, R., Torralba, A., Fidler, S.: Aligning books and movies: towards story-like visual explanations by watching movies and reading books. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2015)","DOI":"10.1109\/ICCV.2015.11"}],"container-title":["Lecture Notes in Computer Science","Chinese Computational Linguistics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-6207-5_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T08:51:38Z","timestamp":1695113498000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-6207-5_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819962068","9789819962075"],"references-count":43,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-6207-5_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"20 September 2023","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":"Harbin","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cncl2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}