{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T15:33:38Z","timestamp":1777736018657,"version":"3.51.4"},"reference-count":67,"publisher":"Springer Science and Business Media LLC","issue":"14","license":[{"start":{"date-parts":[[2022,3,22]],"date-time":"2022-03-22T00:00:00Z","timestamp":1647907200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,3,22]],"date-time":"2022-03-22T00:00:00Z","timestamp":1647907200000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1007\/s00521-022-07072-0","type":"journal-article","created":{"date-parts":[[2022,3,22]],"date-time":"2022-03-22T12:02:47Z","timestamp":1647950567000},"page":"11831-11851","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Mutually improved dense retriever and GNN-based reader for arbitrary-hop open-domain question answering"],"prefix":"10.1007","volume":"34","author":[{"given":"Ronghan","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4745-757X","authenticated-orcid":false,"given":"Lifang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zejun","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongtian","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meng","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyu","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,3,22]]},"reference":[{"key":"7072_CR1","unstructured":"Asai A, Hashimoto K, Hajishirzi H, Socher R, Xiong C (2020) Learning to retrieve reasoning paths over wikipedia graph for question answering. In: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net. https:\/\/openreview.net\/forum?id=SJgVHkrYDH"},{"key":"7072_CR2","unstructured":"Beltagy I, Peters ME, Cohan A (2020) Longformer: The long-document transformer. CoRR http:\/\/arxiv.org\/abs\/2004.05150"},{"key":"7072_CR3","doi-asserted-by":"publisher","unstructured":"Cao ND, Aziz W, Titov I (2019) Question answering by reasoning across documents with graph convolutional networks. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers), pp. 2306\u20132317. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/n19-1240","DOI":"10.18653\/v1\/n19-1240"},{"key":"7072_CR4","doi-asserted-by":"publisher","unstructured":"Chen D, Fisch A, Weston J, Bordes A (2017) Reading wikipedia to answer open-domain questions. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, Canada, July 30 - August 4, Volume 1: Long Papers, pp. 1870\u20131879. Association for Computational Linguistics (2017). https:\/\/doi.org\/10.18653\/v1\/P17-1171","DOI":"10.18653\/v1\/P17-1171"},{"key":"7072_CR5","unstructured":"Das R, Dhuliawala S, Zaheer M, McCallum A (2019) Multi-step retriever-reader interaction for scalable open-domain question answering. In: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019. OpenReview.net. https:\/\/openreview.net\/forum?id=HkfPSh05K7"},{"key":"7072_CR6","doi-asserted-by":"publisher","unstructured":"Devlin J, Chang M, Lee K, Toutanova K (2019) 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, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers), pp. 4171\u20134186. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/n19-1423","DOI":"10.18653\/v1\/n19-1423"},{"key":"7072_CR7","doi-asserted-by":"publisher","unstructured":"Dhingra B, Jin Q, Yang Z, Cohen WW, Salakhutdinov R (2018) Neural models for reasoning over multiple mentions using coreference. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT, New Orleans, Louisiana, USA, June 1-6, 2018, Volume 2 (Short Papers), pp. 42\u201348. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/n18-2007","DOI":"10.18653\/v1\/n18-2007"},{"key":"7072_CR8","doi-asserted-by":"publisher","unstructured":"Ding M, Zhou C, Chen Q, Yang H, Tang J (2019) Cognitive graph for multi-hop reading comprehension at scale. In: Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers, pp. 2694\u20132703. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/p19-1259","DOI":"10.18653\/v1\/p19-1259"},{"key":"7072_CR9","doi-asserted-by":"publisher","unstructured":"Fang Y, Sun S, Gan Z, Pillai R, Wang S, Liu J (2020) Hierarchical graph network for multi-hop question answering. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020, pp. 8823\u20138838. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.710","DOI":"10.18653\/v1\/2020.emnlp-main.710"},{"key":"7072_CR10","doi-asserted-by":"publisher","unstructured":"Feldman Y, El-Yaniv R (2019) Multi-hop paragraph retrieval for open-domain question answering. In: Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers, pp. 2296\u20132309. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/p19-1222","DOI":"10.18653\/v1\/p19-1222"},{"key":"7072_CR11","doi-asserted-by":"crossref","unstructured":"Gao L, Callan J (2021) Condenser: a pre-training architecture for dense retrieval. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event \/ Punta Cana, Dominican Republic, 7-11 November, 2021, pp. 981\u2013993. Association for Computational Linguistics. https:\/\/aclanthology.org\/2021.emnlp-main.75","DOI":"10.18653\/v1\/2021.emnlp-main.75"},{"key":"7072_CR12","unstructured":"Gao L, Callan J (2021) Unsupervised corpus aware language model pre-training for dense passage retrieval. CoRR abs\/2108.05540 (2021). http:\/\/arxiv.org\/abs\/2108.05540"},{"key":"7072_CR13","doi-asserted-by":"publisher","unstructured":"Glass MR, Gliozzo A, Chakravarti R, Ferritto A, Pan L, Bhargav GPS, Garg D, Sil A (2020) Span selection pre-training for question answering. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, pp. 2773\u20132782. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.247","DOI":"10.18653\/v1\/2020.acl-main.247"},{"key":"7072_CR14","unstructured":"Guu K, Lee K, Tung Z, Pasupat P, Chang M (2020) Retrieval augmented language model pre-training. In: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, Proceedings of Machine Learning Research, vol. 119, pp. 3929\u20133938. PMLR. http:\/\/proceedings.mlr.press\/v119\/guu20a.html"},{"key":"7072_CR15","doi-asserted-by":"publisher","unstructured":"Hu M, Peng Y, Huang Z, Qiu X, Wei F, Zhou M (2018) Reinforced mnemonic reader for machine reading comprehension. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13-19, 2018, Stockholm, Sweden, pp. 4099\u20134106. ijcai.org. https:\/\/doi.org\/10.24963\/ijcai.2018\/570","DOI":"10.24963\/ijcai.2018\/570"},{"key":"7072_CR16","unstructured":"Huang H, Zhu C, Shen Y, Chen W (2018) Fusionnet: Fusing via fully-aware attention with application to machine comprehension. In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net. https:\/\/openreview.net\/forum?id=BJIgi_eCZ"},{"key":"7072_CR17","unstructured":"Izacard G, Grave E (2021) Distilling knowledge from reader to retriever for question answering. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net. https:\/\/openreview.net\/forum?id=NTEz-6wysdb"},{"key":"7072_CR18","doi-asserted-by":"crossref","unstructured":"Izacard G, Grave E (2021) Leveraging passage retrieval with generative models for open domain question answering. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, EACL 2021, Online, April 19 - 23, 2021, pp. 874\u2013880. Association for Computational Linguistics. https:\/\/www.aclweb.org\/anthology\/2021.eacl-main.74\/","DOI":"10.18653\/v1\/2021.eacl-main.74"},{"key":"7072_CR19","doi-asserted-by":"publisher","unstructured":"Jiang Y, Bansal M (2019) Self-assembling modular networks for interpretable multi-hop reasoning. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3-7, 2019, pp. 4473\u20134483. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/D19-1455","DOI":"10.18653\/v1\/D19-1455"},{"key":"7072_CR20","doi-asserted-by":"publisher","unstructured":"Jiang Y, Joshi N, Chen Y, Bansal M (2019) Explore, propose, and assemble: An interpretable model for multi-hop reading comprehension. In: Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers, pp. 2714\u20132725. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/p19-1261","DOI":"10.18653\/v1\/p19-1261"},{"issue":"3","key":"7072_CR21","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1109\/TBDATA.2019.2921572","volume":"7","author":"J Johnson","year":"2021","unstructured":"Johnson J, Douze M, J\u00e9gou H (2021) Billion-scale similarity search with gpus. IEEE Trans Big Data 7(3):535\u2013547. https:\/\/doi.org\/10.1109\/TBDATA.2019.2921572","journal-title":"IEEE Trans Big Data"},{"key":"7072_CR22","doi-asserted-by":"publisher","unstructured":"Joshi M, Choi E, Weld DS, Zettlemoyer L (2017) Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, July 30 - August 4, Volume 1: Long Papers, pp. 1601\u20131611. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/P17-1147","DOI":"10.18653\/v1\/P17-1147"},{"key":"7072_CR23","doi-asserted-by":"publisher","unstructured":"Karpukhin V, Oguz B, Min S, Lewis PSH, Wu L, Edunov S, Chen D, Yih W (2020) Dense passage retrieval for open-domain question answering. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020, pp. 6769\u20136781. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.550","DOI":"10.18653\/v1\/2020.emnlp-main.550"},{"key":"7072_CR24","doi-asserted-by":"publisher","unstructured":"Khashabi D, Chaturvedi S, Roth M, Upadhyay S, Roth D (2018) Looking beyond the surface: A challenge set for reading comprehension over multiple sentences. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2018, New Orleans, Louisiana, USA, June 1-6, 2018, Volume 1 (Long Papers), pp. 252\u2013262. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/n18-1023","DOI":"10.18653\/v1\/n18-1023"},{"key":"7072_CR25","doi-asserted-by":"publisher","unstructured":"Khattab O, Zaharia M (2020) Colbert: Efficient and effective passage search via contextualized late interaction over BERT. In: Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, SIGIR 2020, Virtual Event, China, July 25-30, 2020, pp. 39\u201348. ACM. https:\/\/doi.org\/10.1145\/3397271.3401075","DOI":"10.1145\/3397271.3401075"},{"key":"7072_CR26","doi-asserted-by":"crossref","unstructured":"Kocisk\u00fd T, Schwarz J, Blunsom P, Dyer C, Hermann KM, Melis G, Grefenstette E (2018) The narrativeqa reading comprehension challenge. Trans. Assoc. Comput. Linguistics 6, 317\u2013328. https:\/\/transacl.org\/ojs\/index.php\/tacl\/article\/view\/1197","DOI":"10.1162\/tacl_a_00023"},{"key":"7072_CR27","doi-asserted-by":"crossref","unstructured":"Kwiatkowski T, Palomaki J, Redfield O, Collins M, Parikh AP, Alberti C, Epstein D, Polosukhin I, Devlin J, Lee K, Toutanova K, Jones L, Kelcey M, Chang M, Dai AM, Uszkoreit J, Le Q, Petrov S (2019) Natural questions: a benchmark for question answering research. Trans. Assoc. Comput. Linguistics 7, 452\u2013466. https:\/\/transacl.org\/ojs\/index.php\/tacl\/article\/view\/1455","DOI":"10.1162\/tacl_a_00276"},{"key":"7072_CR28","doi-asserted-by":"publisher","unstructured":"Lai G, Xie Q, Liu H, Yang Y, Hovy EH (2017) RACE: large-scale reading comprehension dataset from examinations. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, Copenhagen, Denmark, September 9-11, 2017, pp. 785\u2013794. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/d17-1082","DOI":"10.18653\/v1\/d17-1082"},{"key":"7072_CR29","unstructured":"Lan Z, Chen M, Goodman S, Gimpel K, Sharma P, Soricut R (2020) ALBERT: A lite BERT for self-supervised learning of language representations. In: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net. https:\/\/openreview.net\/forum?id=H1eA7AEtvS"},{"key":"7072_CR30","doi-asserted-by":"publisher","unstructured":"Lee K, Chang M, Toutanova K (2019) Latent retrieval for weakly supervised open domain question answering. In: Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers, pp. 6086\u20136096. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/p19-1612","DOI":"10.18653\/v1\/p19-1612"},{"key":"7072_CR31","unstructured":"Lewis PSH, Perez E, Piktus A, Petroni F, Karpukhin V, Goyal N, K\u00fcttler H, Lewis M, Yih W, Rockt\u00e4schel T, Riedel S, Kiela D (2020) Retrieval-augmented generation for knowledge-intensive NLP tasks. In: Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual. https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/6b493230205f780e1bc26945df7481e5-Abstract.html"},{"key":"7072_CR32","doi-asserted-by":"publisher","unstructured":"Li R, Wang L, Wang S, Jiang Z (2021) Asynchronous multi-grained graph network for interpretable multi-hop reading comprehension. In: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, Virtual Event \/ Montreal, Canada, 19-27 August 2021, pp. 3857\u20133863. ijcai.org. https:\/\/doi.org\/10.24963\/ijcai.2021\/531","DOI":"10.24963\/ijcai.2021\/531"},{"key":"7072_CR33","doi-asserted-by":"crossref","unstructured":"Li S, Li X, Shang L, Jiang X, Liu Q, Sun C, Ji Z, Liu B (2021) Hopretriever: Retrieve hops over wikipedia to answer complex questions. In: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021, pp. 13279\u201313287. AAAI Press. https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/17568","DOI":"10.1609\/aaai.v35i15.17568"},{"key":"7072_CR34","unstructured":"Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V (2019) Roberta: A robustly optimized BERT pretraining approach. CoRR abs\/1907.11692. http:\/\/arxiv.org\/abs\/1907.11692"},{"key":"7072_CR35","doi-asserted-by":"crossref","unstructured":"Luan Y, Eisenstein J, Toutanova K, Collins M (2021) Sparse, dense, and attentional representations for text retrieval. Trans. Assoc. Comput. Linguistics 9, 329\u2013345. https:\/\/transacl.org\/ojs\/index.php\/tacl\/article\/view\/2383","DOI":"10.1162\/tacl_a_00369"},{"key":"7072_CR36","doi-asserted-by":"publisher","unstructured":"Mao Y, He P, Liu X, Shen Y, Gao J, Han J, Chen W (2021) Generation-augmented retrieval for open-domain question answering. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL\/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1-6, 2021, pp. 4089\u20134100. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.316","DOI":"10.18653\/v1\/2021.acl-long.316"},{"key":"7072_CR37","doi-asserted-by":"publisher","unstructured":"Min S, Zhong V, Zettlemoyer L, Hajishirzi (2019) Multi-hop reading comprehension through question decomposition and rescoring. In: Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers, pp. 6097\u20136109. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/p19-1613","DOI":"10.18653\/v1\/p19-1613"},{"key":"7072_CR38","unstructured":"Nguyen T, Rosenberg M, Song X, Gao J, Tiwary S, Majumder R, Deng L (2016) MS MARCO: A human generated machine reading comprehension dataset. In: Proceedings of the Workshop on Cognitive Computation: Integrating neural and symbolic approaches 2016 co-located with the 30th Annual Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, Spain, December 9, 2016, CEUR Workshop Proceedings, vol. 1773. CEUR-WS.org. http:\/\/ceur-ws.org\/Vol-1773\/CoCoNIPS_2016_paper9.pdf"},{"key":"7072_CR39","doi-asserted-by":"publisher","unstructured":"Nie Y, Wang S, Bansal M (2019) Revealing the importance of semantic retrieval for machine reading at scale. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3-7, 2019, pp. 2553\u20132566. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/D19-1258","DOI":"10.18653\/v1\/D19-1258"},{"key":"7072_CR40","doi-asserted-by":"publisher","unstructured":"Nishida K, Nishida K, Nagata M, Otsuka A, Saito I, Asano H, Tomita J (2019) Answering while summarizing: Multi-task learning for multi-hop QA with evidence extraction. In: Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers, pp. 2335\u20132345. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/p19-1225","DOI":"10.18653\/v1\/p19-1225"},{"key":"7072_CR41","doi-asserted-by":"publisher","unstructured":"Perez E, Lewis PSH, Yih W, Cho K, Kiela D (2020) Unsupervised question decomposition for question answering. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020, pp. 8864\u20138880. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.713","DOI":"10.18653\/v1\/2020.emnlp-main.713"},{"key":"7072_CR42","doi-asserted-by":"crossref","unstructured":"Qi P, Lee H, Sido OT, Manning CD (2020) Retrieve, rerank, read, then iterate: Answering open-domain questions of arbitrary complexity from text. CoRR http:\/\/arxiv.org\/abs\/2010.12527","DOI":"10.18653\/v1\/2021.emnlp-main.292"},{"key":"7072_CR43","doi-asserted-by":"publisher","unstructured":"Qi P, Lin X, Mehr L, Wang Z, Manning CD (2019) Answering complex open-domain questions through iterative query generation. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3-7, 2019, pp. 2590\u20132602. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/D19-1261","DOI":"10.18653\/v1\/D19-1261"},{"key":"7072_CR44","doi-asserted-by":"publisher","unstructured":"Qiu L, Xiao Y, Qu Y, Zhou H, Li L, Zhang W, Yu Y (2019) Dynamically fused graph network for multi-hop reasoning. In: Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers, pp. 6140\u20136150. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/p19-1617","DOI":"10.18653\/v1\/p19-1617"},{"key":"7072_CR45","doi-asserted-by":"crossref","unstructured":"Qu Y, Ding Y, Liu J, Liu K, Ren R, Zhao WX, Dong D, Wu H, Wang H (2021) Rocketqa: An optimized training approach to dense passage retrieval for open-domain question answering. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, Online, June 6-11, 2021, pp. 5835\u20135847. Association for Computational Linguistics. https:\/\/www.aclweb.org\/anthology\/2021.naacl-main.466\/","DOI":"10.18653\/v1\/2021.naacl-main.466"},{"key":"7072_CR46","doi-asserted-by":"publisher","unstructured":"Rajpurkar P, Zhang J, Lopyrev K, Liang P (2016) Squad: 100, 000+ questions for machine comprehension of text. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016, Austin, Texas, USA, November 1-4, 2016, pp. 2383\u20132392. The Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/d16-1264","DOI":"10.18653\/v1\/d16-1264"},{"key":"7072_CR47","doi-asserted-by":"crossref","unstructured":"Reddy S, Chen D, Manning CD (2019) Coqa: A conversational question answering challenge. Trans. Assoc. Comput. Linguistics 7, 249\u2013266. https:\/\/transacl.org\/ojs\/index.php\/tacl\/article\/view\/1572","DOI":"10.1162\/tacl_a_00266"},{"issue":"4","key":"7072_CR48","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1561\/1500000019","volume":"3","author":"SE Robertson","year":"2009","unstructured":"Robertson SE, Zaragoza H (2009) The probabilistic relevance framework: BM25 and beyond. Found Trends Inf Retr 3(4):333\u2013389. https:\/\/doi.org\/10.1561\/1500000019","journal-title":"Found Trends Inf Retr"},{"key":"7072_CR49","doi-asserted-by":"crossref","unstructured":"Sachan DS, Patwary M, Shoeybi M, Kant N, Ping W, Hamilton WL, Catanzaro B (2020) End-to-end training of neural retrievers for open-domain question answering. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL\/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1-6, 2021, pp. 6648\u20136662. Association for Computational Linguistics. https:\/\/aclanthology.org\/2021.acl-long.519","DOI":"10.18653\/v1\/2021.acl-long.519"},{"key":"7072_CR50","unstructured":"Sachan DS, Reddy S, Hamilton WL, Dyer C, Yogatama D (2021) End-to-end training of multi-document reader and retriever for open-domain question answering. CoRR . http:\/\/arxiv.org\/abs\/2106.05346"},{"key":"7072_CR51","unstructured":"Seo MJ, Kembhavi A, Farhadi A, Hajishirzi H (2017) Bidirectional attention flow for machine comprehension. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net. https:\/\/openreview.net\/forum?id=HJ0UKP9ge"},{"key":"7072_CR52","doi-asserted-by":"publisher","unstructured":"Shao N, Cui Y, Liu T, Wang S, Hu G (2020) Is graph structure necessary for multi-hop question answering? In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020, pp. 7187\u20137192. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.583","DOI":"10.18653\/v1\/2020.emnlp-main.583"},{"key":"7072_CR53","doi-asserted-by":"crossref","unstructured":"Shao N, Cui Y, Liu T, Wang S, Hu G (2021) Memory augmented sequential paragraph retrieval for multi-hop question answering. CoRR . http:\/\/arxiv.org\/abs\/2102.03741","DOI":"10.18653\/v1\/2020.emnlp-main.583"},{"key":"7072_CR54","unstructured":"Song L, Wang Z, Yu M, Zhang Y, Florian R, Gildea D (2018) Exploring graph-structured passage representation for multi-hop reading comprehension with graph neural networks. CoRR. http:\/\/arxiv.org\/abs\/1809.02040"},{"key":"7072_CR55","doi-asserted-by":"crossref","unstructured":"Tu M, Huang K, Wang G, Huang J, He X, Zhou B (2020) Select, answer and explain: Interpretable multi-hop reading comprehension over multiple documents. In: The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020, pp. 9073\u20139080. AAAI Press. https:\/\/aaai.org\/ojs\/index.php\/AAAI\/article\/view\/6441","DOI":"10.1609\/aaai.v34i05.6441"},{"key":"7072_CR56","doi-asserted-by":"publisher","unstructured":"Tu M, Wang G, Huang J, Tang Y, He X, Zhou B (2019) Multi-hop reading comprehension across multiple documents by reasoning over heterogeneous graphs. In: Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers, pp. 2704\u20132713. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/p19-1260","DOI":"10.18653\/v1\/p19-1260"},{"key":"7072_CR57","doi-asserted-by":"publisher","unstructured":"Wang W, Yang N, Wei F, Chang B, Zhou M (2017) Gated self-matching networks for reading comprehension and question answering. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, July 30 - August 4, Volume 1: Long Papers, pp. 189\u2013198. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/P17-1018","DOI":"10.18653\/v1\/P17-1018"},{"key":"7072_CR58","doi-asserted-by":"crossref","unstructured":"Welbl J, Stenetorp P, Riedel S (2018) Constructing datasets for multi-hop reading comprehension across documents. Trans. Assoc. Comput. Linguistics 6, 287\u2013302. https:\/\/transacl.org\/ojs\/index.php\/tacl\/article\/view\/1325","DOI":"10.1162\/tacl_a_00021"},{"key":"7072_CR59","unstructured":"Xiong L, Xiong C, Li Y, Tang K, Liu J, Bennett PN, Ahmed J, Overwijk A (2021) Approximate nearest neighbor negative contrastive learning for dense text retrieval. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net. https:\/\/openreview.net\/forum?id=zeFrfgyZln"},{"key":"7072_CR60","unstructured":"Xiong W, Li XL, Iyer S, Du J, Lewis PSH, Wang WY, Mehdad Y, Yih S, Riedel S, Kiela D, Oguz B (2021) Answering complex open-domain questions with multi-hop dense retrieval. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net. https:\/\/openreview.net\/forum?id=EMHoBG0avc1"},{"key":"7072_CR61","doi-asserted-by":"publisher","unstructured":"Yadav V, Bethard S, Surdeanu M (2020) Unsupervised alignment-based iterative evidence retrieval for multi-hop question answering. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, pp. 4514\u20134525. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.414","DOI":"10.18653\/v1\/2020.acl-main.414"},{"key":"7072_CR62","unstructured":"Yang Z, Dai Z, Yang Y, Carbonell JG, Salakhutdinov R, Le QV (2019) Xlnet: Generalized autoregressive pretraining for language understanding. In: Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, pp. 5754\u20135764. https:\/\/proceedings.neurips.cc\/paper\/2019\/hash\/dc6a7e655d7e5840e66733e9ee67cc69-Abstract.html"},{"key":"7072_CR63","doi-asserted-by":"publisher","unstructured":"Yang Z, Qi P, Zhang S, Bengio Y, Cohen WW, Salakhutdinov R, Manning CD (2018) Hotpotqa: A dataset for diverse, explainable multi-hop question answering. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31 - November 4, 2018, pp. 2369\u20132380. Association for Computational Linguistics. https:\/\/doi.org\/10.18653\/v1\/d18-1259","DOI":"10.18653\/v1\/d18-1259"},{"key":"7072_CR64","unstructured":"Yih W, Toutanova K, Platt JC, Meek C (2011) Learning discriminative projections for text similarity measures. In: Proceedings of the Fifteenth Conference on Computational Natural Language Learning, CoNLL 2011, Portland, Oregon, USA, June 23-24, 2011, pp. 247\u2013256. ACL. https:\/\/www.aclweb.org\/anthology\/W11-0329\/"},{"key":"7072_CR65","unstructured":"Zaheer M, Guruganesh G, Dubey KA, Ainslie J, Alberti C, Onta\u00f1\u00f3n S, Pham P, Ravula A, Wang Q, Yang L, Ahmed A (2020) Big bird: Transformers for longer sequences. In: Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual. https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/c8512d142a2d849725f31a9a7a361ab9-Abstract.html"},{"key":"7072_CR66","doi-asserted-by":"publisher","unstructured":"Zhang Y, Nie P, Ramamurthy A, Song L (2021) Answering any-hop open-domain questions with iterative document reranking. In: SIGIR \u201921: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, July 11-15, 2021, pp. 481\u2013490. ACM. https:\/\/doi.org\/10.1145\/3404835.3462853","DOI":"10.1145\/3404835.3462853"},{"key":"7072_CR67","unstructured":"Zhao C, Xiong C, Rosset C, Song X, Bennett PN, Tiwary S (2020) Transformer-xh: Multi-evidence reasoning with extra hop attention. In: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net. https:\/\/openreview.net\/forum?id=r1eIiCNYwS"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07072-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07072-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07072-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,29]],"date-time":"2023-01-29T19:10:57Z","timestamp":1675019457000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07072-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,22]]},"references-count":67,"journal-issue":{"issue":"14","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["7072"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07072-0","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,22]]},"assertion":[{"value":"26 September 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 February 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 March 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}