{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T12:20:03Z","timestamp":1743078003175,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":33,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789811982330"},{"type":"electronic","value":"9789811982347"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-981-19-8234-7_30","type":"book-chapter","created":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T07:05:58Z","timestamp":1669187158000},"page":"383-395","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploring Retriever-Reader Approaches in\u00a0Question-Answering on\u00a0Scientific Documents"],"prefix":"10.1007","author":[{"given":"Dieu-Hien","family":"Nguyen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nguyen-Khang","family":"Le","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minh Le","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,24]]},"reference":[{"key":"30_CR1","doi-asserted-by":"crossref","unstructured":"Ainslie, J., et al.: ETC: encoding long and structured inputs in transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 268\u2013284. Association for Computational Linguistics, Online, November 2020","DOI":"10.18653\/v1\/2020.emnlp-main.19"},{"key":"30_CR2","unstructured":"Beltagy, I., Peters, M.E., Cohan, A.: Longformer: the long-document transformer. CoRR (2020)"},{"key":"30_CR3","unstructured":"Campos, D.F.,et al.: Ms marco: a human generated machine reading comprehension dataset. ArXiv (2016)"},{"key":"30_CR4","doi-asserted-by":"crossref","unstructured":"Chen, D., Fisch, A., Weston, J., Bordes, A.: Reading Wikipedia to answer open-domain questions. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics (ACL) (2017)","DOI":"10.18653\/v1\/P17-1171"},{"key":"30_CR5","unstructured":"Clark, K., Luong, M.T., Le, Q.V., Manning, C.D.: ELECTRA: pre-training text encoders as discriminators rather than generators. In: ICLR (2020)"},{"key":"30_CR6","doi-asserted-by":"crossref","unstructured":"Dasigi, P., Lo, K., Beltagy, I., Cohan, A., Smith, N.A., Gardner, M.: A dataset of information-seeking questions and answers anchored in research papers. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 4599\u20134610. Association for Computational Linguistics, Online, June 2021","DOI":"10.18653\/v1\/2021.naacl-main.365"},{"key":"30_CR7","unstructured":"Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) 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, 2\u20137 June 2019, vol. 1 (Long and Short Papers), pp. 4171\u20134186. Association for Computational Linguistics (2019)"},{"key":"30_CR8","doi-asserted-by":"crossref","unstructured":"Gao, L., Dai, Z., Callan, J.: Modularized transfomer-based ranking framework. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 4180\u20134190. Association for Computational Linguistics, Online, November 2020","DOI":"10.18653\/v1\/2020.emnlp-main.342"},{"key":"30_CR9","unstructured":"Guu, K., Lee, K., Tung, Z., Pasupat, P., Chang, M.: REALM: retrieval-augmented language model pre-training. CoRR abs\/2002.08909 (2020)"},{"key":"30_CR10","doi-asserted-by":"crossref","unstructured":"Iyer, S., Min, S., Mehdad, Y., Yih, W.: RECONSIDER: re-ranking using span-focused cross-attention for open domain question answering. CoRR (2020)","DOI":"10.18653\/v1\/2021.naacl-main.100"},{"key":"30_CR11","doi-asserted-by":"publisher","unstructured":"Joshi, M., Choi, E., Weld, D., Zettlemoyer, L.: TriviaQA: a large scale distantly supervised challenge dataset for reading comprehension. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1601\u20131611. Association for Computational Linguistics, Vancouver, Canada, July 2017. https:\/\/doi.org\/10.18653\/v1\/P17-1147","DOI":"10.18653\/v1\/P17-1147"},{"key":"30_CR12","doi-asserted-by":"crossref","unstructured":"Karpukhin, V., et al.: Dense passage retrieval for open-domain question answering. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 6769\u20136781. Association for Computational Linguistics, Online, November 2020","DOI":"10.18653\/v1\/2020.emnlp-main.550"},{"key":"30_CR13","doi-asserted-by":"crossref","unstructured":"Khattab, O., Zaharia, M.: Colbert: Efficient and effective passage search via contextualized late interaction over BERT. CoRR (2020)","DOI":"10.1145\/3397271.3401075"},{"key":"30_CR14","doi-asserted-by":"crossref","unstructured":"Kwiatkowski, T., et al.: Natural questions: a benchmark for question answering research. Trans. Assoc. Comput. Ling. (2019)","DOI":"10.1162\/tacl_a_00276"},{"key":"30_CR15","unstructured":"Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., Soricut, R.: Albert: a lite BERT for self-supervised learning of language representations. In: International Conference on Learning Representations (2020)"},{"key":"30_CR16","doi-asserted-by":"publisher","unstructured":"Lee, K., Chang, M.W., Toutanova, K.: Latent retrieval for weakly supervised open domain question answering. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 6086\u20136096. Association for Computational Linguistics, Florence, Italy, July 2019. https:\/\/doi.org\/10.18653\/v1\/P19-1612","DOI":"10.18653\/v1\/P19-1612"},{"key":"30_CR17","unstructured":"Liu, Y., et al.: Roberta: a robustly optimized BERT pretraining approach. CoRR (2019)"},{"key":"30_CR18","doi-asserted-by":"crossref","unstructured":"Mao, Y., et al.: Reader-guided passage reranking for open-domain question answering. CoRR (2021)","DOI":"10.18653\/v1\/2021.findings-acl.29"},{"key":"30_CR19","doi-asserted-by":"publisher","unstructured":"Ni, J., et al.: Sentence-t5: scalable sentence encoders from pre-trained text-to-text models (2021). https:\/\/doi.org\/10.48550\/ARXIV.2108.08877, https:\/\/arxiv.org\/abs\/2108.08877","DOI":"10.48550\/ARXIV.2108.08877"},{"key":"30_CR20","unstructured":"Nogueira, R., Cho, K.: Passage re-ranking with BERT. CoRR (2019)"},{"key":"30_CR21","unstructured":"Nogueira, R., Yang, W., Cho, K., Lin, J.: Multi-stage document ranking with BERT. CoRR (2019)"},{"key":"30_CR22","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"30_CR23","doi-asserted-by":"publisher","unstructured":"Rajpurkar, P., Zhang, J., Lopyrev, K., Liang, P.: SQuAD: 100,000+ questions for machine comprehension of text. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. pp. 2383\u20132392. Association for Computational Linguistics, Austin, Texas, November 2016. https:\/\/doi.org\/10.18653\/v1\/D16-1264","DOI":"10.18653\/v1\/D16-1264"},{"key":"30_CR24","doi-asserted-by":"crossref","unstructured":"Reimers, N., Gurevych, I.: Sentence-BERT: sentence embeddings using Siamese BERT-networks. CoRR (2019)","DOI":"10.18653\/v1\/D19-1410"},{"key":"30_CR25","doi-asserted-by":"crossref","unstructured":"Seo, M., Lee, J., Kwiatkowski, T., Parikh, A., Farhadi, A., Hajishirzi, H.: Real-time open-domain question answering with dense-sparse phrase index. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 4430\u20134441. Association for Computational Linguistics, Florence, Italy, July 2019","DOI":"10.18653\/v1\/P19-1436"},{"key":"30_CR26","doi-asserted-by":"publisher","unstructured":"Sun, H., Cohen, W.W., Salakhutdinov, R.: Iterative hierarchical attention for answering complex questions over long documents. In; ICLR (2021). https:\/\/doi.org\/10.48550\/ARXIV.2106.00200","DOI":"10.48550\/ARXIV.2106.00200"},{"key":"30_CR27","doi-asserted-by":"publisher","unstructured":"Talmor, A., Berant, J.: The web as a knowledge-base for answering complex questions. 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. 641\u2013651. Association for Computational Linguistics, New Orleans, Louisiana, June 2018. https:\/\/doi.org\/10.18653\/v1\/N18-1059","DOI":"10.18653\/v1\/N18-1059"},{"key":"30_CR28","unstructured":"Wang, W., Wei, F., Dong, L., Bao, H., Yang, N., Zhou, M.: Minilm: deep self-attention distillation for task-agnostic compression of pre-trained transformers. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS 2020, Curran Associates Inc., Red Hook, NY, USA (2020)"},{"key":"30_CR29","doi-asserted-by":"crossref","unstructured":"Wang, Z., Ng, P., Ma, X., Nallapati, R., Xiang, B.: Multi-passage BERT: a globally normalized BERT model for open-domain question answering. In: EMNLP (2019)","DOI":"10.18653\/v1\/D19-1599"},{"key":"30_CR30","unstructured":"Wolf, T., et al.: Transformers: State-of-the-art natural language processing. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pp. 38\u201345. Association for Computational Linguistics, Online, October 2020"},{"key":"30_CR31","doi-asserted-by":"crossref","unstructured":"Yan, M., et al.: IDST at TREC 2019 deep learning track: deep cascade ranking with generation-based document expansion and pre-trained language modeling. In: Voorhees, E.M., Ellis, A. (eds.) Proceedings of the Twenty-Eighth Text REtrieval Conference, TREC 2019, Gaithersburg, Maryland, USA, 13\u201315 November 2019. NIST Special Publication, vol. 1250. National Institute of Standards and Technology (NIST) (2019)","DOI":"10.6028\/NIST.SP.1250.deep-IDST"},{"key":"30_CR32","doi-asserted-by":"publisher","unstructured":"Yang, Z., et al.: HotpotQA: a dataset for diverse, explainable multi-hop question answering. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2369\u20132380. Association for Computational Linguistics, Brussels, Belgium, October\u2013November 2018. https:\/\/doi.org\/10.18653\/v1\/D18-1259","DOI":"10.18653\/v1\/D18-1259"},{"key":"30_CR33","unstructured":"Zaheer, M., et al.: Big bird: transformers for longer sequences. In: 33th Proceeding Conference on Advances in Neural Information Processing Systems (2020)"}],"container-title":["Communications in Computer and Information Science","Recent Challenges in Intelligent Information and Database Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-19-8234-7_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T11:01:55Z","timestamp":1728471715000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-19-8234-7_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811982330","9789811982347"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-981-19-8234-7_30","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"24 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACIIDS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Intelligent Information and Database Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ho Chi Minh City","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aciids2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aciids.pwr.edu.pl\/2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}