{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T18:44:27Z","timestamp":1742928267878,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":35,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819755684"},{"type":"electronic","value":"9789819755691"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-981-97-5569-1_3","type":"book-chapter","created":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T03:26:37Z","timestamp":1733973997000},"page":"36-51","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Investigation of Simple-but-Effective Architecture for Long-form Text Matching with Transformers"],"prefix":"10.1007","author":[{"given":"Chen","family":"Shen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,13]]},"reference":[{"key":"3_CR1","unstructured":"Beltagy, I., Peters, M.E., Cohan, A.: Longformer: The long-document transformer. CoRR abs\/2004.05150 (2020)"},{"key":"3_CR2","unstructured":"Brown, T.B., Mann, B., Ryder, N., et\u00a0al.: Language models are few-shot learners. In: NeurIPS (2020)"},{"key":"3_CR3","doi-asserted-by":"crossref","unstructured":"Caciularu, A., Cohan, A., Beltagy, I., Peters, M.E., Cattan, A., Dagan, I.: CDLM: cross-document language modeling. In: Findings of the Association for Computational Linguistics: EMNLP 2021, Virtual Event \/ Punta Cana, Dominican Republic, 16-20 November, 2021. pp. 2648\u20132662 (2021)","DOI":"10.18653\/v1\/2021.findings-emnlp.225"},{"key":"3_CR4","unstructured":"Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL-HLT. pp. 4171\u20134186 (2019)"},{"key":"3_CR5","unstructured":"Hu, B., Lu, Z., Li, H., Chen, Q.: Convolutional neural network architectures for matching natural language sentences. In: NeurIPS. pp. 2042\u20132050 (2014)"},{"key":"3_CR6","doi-asserted-by":"crossref","unstructured":"Huang, P., He, X., Gao, J., Deng, L., Acero, A., Heck, L.P.: Learning deep structured semantic models for web search using clickthrough data. In: CIKM. pp. 2333\u20132338 (2013)","DOI":"10.1145\/2505515.2505665"},{"key":"3_CR7","doi-asserted-by":"crossref","unstructured":"Jiang, J., Zhang, M., Li, C., Bendersky, M., Golbandi, N., Najork, M.: Semantic text matching for long-form documents. In: The World Wide Web Conference, WWW 2019, San Francisco, CA, USA, May 13-17, 2019. pp. 795\u2013806 (2019)","DOI":"10.1145\/3308558.3313707"},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"Li, C., Fisher, E., Thomas, R., Pittard, S., Hertzberg, V., Choi, J.D.: Competence-level prediction and resume & job description matching using context-aware transformer models. In: EMNLP. pp. 8456\u20138466 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.679"},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"Liu, B., Niu, D., Wei, H., Lin, J., He, Y., Lai, K., Xu, Y.: Matching article pairs with graphical decomposition and convolutions. In: ACL. pp. 6284\u20136294 (2019)","DOI":"10.18653\/v1\/P19-1632"},{"key":"3_CR10","unstructured":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized BERT pretraining approach. CoRR abs\/1907.11692 (2019)"},{"key":"3_CR11","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019 (2019)"},{"key":"3_CR12","doi-asserted-by":"crossref","unstructured":"Lu, J., Lin, C., Wang, J., Li, C.: Synergy of database techniques and machine learning models for string similarity search and join. In: CIKM. pp. 2975\u20132976. ACM (2019)","DOI":"10.1145\/3357384.3360319"},{"key":"3_CR13","doi-asserted-by":"crossref","unstructured":"Miao, Z., Wang, J.: Watchog: A light-weight contrastive learning based framework for column annotation. Proc. ACM Manag. Data 1(4), 272:1\u2013272:24 (2023)","DOI":"10.1145\/3626766"},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Pang, B., Nijkamp, E., Kryscinski, W., Savarese, S., Zhou, Y., Xiong, C.: Long document summarization with top-down and bottom-up inference. In: EACL. pp. 1237\u20131254 (2023)","DOI":"10.18653\/v1\/2023.findings-eacl.94"},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"Pang, L., Lan, Y., Cheng, X.: Match-ignition: Plugging pagerank into transformer for long-form text matching. In: CIKM. pp. 1396\u20131405 (2021)","DOI":"10.1145\/3459637.3482450"},{"key":"3_CR16","doi-asserted-by":"crossref","unstructured":"Pappagari, R., Zelasko, P., Villalba, J., Carmiel, Y., Dehak, N.: Hierarchical transformers for long document classification. In: IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2019, Singapore, December 14-18, 2019. pp. 838\u2013844. IEEE (2019)","DOI":"10.1109\/ASRU46091.2019.9003958"},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"Park, H.H., Vyas, Y., Shah, K.: Efficient classification of long documents using transformers. In: ACL. pp. 702\u2013709 (2022)","DOI":"10.18653\/v1\/2022.acl-short.79"},{"key":"3_CR18","unstructured":"Rae, J.W., Potapenko, A., Jayakumar, S.M., Hillier, C., Lillicrap, T.P.: Compressive transformers for long-range sequence modelling. In: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020 (2020)"},{"key":"3_CR19","unstructured":"Sanh, V., Debut, L., Chaumond, J., Wolf, T.: Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs\/1910.01108 (2019)"},{"key":"3_CR20","unstructured":"Tay, Y., Dehghani, M., Abnar, S., Shen, Y., Bahri, D., Pham, P., Rao, J., Yang, L., Ruder, S., Metzler, D.: Long range arena : A benchmark for efficient transformers. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021 (2021)"},{"key":"3_CR21","doi-asserted-by":"crossref","unstructured":"Tian, B., Zhang, Y., Wang, J., Xing, C.: Hierarchical inter-attention network for document classification with multi-task learning. In: IJCAI. pp. 3569\u20133575 (2019)","DOI":"10.24963\/ijcai.2019\/495"},{"key":"3_CR22","doi-asserted-by":"crossref","unstructured":"Wang, J., Li, Y.: Minun: evaluating counterfactual explanations for entity matching. In: DEEM \u201922: Proceedings of the Sixth Workshop on Data Management for End-To-End Machine Learning. pp. 7:1\u20137:11 (2022)","DOI":"10.1145\/3533028.3533304"},{"key":"3_CR23","doi-asserted-by":"crossref","unstructured":"Wang, J., Li, Y., Hirota, W.: Machamp: A generalized entity matching benchmark. In: CIKM. pp. 4633\u20134642 (2021)","DOI":"10.1145\/3459637.3482008"},{"key":"3_CR24","doi-asserted-by":"crossref","unstructured":"Wang, J., Li, Y., Hirota, W., Kandogan, E.: Machop: an end-to-end generalized entity matching framework. In: aiDM \u201922: Proceedings of the Fifth International Workshop on Exploiting Artificial Intelligence Techniques for Data Management. pp. 2:1\u20132:10 (2022)","DOI":"10.1145\/3533702.3534910"},{"key":"3_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2020.04.025","volume":"530","author":"J Wang","year":"2020","unstructured":"Wang, J., Lin, C., Li, M., Zaniolo, C.: Boosting approximate dictionary-based entity extraction with synonyms. Inf. Sci. 530, 1\u201321 (2020)","journal-title":"Inf. Sci."},{"key":"3_CR26","unstructured":"Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E.H., Le, Q.V., Zhou, D.: Chain-of-thought prompting elicits reasoning in large language models. In: NeurIPS (2022)"},{"key":"3_CR27","doi-asserted-by":"crossref","unstructured":"Wolf, T., Debut, L., Sanh, V., et\u00a0al.: Transformers: State-of-the-art natural language processing. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, EMNLP 2020 - Demos, Online, November 16-20, 2020. pp. 38\u201345 (2020)","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"3_CR28","doi-asserted-by":"crossref","unstructured":"Wu, C., Wu, F., Qi, T., Huang, Y.: Hi-transformer: Hierarchical interactive transformer for efficient and effective long document modeling. In: ACL\/IJCNLP. pp. 848\u2013853 (2021)","DOI":"10.18653\/v1\/2021.acl-short.107"},{"key":"3_CR29","doi-asserted-by":"crossref","unstructured":"Wu, J., Zhang, Y., Wang, J., Lin, C., Fu, Y., Xing, C.: Scalable metric similarity join using mapreduce. In: ICDE. pp. 1662\u20131665. IEEE (2019)","DOI":"10.1109\/ICDE.2019.00167"},{"key":"3_CR30","doi-asserted-by":"crossref","unstructured":"Yang, L., Zhang, M., Li, C., Bendersky, M., Najork, M.: Beyond 512 tokens: Siamese multi-depth transformer-based hierarchical encoder for long-form document matching. In: CIKM. pp. 1725\u20131734 (2020)","DOI":"10.1145\/3340531.3411908"},{"key":"3_CR31","doi-asserted-by":"crossref","unstructured":"Yang, Z., Yang, D., Dyer, C., He, X., Smola, A.J., Hovy, E.H.: Hierarchical attention networks for document classification. In: NAACL HLT. pp. 1480\u20131489 (2016)","DOI":"10.18653\/v1\/N16-1174"},{"key":"3_CR32","unstructured":"Zaheer, M., Guruganesh, G., Dubey, K.A., Ainslie, J., Alberti, C., Onta\u00f1\u00f3n, S., Pham, P., Ravula, A., Wang, Q., Yang, L., Ahmed, A.: Big bird: Transformers for longer sequences. In: NeurIPS (2020)"},{"key":"3_CR33","doi-asserted-by":"crossref","unstructured":"Zhang, H., Zhang, J.: Text graph transformer for document classification. In: EMNLP. pp. 8322\u20138327 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.668"},{"key":"3_CR34","doi-asserted-by":"crossref","unstructured":"Zhou, X., Pappas, N., Smith, N.A.: Multilevel text alignment with cross-document attention. In: EMNLP. pp. 5012\u20135025 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.407"},{"key":"3_CR35","unstructured":"Zhu, C., Ping, W., Xiao, C., Shoeybi, M., Goldstein, T., Anandkumar, A., Catanzaro, B.: Long-short transformer: Efficient transformers for language and vision. In: Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual. pp. 17723\u201317736 (2021)"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-5569-1_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T04:41:39Z","timestamp":1733978499000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-5569-1_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819755684","9789819755691"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-5569-1_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"13 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Gifu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2024a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.dasfaa2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}