{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:18:40Z","timestamp":1776442720471,"version":"3.51.2"},"reference-count":74,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2023,2,7]],"date-time":"2023-02-07T00:00:00Z","timestamp":1675728000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"MIAI@Grenoble Alpes","award":["ANR-19-P3IA-0003"],"award-info":[{"award-number":["ANR-19-P3IA-0003"]}]},{"DOI":"10.13039\/501100004543","name":"Chinese Scholarship Council","doi-asserted-by":"crossref","award":["201906960018"],"award-info":[{"award-number":["201906960018"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2023,7,31]]},"abstract":"<jats:p>On a wide range of natural language processing and information retrieval tasks, transformer-based models, particularly pre-trained language models like BERT, have demonstrated tremendous effectiveness. Due to the quadratic complexity of the self-attention mechanism, however, such models have difficulties processing long documents. Recent works dealing with this issue include truncating long documents, in which case one loses potential relevant information, segmenting them into several passages, which may lead to miss some information and high computational complexity when the number of passages is large, or modifying the self-attention mechanism to make it sparser as in sparse-attention models, at the risk again of missing some information. We follow here a slightly different approach in which one first selects key blocks of a long document by local query-block pre-ranking, and then few blocks are aggregated to form a short document that can be processed by a model such as BERT. Experiments conducted on standard Information Retrieval datasets demonstrate the effectiveness of the proposed approach.<\/jats:p>","DOI":"10.1145\/3568394","type":"journal-article","created":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T13:06:34Z","timestamp":1666011994000},"page":"1-35","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["The Power of Selecting Key Blocks with Local Pre-ranking for Long Document Information Retrieval"],"prefix":"10.1145","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1041-8887","authenticated-orcid":false,"given":"Minghan","family":"Li","sequence":"first","affiliation":[{"name":"Universit\u00e9 Grenoble Alpes, Grenoble, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7039-3792","authenticated-orcid":false,"given":"Diana Nicoleta","family":"Popa","sequence":"additional","affiliation":[{"name":"Telepathy Labs, Zurich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7718-3296","authenticated-orcid":false,"given":"Johan","family":"Chagnon","sequence":"additional","affiliation":[{"name":"University of Wollongong, Wollongong, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1884-7993","authenticated-orcid":false,"given":"Yagmur Gizem","family":"Cinar","sequence":"additional","affiliation":[{"name":"Amazon, Edinburgh, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8858-3233","authenticated-orcid":false,"given":"Eric","family":"Gaussier","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Grenoble Alpes, Grenoble, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,2,7]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"268","DOI":"10.18653\/v1\/2020.emnlp-main.19","volume-title":"Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)","author":"Ainslie Joshua","year":"2020","unstructured":"Joshua Ainslie, Santiago Ontanon, Chris Alberti, Vaclav Cvicek, Zachary Fisher, Philip Pham, Anirudh Ravula, Sumit Sanghai, Qifan Wang, and Li Yang. 2020. ETC: Encoding long and structured inputs in transformers. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP). 268\u2013284."},{"key":"e_1_3_2_3_2","article-title":"Layer normalization","author":"Ba Jimmy Lei","year":"2016","unstructured":"Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. 2016. Layer normalization. arXiv preprint arXiv:1607.06450 (2016).","journal-title":"arXiv preprint arXiv:1607.06450"},{"key":"e_1_3_2_4_2","unstructured":"Iz Beltagy Matthew E. Peters and Arman Cohan. 2020. Longformer: The Long-Document Transformer. arXiv: 2004.05150."},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/1183614.1183644"},{"key":"e_1_3_2_6_2","first-page":"579","volume-title":"Proceedings of the 13th USENIX Conference on Operating Systems Design and Implementation","author":"Chen Tianqi","year":"2018","unstructured":"Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Meghan Cowan, Haichen Shen, Leyuan Wang, Yuwei Hu, Luis Ceze et\u00a0al. 2018. TVM: An automated end-to-end optimizing compiler for deep learning. In Proceedings of the 13th USENIX Conference on Operating Systems Design and Implementation. 579\u2013594."},{"key":"e_1_3_2_7_2","article-title":"Generating long sequences with sparse transformers","volume":"1904","author":"Child Rewon","year":"2019","unstructured":"Rewon Child, Scott Gray, Alec Radford, and Ilya Sutskever. 2019. Generating long sequences with sparse transformers. CoRR abs\/1904.10509 (2019).","journal-title":"CoRR"},{"key":"e_1_3_2_8_2","article-title":"Overview of the TREC 2019 Deep Learning Track","author":"Craswell Nick","year":"2020","unstructured":"Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, and Ellen M. Voorhees. 2020. Overview of the TREC 2019 Deep Learning Track. arXiv preprint arXiv:2003.07820 (2020).","journal-title":"arXiv preprint arXiv:2003.07820"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331303"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1285"},{"key":"e_1_3_2_11_2","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","volume":"1810","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. BERT: Pre-training of deep bidirectional transformers for language understanding. CoRR abs\/1810.04805 (2018).","journal-title":"CoRR"},{"key":"e_1_3_2_12_2","unstructured":"Ming Ding Chang Zhou Hongxia Yang and Jie Tang. 2020. CogLTX: Applying BERT to long texts. In Proceeding of the Advances in Neural Information Processing Systems . Vol. 33. Curran Associates Inc. 12792\u201312804."},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3209980"},{"key":"e_1_3_2_14_2","first-page":"139","volume-title":"Proceedings of the Meeting of the Association for Computational Linguistics: Human Language Technology","author":"Fang Hui","year":"2008","unstructured":"Hui Fang. 2008. A re-examination of query expansion using lexical resources. In Proceedings of the Meeting of the Association for Computational Linguistics: Human Language Technology. 139\u2013147."},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/1148170.1148193"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/7287.001.0001"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/3368089.3417050"},{"key":"e_1_3_2_18_2","first-page":"1792","volume-title":"Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics","author":"Grail Quentin","year":"2021","unstructured":"Quentin Grail, Julien Perez, and Eric Gaussier. 2021. Globalizing BERT-based transformer architectures for long document summarization. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, 1792\u20131810."},{"key":"e_1_3_2_19_2","article-title":"GPU kernels for block-sparse weights","volume":"3","author":"Gray Scott","year":"2017","unstructured":"Scott Gray, Alec Radford, and Diederik P. Kingma. 2017. GPU kernels for block-sparse weights. arXiv preprint arXiv:1711.09224 3 (2017).","journal-title":"arXiv preprint arXiv:1711.09224"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/2983323.2983769"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2019.102067"},{"key":"e_1_3_2_22_2","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1145\/3404835.3462889","volume-title":"Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Hofst\u00e4tter Sebastian","year":"2021","unstructured":"Sebastian Hofst\u00e4tter, Bhaskar Mitra, Hamed Zamani, Nick Craswell, and Allan Hanbury. 2021. Intra-document cascading: Learning to select passages for neural document ranking. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1349\u20131358."},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401224"},{"key":"e_1_3_2_24_2","first-page":"513","volume-title":"Proceedings of the 24th European Conference on Artificial Intelligence","volume":"325","author":"Hofst\u00e4tter Sebastian","year":"2020","unstructured":"Sebastian Hofst\u00e4tter, Markus Zlabinger, and Allan Hanbury. 2020. Interpretable & time-budget-constrained contextualization for re-ranking. In Proceedings of the 24th European Conference on Artificial Intelligence, Vol. 325. IOS Press, 513\u2013520."},{"key":"e_1_3_2_25_2","unstructured":"Baotian Hu Zhengdong Lu Hang Li and Qingcai Chen. 2014. Convolutional neural network architectures for matching natural language sentences. In Proceeding of the Advances in Neural Information Processing Systems Vol. 27. Curran Associates Inc. 2042\u20132050."},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/2505515.2505665"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1110"},{"key":"e_1_3_2_28_2","first-page":"279","volume-title":"Proceedings of the 11th ACM International Conference on Web Search and Data Mining","author":"Hui Kai","year":"2018","unstructured":"Kai Hui, Andrew Yates, Klaus Berberich, and Gerard De Melo. 2018. Co-PACRR: A context-aware neural IR model for ad-hoc retrieval. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining. 279\u2013287."},{"key":"e_1_3_2_29_2","first-page":"4594","volume-title":"Proceedings of the Conference on Empirical Methods in Natural Language Processing","author":"Jiang Jyun-Yu","year":"2020","unstructured":"Jyun-Yu Jiang, Chenyan Xiong, Chia-Jung Lee, and Wei Wang. 2020. Long document ranking with query-directed sparse transformer. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. 4594\u20134605."},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-45442-5_4"},{"key":"e_1_3_2_31_2","unstructured":"Nikita Kitaev \u0141ukasz Kaiser and Anselm Levskaya. 2020. Reformer: The Efficient Transformer. arxiv:2001.04451."},{"key":"e_1_3_2_32_2","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky Alex","year":"2012","unstructured":"Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. ImageNet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25 (2012), 1097\u20131105.","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0004-3702(99)00101-0"},{"key":"e_1_3_2_34_2","first-page":"4482","volume-title":"Proceedings of the Conference on Empirical Methods in Natural Language Processing","author":"Li Canjia","year":"2018","unstructured":"Canjia Li, Yingfei Sun, Ben He, Le Wang, Kai Hui, Andrew Yates, Le Sun, and Jungang Xu. 2018. NPRF: A neural pseudo relevance feedback framework for ad-hoc information retrieval. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. 4482\u20134491."},{"key":"e_1_3_2_35_2","article-title":"PARADE: Passage representation aggregation for document reranking","author":"Li Canjia","year":"2020","unstructured":"Canjia Li, Andrew Yates, Sean MacAvaney, Ben He, and Yingfei Sun. 2020. PARADE: Passage representation aggregation for document reranking. arXiv preprint arXiv:2008.09093 (2020).","journal-title":"arXiv preprint arXiv:2008.09093"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331250"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.2200\/S00348ED1V01Y201104HLT012"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3463083"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531856"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-14267-3"},{"key":"e_1_3_2_41_2","article-title":"Roberta: A robustly optimized bert pretraining approach","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 arXiv:1907.11692 (2019).","journal-title":"arXiv preprint arXiv:1907.11692"},{"key":"e_1_3_2_42_2","article-title":"Neural passage retrieval with improved negative contrast","author":"Lu Jing","year":"2020","unstructured":"Jing Lu, Gustavo Hern\u00e1ndez \u00c1brego, Ji Ma, Jianmo Ni, and Yinfei Yang. 2020. Neural passage retrieval with improved negative contrast. arXiv preprint arXiv:2010.12523 (2020).","journal-title":"arXiv preprint arXiv:2010.12523"},{"key":"e_1_3_2_43_2","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.1145\/3331184.3331317","volume-title":"Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"MacAvaney Sean","year":"2019","unstructured":"Sean MacAvaney, Andrew Yates, Arman Cohan, and Nazli Goharian. 2019. CEDR: Contextualized embeddings for document ranking. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 1101\u20131104."},{"key":"e_1_3_2_44_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Micikevicius Paulius","year":"2018","unstructured":"Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory Diamos, Erich Elsen, David Garcia, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, et\u00a0al. 2018. Mixed precision training. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_45_2","volume-title":"Advances in Neural Information Processing Systems","author":"Mikolov Tomas","year":"2013","unstructured":"Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems, Vol. 26. Curran Associates, Inc. Retrieved from https:\/\/proceedings.neurips.cc\/paper\/2013\/file\/9aa42b31882ec039965f3c4923ce901b-Paper.pdf."},{"issue":"2","key":"e_1_3_2_46_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3428687","article-title":"Weighting passages enhances accuracy","volume":"39","author":"Muntean Cristina Ioana","year":"2020","unstructured":"Cristina Ioana Muntean, Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto, and Ophir Frieder. 2020. Weighting passages enhances accuracy. ACM Trans. Inf. Syst. 39, 2 (2020), 1\u201311.","journal-title":"ACM Trans. Inf. Syst."},{"key":"e_1_3_2_47_2","volume-title":"Proceedings of the CoCo@NIPS","author":"Nguyen Tri","year":"2016","unstructured":"Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. 2016. MS MARCO: A human generated machine reading comprehension dataset. In Proceedings of the CoCo@NIPS."},{"key":"e_1_3_2_48_2","article-title":"Passage re-ranking with BERT","author":"Nogueira Rodrigo","year":"2019","unstructured":"Rodrigo Nogueira and Kyunghyun Cho. 2019. Passage re-ranking with BERT. arXiv preprint arXiv:1901.04085 (2019).","journal-title":"arXiv preprint arXiv:1901.04085"},{"issue":"3","key":"e_1_3_2_49_2","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1023\/A:1026080230789","article-title":"From retrieval status values to probabilities of relevance for advanced IR applications","volume":"6","author":"Nottelmann Henrik","year":"2003","unstructured":"Henrik Nottelmann and Norbert Fuhr. 2003. From retrieval status values to probabilities of relevance for advanced IR applications. Inf. Retr. 6, 3 (2003), 363\u2013388.","journal-title":"Inf. Retr."},{"issue":"2","key":"e_1_3_2_50_2","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/s10791-017-9321-y","article-title":"Neural information retrieval: At the end of the early years","volume":"21","author":"Onal Kezban Dilek","year":"2018","unstructured":"Kezban Dilek Onal, Ye Zhang, Ismail Sengor Altingovde, Md Mustafizur Rahman, Pinar Karagoz, Alex Braylan, Brandon Dang, Heng-Lu Chang, Henna Kim, Quinten McNamara, et\u00a0al. 2018. Neural information retrieval: At the end of the early years. Inf. Retr. J. 21, 2 (2018), 111\u2013182.","journal-title":"Inf. Retr. J."},{"key":"e_1_3_2_51_2","article-title":"Representation learning with contrastive predictive coding","author":"Oord Aaron van den","year":"2018","unstructured":"Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018).","journal-title":"arXiv preprint arXiv:1807.03748"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2016.2520371"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10341"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132914"},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.5555\/1953048.2078195"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1162"},{"key":"e_1_3_2_57_2","article-title":"Introducing LETOR 4.0 datasets","author":"Qin Tao","year":"2013","unstructured":"Tao Qin and Tie-Yan Liu. 2013. Introducing LETOR 4.0 datasets. arXiv preprint arXiv:1306.2597 (2013).","journal-title":"arXiv preprint arXiv:1306.2597"},{"key":"e_1_3_2_58_2","first-page":"3982","volume-title":"Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)","author":"Reimers Nils","year":"2019","unstructured":"Nils Reimers and Iryna Gurevych. 2019. Sentence-BERT: Sentence embeddings using Siamese BERT-networks. In Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 3982\u20133992."},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.5555\/1823431"},{"key":"e_1_3_2_60_2","volume-title":"Proceedings of the 17th Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval","author":"Robertson Stephen E.","year":"1994","unstructured":"Stephen E. Robertson and Steve Walker. 1994. Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval. In Proceedings of the 17th Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval."},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.1561\/1500000019"},{"key":"e_1_3_2_62_2","doi-asserted-by":"publisher","DOI":"10.1145\/2661829.2661935"},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W18-5501"},{"key":"e_1_3_2_64_2","first-page":"491","volume-title":"Proceedings of the European Conference on Machine Learning","author":"Turney Peter D.","year":"2001","unstructured":"Peter D. Turney. 2001. Mining the web for synonyms: PMI-IR versus LSA on TOEFL. In Proceedings of the European Conference on Machine Learning. Springer, 491\u2013502."},{"key":"e_1_3_2_65_2","first-page":"873","volume-title":"Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval","author":"Gysel Christophe Van","year":"2018","unstructured":"Christophe Van Gysel and Maarten de Rijke. 2018. Pytrec_eval: An extremely fast Python interface to trec_eval. In Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 873\u2013876."},{"key":"e_1_3_2_66_2","doi-asserted-by":"publisher","DOI":"10.5555\/3295222.3295349"},{"key":"e_1_3_2_67_2","first-page":"2922","volume-title":"Proceedings of the 25th International Joint Conference on Artificial Intelligence","author":"Wan Shengxian","year":"2016","unstructured":"Shengxian Wan, Yanyan Lan, Jun Xu, Jiafeng Guo, Liang Pang, and Xueqi Cheng. 2016. Match-SRNN: Modeling the recursive matching structure with spatial RNN. In Proceedings of the 25th International Joint Conference on Artificial Intelligence. 2922\u20132928."},{"key":"e_1_3_2_68_2","article-title":"Hugging Face\u2019s transformers: State-of-the-art natural language processing","author":"Wolf Thomas","year":"2019","unstructured":"Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, R\u00e9mi Louf, Morgan Funtowicz, et\u00a0al. 2019. Hugging Face\u2019s transformers: State-of-the-art natural language processing. arXiv preprint arXiv:1910.03771 (2019).","journal-title":"arXiv preprint arXiv:1910.03771"},{"key":"e_1_3_2_69_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2006.10.009"},{"key":"e_1_3_2_70_2","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080809"},{"key":"e_1_3_2_71_2","doi-asserted-by":"publisher","DOI":"10.1145\/3239571"},{"key":"e_1_3_2_72_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1259"},{"key":"e_1_3_2_73_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015676"},{"key":"e_1_3_2_74_2","unstructured":"Manzil Zaheer Guru Guruganesh Avinava Dubey Joshua Ainslie Chris Alberti Santiago Ontanon Philip Pham Anirudh Ravula Qifan Wang Li Yang and Amr Ahmed. 2021. Big Bird: Transformers for Longer Sequences. arxiv:2007.14062."},{"key":"e_1_3_2_75_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Zhao Chen","year":"2020","unstructured":"Chen Zhao, Chenyan Xiong, Corby Rosset, Xia Song, Paul Bennett, and Saurabh Tiwary. 2020. Transformer-XH: Multi-evidence reasoning with extra hop attention. In Proceedings of the International Conference on Learning Representations."}],"container-title":["ACM Transactions on Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3568394","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3568394","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:51:33Z","timestamp":1750182693000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3568394"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,7]]},"references-count":74,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,7,31]]}},"alternative-id":["10.1145\/3568394"],"URL":"https:\/\/doi.org\/10.1145\/3568394","relation":{},"ISSN":["1046-8188","1558-2868"],"issn-type":[{"value":"1046-8188","type":"print"},{"value":"1558-2868","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,7]]},"assertion":[{"value":"2021-12-03","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-09-29","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-02-07","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}