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Web"],"published-print":{"date-parts":[[2023,2,28]]},"abstract":"<jats:p>\n            Pseudo-relevance feedback mechanisms, from Rocchio to the relevance models, have shown the usefulness of expanding and reweighting the users\u2019 initial queries using information occurring in an initial set of retrieved documents, known as the pseudo-relevant set. Recently, dense retrieval \u2013 through the use of neural contextual language models such as BERT for analysing the documents\u2019 and queries\u2019 contents and computing their relevance scores \u2013 has shown a promising performance on several information retrieval tasks still relying on the traditional inverted index for identifying documents relevant to a query. Two different dense retrieval families have emerged: the use of single embedded representations for each passage and query, e.g., using BERT\u2019s [CLS] token, or via multiple representations, e.g., using an embedding for each token of the query and document (exemplified by ColBERT). In this work, we conduct the first study into the potential for multiple representation dense retrieval to be enhanced using pseudo-relevance feedback and present our proposed approach ColBERT-PRF. In particular, based on the pseudo-relevant set of documents identified using a first-pass dense retrieval, ColBERT-PRF extracts the representative feedback embeddings from the document embeddings of the pseudo-relevant set. Among the representative feedback embeddings, the embeddings that most highly discriminate among documents are employed as the expansion embeddings, which are then added to the original query representation. We show that these additional expansion embeddings both enhance the effectiveness of a reranking of the initial query results as well as an additional dense retrieval operation. Indeed, experiments on the MSMARCO passage ranking dataset show that MAP can be improved by up to 26% on the TREC 2019 query set and 10% on the TREC 2020 query set by the application of our proposed\n            <jats:sans-serif>ColBERT-PRF<\/jats:sans-serif>\n            method on a ColBERT dense retrieval approach.We further validate the effectiveness of our proposed pseudo-relevance feedback technique for a dense retrieval model on MSMARCO document ranking and TREC Robust04 document ranking tasks. For instance,\n            <jats:sans-serif>ColBERT-PRF<\/jats:sans-serif>\n            exhibits up to 21% and 14% improvement in MAP over the ColBERT E2E model on the MSMARCO document ranking TREC 2019 and TREC 2020 query sets, respectively. Additionally, we study the effectiveness of variants of the\n            <jats:sans-serif>ColBERT-PRF<\/jats:sans-serif>\n            model with different weighting methods. Finally, we show that\n            <jats:sans-serif>ColBERT-PRF<\/jats:sans-serif>\n            can be made more efficient, attaining up to 4.54\u00d7 speedup over the default\n            <jats:sans-serif>ColBERT-PRF<\/jats:sans-serif>\n            model, and with little impact on effectiveness, through the application of approximate scoring and different clustering methods.\n          <\/jats:p>","DOI":"10.1145\/3572405","type":"journal-article","created":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T11:57:11Z","timestamp":1669118231000},"page":"1-39","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":32,"title":["ColBERT-PRF: Semantic Pseudo-Relevance Feedback for Dense Passage and Document Retrieval"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5151-2773","authenticated-orcid":false,"given":"Xiao","family":"Wang","sequence":"first","affiliation":[{"name":"University of Glasgow, Glasgow, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3143-279X","authenticated-orcid":false,"given":"Craig","family":"MacDonald","sequence":"additional","affiliation":[{"name":"University of Glasgow, Glasgow, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7427-1001","authenticated-orcid":false,"given":"Nicola","family":"Tonellotto","sequence":"additional","affiliation":[{"name":"University of Pisa, Pisa, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4701-3223","authenticated-orcid":false,"given":"Iadh","family":"Ounis","sequence":"additional","affiliation":[{"name":"University of Glasgow, Glasgow, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,1,16]]},"reference":[{"key":"e_1_3_3_2_2","volume-title":"Proceedings of TREC","author":"Abdul-Jaleel Nasreen","year":"2004","unstructured":"Nasreen Abdul-Jaleel, James Allan, W. Bruce Croft, Fernando Diaz, Leah Larkey, Xiaoyan Li, Mark D. Smucker, and Courtney Wade. 2004. UMass at TREC 2004: Novelty and HARD. In Proceedings of TREC."},{"key":"e_1_3_3_3_2","article-title":"Probability Models for Information Retrieval Based on Divergence from Randomness Ph.D. thesis","author":"Amati Giambattista","year":"2003","unstructured":"Giambattista Amati. 2003. Probability Models for Information Retrieval Based on Divergence from Randomness Ph.D. thesis. University of Glasgow (2003).","journal-title":"University of Glasgow"},{"key":"e_1_3_3_4_2","first-page":"127","volume-title":"Proceedings of ECIR","author":"Amati Giambattista","year":"2004","unstructured":"Giambattista Amati, Claudio Carpineto, and Giovanni Romano. 2004. Query difficulty, robustness, and selective application of query expansion. 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In Proceedings of SODA. 1027\u20131035."},{"key":"e_1_3_3_7_2","first-page":"243","volume-title":"Proceedings of SIGIR","author":"Cao Guihong","year":"2008","unstructured":"Guihong Cao, Jian-Yun Nie, Jianfeng Gao, and Stephen Robertson. 2008. Selecting good expansion terms for pseudo-relevance feedback. In Proceedings of SIGIR. 243\u2013250."},{"key":"e_1_3_3_8_2","volume-title":"Proceedings of TREC","author":"Craswell Nick","year":"2021","unstructured":"Nick Craswell, Bhaskar Mitra, Emine Yilmaz, and Daniel Campos. 2021. Overview of the TREC 2020 deep learning track. In Proceedings of TREC."},{"key":"e_1_3_3_9_2","volume-title":"Proceedings of TREC","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. In Proceedings of TREC."},{"key":"e_1_3_3_10_2","first-page":"985","volume-title":"Proceedings of SIGIR","author":"Dai Zhuyun","year":"2019","unstructured":"Zhuyun Dai and Jamie Callan. 2019. Deeper text understanding for IR with contextual neural language modeling. In Proceedings of SIGIR. 985\u2013988."},{"key":"e_1_3_3_11_2","first-page":"1897","volume-title":"Proceedings of WWW","author":"Dai Zhuyun","year":"2020","unstructured":"Zhuyun Dai and Jamie Callan. 2020. Context-aware document term weighting for ad-hoc search. In Proceedings of WWW. 1897\u20131907."},{"key":"e_1_3_3_12_2","first-page":"4171","volume-title":"Proceedings of ACL","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of ACL. 4171\u20134186."},{"key":"e_1_3_3_13_2","first-page":"367","volume-title":"Proceedings of ACL","author":"Diaz Fernando","year":"2016","unstructured":"Fernando Diaz, Bhaskar Mitra, and Nick Craswell. 2016. Query expansion with locally-trained word embeddings. In Proceedings of ACL. 367\u2013377."},{"key":"e_1_3_3_14_2","first-page":"257","volume-title":"Proceedings of ECIR","author":"Formal Thibault","year":"2021","unstructured":"Thibault Formal, Benjamin Piwowarski, and St\u00e9phane Clinchant. 2021. A white box analysis of ColBERT. In Proceedings of ECIR. 257\u2013263."},{"key":"e_1_3_3_15_2","first-page":"55","volume-title":"Proceedings of CIKM","author":"Guo Jiafeng","year":"2016","unstructured":"Jiafeng Guo, Yixing Fan, Qingyao Ai, and W. Bruce Croft. 2016. A deep relevance matching model for ad-hoc retrieval. In Proceedings of CIKM. 55\u201364."},{"key":"e_1_3_3_16_2","article-title":"Billion-scale similarity search with GPUs","author":"Johnson Jeff","year":"2017","unstructured":"Jeff Johnson, Matthijs Douze, and Herv\u00e9 J\u00e9gou. 2017. Billion-scale similarity search with GPUs. arXiv preprint arXiv:1702.08734 (2017).","journal-title":"arXiv preprint arXiv:1702.08734"},{"key":"e_1_3_3_17_2","first-page":"6769","volume-title":"Proceedings of EMNLP","author":"Karpukhin Vladimir","year":"2020","unstructured":"Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020. Dense passage retrieval for open-domain question answering. In Proceedings of EMNLP. 6769\u20136781."},{"key":"e_1_3_3_18_2","first-page":"39","volume-title":"Proceedings of SIGIR","author":"Khattab Omar","year":"2020","unstructured":"Omar Khattab and Matei Zaharia. 2020. ColBERT: Efficient and effective passage search via contextualized late interaction over BERT. In Proceedings of SIGIR. 39\u201348."},{"key":"e_1_3_3_19_2","first-page":"261","volume-title":"Proceedings of ICCI","author":"Khennak Ilyes","year":"2019","unstructured":"Ilyes Khennak, Habiba Drias, Amine Kechid, and Hadjer Moulai. 2019. Clustering algorithms for query expansion based information retrieval. In Proceedings of ICCI. 261\u2013272."},{"key":"e_1_3_3_20_2","first-page":"1929","volume-title":"Proceedings of CIKM","author":"Kuzi Saar","year":"2016","unstructured":"Saar Kuzi, Anna Shtok, and Oren Kurland. 2016. Query expansion using word embeddings. In Proceedings of CIKM. 1929\u20131932."},{"key":"e_1_3_3_21_2","first-page":"4482","volume-title":"Proceedings of EMNLP","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 EMNLP. 4482\u20134491."},{"key":"e_1_3_3_22_2","unstructured":"Canjia Li Andrew Yates Sean MacAvaney Ben He and Yingfei Sun. 2021. PARADE: Passage Representation Aggregation for Document Reranking. arXiv:2008.09093 [cs.IR]."},{"key":"e_1_3_3_23_2","volume-title":"Proceedings of ECIR","author":"Li Hang","year":"2021","unstructured":"Hang Li, Shengyao Zhuang, Ahmed Mourad, Xueguang Ma, Jimmy Lin, and Guido Zuccon. 2021. Improving query representations for dense retrieval with pseudo relevance feedback: A reproducibility study. In Proceedings of ECIR."},{"key":"e_1_3_3_24_2","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1162\/tacl_a_00369","article-title":"Sparse, dense, and attentional representations for text retrieval","volume":"9","author":"Luan Yi","year":"2021","unstructured":"Yi Luan, Jacob Eisenstein, Kristina Toutanova, and Michael Collins. 2021. Sparse, dense, and attentional representations for text retrieval. 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IIR 2021 Workshop (2021).","journal-title":"IIR 2021 Workshop"},{"key":"e_1_3_3_30_2","first-page":"467","article-title":"CEQE: Contextualized embeddings for query expansion","author":"Naseri Shahrzad","year":"2021","unstructured":"Shahrzad Naseri, Jeffrey Dalton, Andrew Yates, and James Allan. 2021. CEQE: Contextualized embeddings for query expansion. Proceedings of ECIR (2021), 467\u2013482.","journal-title":"Proceedings of ECIR"},{"key":"e_1_3_3_31_2","volume-title":"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 CoCo@NIPs."},{"key":"e_1_3_3_32_2","article-title":"Document ranking with a pretrained sequence-to-sequence model","author":"Nogueira Rodrigo","year":"2020","unstructured":"Rodrigo Nogueira, Zhiying Jiang, and Jimmy Lin. 2020. Document ranking with a pretrained sequence-to-sequence model. arXiv preprint arXiv:2003.06713 (2020).","journal-title":"arXiv preprint arXiv:2003.06713"},{"key":"e_1_3_3_33_2","article-title":"From doc2query to docTTTTTquery","author":"Nogueira Rodrigo","year":"2019","unstructured":"Rodrigo Nogueira, Jimmy Lin, and AI Epistemic. 2019. From doc2query to docTTTTTquery. Online preprint (2019).","journal-title":"Online preprint"},{"key":"e_1_3_3_34_2","article-title":"Document expansion by query prediction","author":"Nogueira Rodrigo","year":"2019","unstructured":"Rodrigo Nogueira, Wei Yang, Jimmy Lin, and Kyunghyun Cho. 2019. Document expansion by query prediction. arXiv preprint arXiv:1904.08375 (2019).","journal-title":"arXiv preprint arXiv:1904.08375"},{"key":"e_1_3_3_35_2","first-page":"517","volume-title":"Proceedings of ECIR","author":"Ounis Iadh","year":"2005","unstructured":"Iadh Ounis, Gianni Amati, Vassilis Plachouras, Ben He, Craig Macdonald, and Douglas Johnson. 2005. Terrier information retrieval platform. In Proceedings of ECIR. 517\u2013519."},{"key":"e_1_3_3_36_2","first-page":"297","volume-title":"Proceedings of ECIR","author":"Padaki Ramith","year":"2020","unstructured":"Ramith Padaki, Zhuyun Dai, and Jamie Callan. 2020. Rethinking query expansion for BERT reranking. In Proceedings of ECIR. 297\u2013304."},{"key":"e_1_3_3_37_2","first-page":"2227","volume-title":"Proceedings of NAACL-HLT","author":"Peters Matthew E.","year":"2018","unstructured":"Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word representations. In Proceedings of NAACL-HLT. 2227\u20132237."},{"key":"e_1_3_3_38_2","first-page":"313","article-title":"Relevance feedback in information retrieval","author":"Rocchio Joseph","year":"1971","unstructured":"Joseph Rocchio. 1971. Relevance feedback in information retrieval. The Smart Retrieval System-experiments in Automatic Document Processing (1971), 313\u2013323.","journal-title":"The Smart Retrieval System-experiments in Automatic Document Processing"},{"key":"e_1_3_3_39_2","first-page":"1253","volume-title":"Proceedings of SIGIR","author":"Roy Dwaipayan","year":"2019","unstructured":"Dwaipayan Roy, Sumit Bhatia, and Mandar Mitra. 2019. Selecting discriminative terms for relevance model. In Proceedings of SIGIR. 1253\u20131256."},{"key":"e_1_3_3_40_2","first-page":"1835","volume-title":"Proceedings of CIKM","author":"Roy Dwaipayan","year":"2018","unstructured":"Dwaipayan Roy, Debasis Ganguly, Sumit Bhatia, Srikanta Bedathur, and Mandar Mitra. 2018. Using word embeddings for information retrieval: How collection and term normalization choices affect performance. In Proceedings of CIKM. 1835\u20131838."},{"key":"e_1_3_3_41_2","volume-title":"Proceedings of SIGIR Workshop on Neural Information Retrieval","author":"Roy Dwaipayan","year":"2016","unstructured":"Dwaipayan Roy, Debjyoti Paul, Mandar Mitra, and Utpal Garain. 2016. Using word embeddings for automatic query expansion. In Proceedings of SIGIR Workshop on Neural Information Retrieval. arXiv:1606.07608."},{"key":"e_1_3_3_42_2","first-page":"3453","volume-title":"Proceedings of CIKM","author":"Tonellotto Nicola","year":"2021","unstructured":"Nicola Tonellotto and Craig Macdonald. 2021. Query embedding pruning for dense retrieval. In Proceedings of CIKM. 3453\u20133457."},{"issue":"6","key":"e_1_3_3_43_2","doi-asserted-by":"crossref","first-page":"102342","DOI":"10.1016\/j.ipm.2020.102342","article-title":"A pseudo-relevance feedback framework combining relevance matching and semantic matching for information retrieval","volume":"57","author":"Wang Junmei","year":"2020","unstructured":"Junmei Wang, Min Pan, Tingting He, Xiang Huang, Xueyan Wang, and Xinhui Tu. 2020. A pseudo-relevance feedback framework combining relevance matching and semantic matching for information retrieval. Information Processing & Management 57, 6 (2020), 102342.","journal-title":"Information Processing & Management"},{"issue":"5","key":"e_1_3_3_44_2","doi-asserted-by":"crossref","first-page":"103026","DOI":"10.1016\/j.ipm.2022.103026","article-title":"Improving zero-shot retrieval using dense external expansion","volume":"59","author":"Wang Xiao","year":"2022","unstructured":"Xiao Wang, Craig Macdonald, and Iadh Ounis. 2022. 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In Proceedings of SIGIR. 55\u201364."},{"key":"e_1_3_3_47_2","volume-title":"Proceedings of ICLR","author":"Xiong Lee","year":"2021","unstructured":"Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul Bennett, Junaid Ahmed, and Arnold Overwijk. 2021. Approximate nearest neighbor negative contrastive learning for dense text retrieval. In Proceedings of ICLR."},{"key":"e_1_3_3_48_2","first-page":"440","volume-title":"Proceedings of ECIR","author":"Yu HongChien","year":"2021","unstructured":"HongChien Yu, Zhuyun Dai, and Jamie Callan. 2021. PGT: Pseudo relevance feedback using a graph-based transformer. In Proceedings of ECIR. 440\u2013447."},{"key":"e_1_3_3_49_2","first-page":"3592","volume-title":"Proceedings of CIKM","author":"Yu HongChien","year":"2021","unstructured":"HongChien Yu, Chenyan Xiong, and Jamie Callan. 2021. Improving query representations for dense retrieval with pseudo relevance feedback. 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