{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T16:04:49Z","timestamp":1742918689943,"version":"3.40.3"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030997359"},{"type":"electronic","value":"9783030997366"}],"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\/978-3-030-99736-6_22","type":"book-chapter","created":{"date-parts":[[2022,4,4]],"date-time":"2022-04-04T19:02:47Z","timestamp":1649098967000},"page":"322-335","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Bi-granularity Adversarial Training for Non-factoid Answer Retrieval"],"prefix":"10.1007","author":[{"given":"Zhiling","family":"Jin","sequence":"first","affiliation":[]},{"given":"Yu","family":"Hong","sequence":"additional","affiliation":[]},{"given":"Hongyu","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Jianmin","family":"Yao","sequence":"additional","affiliation":[]},{"given":"Min","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,4,5]]},"reference":[{"key":"22_CR1","doi-asserted-by":"crossref","unstructured":"Ahmad, A., Constant, N., Yang, Y., Cer, D.: ReQA: an evaluation for end-to-end answer retrieval models. arXiv preprint arXiv:1907.04780 (2019)","DOI":"10.18653\/v1\/D19-5819"},{"key":"22_CR2","unstructured":"Chakraborty, A., Alam, M., Dey, V., Chattopadhyay, A., Mukhopadhyay, D.: Adversarial attacks and defences: a survey. arXiv preprint arXiv:1810.00069 (2018)"},{"key":"22_CR3","doi-asserted-by":"crossref","unstructured":"Chen, D., Peng, S., Li, K., Xu, Y., Zhang, J., Xie, X.: Re-ranking answer selection with similarity aggregation. In: SIGIR, pp. 1677\u20131680 (2020)","DOI":"10.1145\/3397271.3401199"},{"key":"22_CR4","doi-asserted-by":"crossref","unstructured":"Cohen, D., Yang, L., Croft, W.B.: WikiPassageQA: a benchmark collection for research on non-factoid answer passage retrieval. In: SIGIR, pp. 1165\u20131168 (2018)","DOI":"10.1145\/3209978.3210118"},{"key":"22_CR5","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018)"},{"key":"22_CR6","doi-asserted-by":"crossref","unstructured":"Dror, R., Baumer, G., Shlomov, S., Reichart, R.: The Hitchhiker\u2019s guide to testing statistical significance in natural language processing. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1383\u20131392 (2018)","DOI":"10.18653\/v1\/P18-1128"},{"key":"22_CR7","doi-asserted-by":"crossref","unstructured":"Eger, S., et al.: Text processing like humans do: visually attacking and shielding NLP systems. arXiv preprint arXiv:1903.11508 (2019)","DOI":"10.18653\/v1\/N19-1165"},{"key":"22_CR8","doi-asserted-by":"crossref","unstructured":"Gan, W.C., Ng, H.T.: Improving the robustness of question answering systems to question paraphrasing. In: ACL, pp. 6065\u20136075 (2019)","DOI":"10.18653\/v1\/P19-1610"},{"key":"22_CR9","doi-asserted-by":"crossref","unstructured":"Garg, S., Ramakrishnan, G.: BAE: BERT-based adversarial examples for text classification. arXiv preprint arXiv:2004.01970 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.498"},{"key":"22_CR10","doi-asserted-by":"crossref","unstructured":"Garg, S., Vu, T., Moschitti, A.: TANDA: transfer and adapt pre-trained transformer models for answer sentence selection. In: AAAI, vol. 34, pp. 7780\u20137788 (2020)","DOI":"10.1609\/aaai.v34i05.6282"},{"issue":"4","key":"22_CR11","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1162\/COLI_a_00237","volume":"41","author":"F Hill","year":"2015","unstructured":"Hill, F., Reichart, R., Korhonen, A.: SimLex-999: evaluating semantic models with (genuine) similarity estimation. Comput. Linguist. 41(4), 665\u2013695 (2015)","journal-title":"Comput. Linguist."},{"key":"22_CR12","doi-asserted-by":"crossref","unstructured":"Iyyer, M., Wieting, J., Gimpel, K., Zettlemoyer, L.: Adversarial example generation with syntactically controlled paraphrase networks. arXiv:1804.06059 (2018)","DOI":"10.18653\/v1\/N18-1170"},{"key":"22_CR13","doi-asserted-by":"crossref","unstructured":"Jia, R., Liang, P.: Adversarial examples for evaluating reading comprehension systems. arXiv preprint arXiv:1707.07328 (2017)","DOI":"10.18653\/v1\/D17-1215"},{"key":"22_CR14","doi-asserted-by":"crossref","unstructured":"Jin, D., Jin, Z., Zhou, J.T., Szolovits, P.: Is BERT really robust? A strong baseline for natural language attack on text classification and entailment. In: AAAI, vol. 34, pp. 8018\u20138025 (2020)","DOI":"10.1609\/aaai.v34i05.6311"},{"key":"22_CR15","unstructured":"Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692 (2019)"},{"key":"22_CR16","unstructured":"Mass, Y., Roitman, H., Erera, S., Rivlin, O., Weiner, B., Konopnicki, D.: A study of BERT for non-factoid question-answering under passage length constraints. arXiv preprint arXiv:1908.06780 (2019)"},{"issue":"11","key":"22_CR17","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1145\/219717.219748","volume":"38","author":"GA Miller","year":"1995","unstructured":"Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39\u201341 (1995)","journal-title":"Commun. ACM"},{"key":"22_CR18","doi-asserted-by":"crossref","unstructured":"Morris, J., Lifland, E., Yoo, J.Y., Grigsby, J., Jin, D., Qi, Y.: TextAttack: a framework for adversarial attacks, data augmentation, and adversarial training in NLP. In: EMNLP, pp. 119\u2013126 (2020)","DOI":"10.18653\/v1\/2020.emnlp-demos.16"},{"key":"22_CR19","doi-asserted-by":"crossref","unstructured":"Morris, J.X., Lifland, E., Lanchantin, J., Ji, Y., Qi, Y.: Reevaluating adversarial examples in natural language. arXiv preprint arXiv:2004.14174 (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.341"},{"key":"22_CR20","doi-asserted-by":"crossref","unstructured":"Mrk\u0161i\u0107, et al.: Counter-fitting word vectors to linguistic constraints. arXiv preprint arXiv:1603.00892 (2016)","DOI":"10.18653\/v1\/N16-1018"},{"key":"22_CR21","doi-asserted-by":"crossref","unstructured":"Niven, T., Kao, H.Y.: Probing neural network comprehension of natural language arguments. arXiv preprint arXiv:1907.07355 (2019)","DOI":"10.18653\/v1\/P19-1459"},{"key":"22_CR22","unstructured":"Palangi, H., et al.: Semantic modelling with long-short-term memory for information retrieval. arXiv preprint arXiv:1412.6629 (2014)"},{"key":"22_CR23","doi-asserted-by":"crossref","unstructured":"Pang, L., Lan, Y., Guo, J., Xu, J., Wan, S., Cheng, X.: Text matching as image recognition. In: AAAI, vol. 30 (2016)","DOI":"10.1609\/aaai.v30i1.10341"},{"key":"22_CR24","doi-asserted-by":"crossref","unstructured":"Parikh, A.P., T\u00e4ckstr\u00f6m, O., Das, D., Uszkoreit, J.: A decomposable attention model for natural language inference. arXiv preprint arXiv:1606.01933 (2016)","DOI":"10.18653\/v1\/D16-1244"},{"key":"22_CR25","doi-asserted-by":"crossref","unstructured":"Ren, S., Deng, Y., He, K., Che, W.: Generating natural language adversarial examples through probability weighted word saliency. In: ACL, pp. 1085\u20131097 (2019)","DOI":"10.18653\/v1\/P19-1103"},{"key":"22_CR26","doi-asserted-by":"crossref","unstructured":"R\u00fcckl\u00e9, A., Moosavi, N.S., Gurevych, I.: COALA: a neural coverage-based approach for long answer selection with small data. In: AAAI, vol. 33, pp. 6932\u20136939 (2019)","DOI":"10.1609\/aaai.v33i01.33016932"},{"key":"22_CR27","unstructured":"Santos, C.d., Tan, M., Xiang, B., Zhou, B.: Attentive pooling networks. arXiv preprint arXiv:1602.03609 (2016)"},{"key":"22_CR28","doi-asserted-by":"crossref","unstructured":"Shen, Y., et al.: Knowledge-aware attentive neural network for ranking question answer pairs. In: SIGIR, pp. 901\u2013904 (2018)","DOI":"10.1145\/3209978.3210081"},{"key":"22_CR29","doi-asserted-by":"crossref","unstructured":"Tay, Y., Tuan, L.A., Hui, S.C.: Hyperbolic representation learning for fast and efficient neural question answering. In: WSDM, pp. 583\u2013591 (2018)","DOI":"10.1145\/3159652.3159664"},{"key":"22_CR30","unstructured":"Wang, M., Smith, N.A., Mitamura, T.: What is the jeopardy model? A quasi-synchronous grammar for QA. In: EMNLP-CoNLL, pp. 22\u201332 (2007)"},{"key":"22_CR31","unstructured":"Wang, W., Wang, L., Wang, R., Wang, Z., Ye, A.: Towards a robust deep neural network in texts: a survey. arXiv preprint arXiv:1902.07285 (2019)"},{"key":"22_CR32","unstructured":"Wolf, T., et al.: HuggingFace\u2019s transformers: state-of-the-art natural language processing. arXiv preprint arXiv:1910.03771 (2019)"},{"key":"22_CR33","unstructured":"Xu, P., Ma, X., Nallapati, R., Xiang, B.: Passage ranking with weak supervision. arXiv preprint arXiv:1905.05910 (2019)"},{"key":"22_CR34","doi-asserted-by":"crossref","unstructured":"Yang, L., Ai, Q., Guo, J., Croft, W.B.: aNMM: ranking short answer texts with attention-based neural matching model. In: CIKM, pp. 287\u2013296 (2016)","DOI":"10.1145\/2983323.2983818"},{"key":"22_CR35","unstructured":"Zhang, X., Zhao, J., LeCun, Y.: Character-level convolutional networks for text classification. arXiv preprint arXiv:1509.01626 (2015)"},{"key":"22_CR36","unstructured":"Zhu, H., Mak, D., Gioannini, J., Xia, F.: Nlpstattest: a toolkit for comparing NLP system performance. arXiv preprint arXiv:2011.13231 (2020)"}],"container-title":["Lecture Notes in Computer Science","Advances in Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-99736-6_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T08:57:35Z","timestamp":1710233855000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-99736-6_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030997359","9783030997366"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-99736-6_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"5 April 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Information Retrieval","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Stavanger","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Norway","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":"10 April 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 April 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"44","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecir2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecir2022.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"395","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"35","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"29","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"9% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4-6","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Additionally, there are other papers: 11 reproducibility, 12 doctoral, 13 CLEF Labs, 5 workshops and 4 tutorials.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}