{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T04:06:29Z","timestamp":1770350789418,"version":"3.49.0"},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"19","license":[{"start":{"date-parts":[[2022,8,17]],"date-time":"2022-08-17T00:00:00Z","timestamp":1660694400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,8,17]],"date-time":"2022-08-17T00:00:00Z","timestamp":1660694400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100012165","name":"Key Technologies Research and Development Program","doi-asserted-by":"publisher","award":["2018YFC0830200"],"award-info":[{"award-number":["2018YFC0830200"]}],"id":[{"id":"10.13039\/501100012165","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012165","name":"Key Technologies Research and Development Program","doi-asserted-by":"publisher","award":["2018YFC0830206"],"award-info":[{"award-number":["2018YFC0830206"]}],"id":[{"id":"10.13039\/501100012165","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012165","name":"Key Technologies Research and Development Program","doi-asserted-by":"publisher","award":["2020YFB1406902"],"award-info":[{"award-number":["2020YFB1406902"]}],"id":[{"id":"10.13039\/501100012165","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012165","name":"Key Technologies Research and Development Program","doi-asserted-by":"publisher","award":["2020YFC0832505"],"award-info":[{"award-number":["2020YFC0832505"]}],"id":[{"id":"10.13039\/501100012165","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,10]]},"DOI":"10.1007\/s00521-022-07588-5","type":"journal-article","created":{"date-parts":[[2022,8,17]],"date-time":"2022-08-17T04:02:45Z","timestamp":1660708965000},"page":"16367-16385","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Toward automatic support for leading court debates: a novel task proposal & effective approach of judicial question generation"],"prefix":"10.1007","volume":"34","author":[{"given":"Changzhen","family":"Ji","sequence":"first","affiliation":[]},{"given":"Yating","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xiaozhong","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Adam","family":"Jatowt","sequence":"additional","affiliation":[]},{"given":"Sourav S.","family":"Bhowmick","sequence":"additional","affiliation":[]},{"given":"Changlong","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Conghui","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Tiejun","family":"Zhao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,17]]},"reference":[{"key":"7588_CR1","unstructured":"Center JI Some federal judges handle inordinate caseloads. available from https:\/\/trac.syr.edu\/tracreports\/judge\/501\/. Accessed: 2019-05-06"},{"key":"7588_CR2","unstructured":"Center JI As workloads rise in federal courts, judge counts remain flat. available from https:\/\/trac.syr.edu\/tracreports\/judge\/364\/. Accessed: 2019-05-06"},{"key":"7588_CR3","unstructured":"CMPA-Legal, regulatory proceedings: When multiple defendants are involved in a legal action, things can get complicated. Accessed: 2020-01-22"},{"key":"7588_CR4","first-page":"249","volume":"11","author":"N Andrews","year":"2001","unstructured":"Andrews N (2001) Multi-party proceedings in england: representative and group actions. Duke J Comp Int\u2019l L 11:249","journal-title":"Duke J Comp Int\u2019l L"},{"key":"7588_CR5","first-page":"182","volume":"4","author":"B Hanotiau","year":"2007","unstructured":"Hanotiau B, Lee JB (2007) Complex arbitrations-multiparty, multicontract, multi-issue and class actions. Rev Bras Arb 4:182","journal-title":"Rev Bras Arb"},{"key":"7588_CR6","doi-asserted-by":"crossref","unstructured":"Deng Y, Li Y, Sun F, Ding B, Lam W (2021) Unified conversational recommendation policy learning via graph-based reinforcement learning. In: 44th International ACM SIGIR conference on research and development in information retrieval . ACM","DOI":"10.1145\/3404835.3462913"},{"key":"7588_CR7","doi-asserted-by":"crossref","unstructured":"Wang J, Liu J, Bi W, Liu X, He K, Xu R, Yang M (2020) Improving knowledge-aware dialogue generation via knowledge base question answering. In: Proceedings of the AAAI conference on artificial intelligence, pp. 9169\u20139176","DOI":"10.1609\/aaai.v34i05.6453"},{"key":"7588_CR8","doi-asserted-by":"crossref","unstructured":"Kaiser M, Saha\u00a0Roy R, Weikum G (2021) Reinforcement learning from reformulations in conversational question answering over knowledge graphs. In: 44th International ACM SIGIR conference on research and development in information retrieval . ACM","DOI":"10.1145\/3404835.3462859"},{"key":"7588_CR9","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A.N, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. In: Conference on neural information processing systems, pp. 5998\u20136008"},{"issue":"4","key":"7588_CR10","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1177\/000276426000400409","volume":"4","author":"S Nagel","year":"1960","unstructured":"Nagel S (1960) Using simple calculations to predict judicial decisions. American Behav Sci 4(4):24\u201328","journal-title":"American Behav Sci"},{"key":"7588_CR11","doi-asserted-by":"crossref","unstructured":"McElvain G, Sanchez G, Matthews S, Teo D, Pompili F, Custis T (2019) Westsearch plus: A non-factoid question-answering system for the legal domain. In: Proceedings of the 42rd international ACM SIGIR conference on research and development in information retrieval, pp. 1361\u20131364","DOI":"10.1145\/3331184.3331397"},{"key":"7588_CR12","doi-asserted-by":"crossref","unstructured":"Biega A.J, Potash P, Daum\u00e9 H, Diaz F, Finck M (2020) Operationalizing the legal principle of data minimization for personalization. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, pp. 399\u2013408","DOI":"10.1145\/3397271.3401034"},{"key":"7588_CR13","doi-asserted-by":"crossref","unstructured":"Bhattacharya P, Ghosh K, Pal A, Ghosh S (2020) Hier-spcnet: A legal statute hierarchy-based heterogeneous network for computing legal case document similarity. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, pp. 1657\u20131660","DOI":"10.1145\/3397271.3401191"},{"key":"7588_CR14","doi-asserted-by":"crossref","unstructured":"Shao Y (2020) Towards legal case retrieval. In: Proceedings of the 43rd International ACM SIGIR conference on research and development in information retrieval, pp. 2485\u20132485","DOI":"10.1145\/3397271.3401457"},{"key":"7588_CR15","doi-asserted-by":"crossref","unstructured":"Dong Q, Niu S (2021) Legal judgment prediction via relational learning. In: Proceedings of the 44rd international ACM SIGIR conference on research and development in information retrieval, pp. 983\u2013992","DOI":"10.1145\/3404835.3462931"},{"key":"7588_CR16","doi-asserted-by":"crossref","unstructured":"Ma L, Zhang Y, Wang T, Liu X, Ye W, Sun C, Zhang S (2021) Legal judgment prediction with multi-stage case representation learning in the real court setting. In: Proceedings of the 44rd international ACM SIGIR conference on research and development in information retrieval, pp. 993\u20131002","DOI":"10.1145\/3404835.3462945"},{"key":"7588_CR17","doi-asserted-by":"crossref","unstructured":"Shao Y, Wu Y, Liu Y, Mao J, Zhang M, Ma S (2021) Investigating user behavior in legal case retrieval. In: Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval, pp. 962\u2013972","DOI":"10.1145\/3404835.3462876"},{"key":"7588_CR18","doi-asserted-by":"crossref","unstructured":"Ma Y, Shao Y, Wu Y, Liu Y, Zhang R, Zhang M, Ma S (2021) Lecard: a legal case retrieval dataset for chinese law system. In: Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval, pp. 2342\u20132348","DOI":"10.1145\/3404835.3463250"},{"key":"7588_CR19","doi-asserted-by":"crossref","unstructured":"Roegiest A, Lee E (2019) On tradeoffs between document signature methods for a legal due diligence corpus. In: Proceedings of the 42rd international ACM SIGIR conference on research and development in information retrieval, pp. 1001\u20131004","DOI":"10.1145\/3331184.3331311"},{"key":"7588_CR20","doi-asserted-by":"crossref","unstructured":"Yue L, Liu Q, Jin B, Wu H, Zhang K, An Y, Cheng M, Yin B, Wu D (2021) Neurjudge: A circumstance-aware neural framework for legal judgment prediction. In: Proceedings of the 44rd international ACM SIGIR conference on research and development in information retrieval, pp. 973\u2013982","DOI":"10.1145\/3404835.3462826"},{"key":"7588_CR21","doi-asserted-by":"crossref","unstructured":"Liu B, Wu Y, Liu Y, Zhang F, Shao Y, Li C, Zhang M, Ma S (2021) Conversational vs traditional: Comparing search behavior and outcome in legal case retrieval. In: Proceedings of the 44rd international ACM SIGIR conference on research and development in information retrieval, pp. 1622\u20131626","DOI":"10.1145\/3404835.3463064"},{"key":"7588_CR22","doi-asserted-by":"publisher","DOI":"10.1017\/9781316761380","volume-title":"Artificial intelligence and legal analytics: new tools for law practice in the digital age","author":"KD Ashley","year":"2017","unstructured":"Ashley KD (2017) Artificial intelligence and legal analytics: new tools for law practice in the digital age. Cambridge University Press, England"},{"key":"7588_CR23","first-page":"4734","volume-title":"CLC-RS: a Chinese legal case retrieval system with masked language ranking","author":"J Li","year":"2021","unstructured":"Li J, Yang M, Li C (2021) CLC-RS: a Chinese legal case retrieval system with masked language ranking. Association for Computing Machinery, New York, pp 4734\u20134738"},{"key":"7588_CR24","doi-asserted-by":"crossref","unstructured":"Zhou X, Zhang Y, Liu X, Sun C, Si L (2019) Legal intelligence for e-commerce: multi-task learning by leveraging multiview dispute representation. In: Proceedings of the 42rd international ACM SIGIR conference on research and development in information retrieval, pp. 315\u2013324","DOI":"10.1145\/3331184.3331212"},{"key":"7588_CR25","doi-asserted-by":"crossref","unstructured":"Wang P, Fan Y, Niu S, Yang Z, Zhang Y, Guo J (2019) Hierarchical matching network for crime classification. In: Proceedings of the 42rd international ACM SIGIR conference on research and development in information retrieval, pp. 325\u2013334","DOI":"10.1145\/3331184.3331223"},{"issue":"3","key":"7588_CR26","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1007\/s10462-017-9566-2","volume":"51","author":"A Kanapala","year":"2019","unstructured":"Kanapala A, Pal S, Pamula R (2019) Text summarization from legal documents: a survey. Artif Intell Rev 51(3):371\u2013402","journal-title":"Artif Intell Rev"},{"key":"7588_CR27","doi-asserted-by":"crossref","unstructured":"Merchant K, Pande Y (2018) Nlp based latent semantic analysis for legal text summarization. In: 2018 International conference on advances in computing, communications and informatics (ICACCI), pp. 1803\u20131807 . IEEE","DOI":"10.1109\/ICACCI.2018.8554831"},{"key":"7588_CR28","doi-asserted-by":"crossref","unstructured":"Duan X, Zhang Y, Yuan L, Zhou X, Liu X, Wang T, Wang R, Zhang Q, Sun C, Wu F (2019) Legal summarization for multi-role debate dialogue via controversy focus mining and multi-task learning. In: Proceedings of the 28th ACM international conference on information and knowledge management, pp. 1361\u20131370","DOI":"10.1145\/3357384.3357940"},{"key":"7588_CR29","doi-asserted-by":"crossref","unstructured":"Fawei B, Pan J.Z, Kollingbaum M, Wyner A.Z (2018) A methodology for a criminal law and procedure ontology for legal question answering. In: Joint international semantic technology conference, pp. 198\u2013214","DOI":"10.1007\/978-3-030-04284-4_14"},{"key":"7588_CR30","doi-asserted-by":"crossref","unstructured":"Wang Z, Wang B, Duan X, Wu D, Wang S, Hu G, Liu T (2019) IFlyLegal: a Chinese legal system for consultation, law searching, and document analysis. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp. 97\u2013102","DOI":"10.18653\/v1\/D19-3017"},{"issue":"2","key":"7588_CR31","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1631\/FITEE.2100041","volume":"23","author":"W Bin","year":"2022","unstructured":"Bin W, Kun K, Changlong S, Jun F, Yating Z, Xinli Z, Jianghong Z, Yinsheng Z, Fei W (2022) A full-process intelligent trial system for smart court. Front Inf Technol Electron Eng 23(2):186","journal-title":"Front Inf Technol Electron Eng"},{"issue":"1","key":"7588_CR32","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1145\/365153.365168","volume":"9","author":"J Weizenbaum","year":"1966","unstructured":"Weizenbaum J (1966) Eliza-a computer program for the study of natural language communication between man and machine. Commun ACM 9(1):36\u201345","journal-title":"Commun ACM"},{"issue":"2","key":"7588_CR33","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1017\/S0269888906000944","volume":"21","author":"J Schatzmann","year":"2006","unstructured":"Schatzmann J, Weilhammer K, Stuttle M, Young S (2006) A survey of statistical user simulation techniques for reinforcement-learning of dialogue management strategies. The knowledge engineering review 21(2):97\u2013126","journal-title":"The knowledge engineering review"},{"key":"7588_CR34","unstructured":"Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Conference neural information processing system, pp. 27:3104\u20133112"},{"key":"7588_CR35","doi-asserted-by":"crossref","unstructured":"Luong T, Pham H, Manning C.D (2015) Effective approaches to attention-based neural machine translation. In: Proceedings of the 2015 conference on empirical methods in natural language processing, pp. 1412\u20131421","DOI":"10.18653\/v1\/D15-1166"},{"key":"7588_CR36","unstructured":"Bahdanau D, Cho K, Bengio, Y (2015) Neural machine translation by jointly learning to align and translate. In: International conference on learning representations"},{"issue":"8","key":"7588_CR37","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"key":"7588_CR38","unstructured":"Gehring J, Auli M, Grangier D, Yarats D, Dauphin Y.N (2017) Convolutional sequence to sequence learning. In: International conference on machine learning, pp. 1243\u20131252 . PMLR"},{"key":"7588_CR39","unstructured":"Kalchbrenner N, Espeholt L, Simonyan K, Oord Avd, Graves A, Kavukcuoglu K (2016) Neural machine translation in linear time. arXiv preprint arXiv:1610.10099"},{"key":"7588_CR40","doi-asserted-by":"crossref","unstructured":"See A, Liu P.J, Manning C.D (2017) Get to the point: Summarization with pointer-generator networks. In: Proceedings of the 55th annual meeting of the association for computational linguistics, pp. 1073\u20131083","DOI":"10.18653\/v1\/P17-1099"},{"key":"7588_CR41","doi-asserted-by":"crossref","unstructured":"Gu J, Lu Z, Li H, Li VOK (2016) Incorporating copying mechanism in sequence-to-sequence learning. In: Proceedings of the 54th annual meeting of the association for computational linguistics, pp. 1631\u20131640","DOI":"10.18653\/v1\/P16-1154"},{"key":"7588_CR42","doi-asserted-by":"crossref","unstructured":"Zhang H, Lan Y, Pang L, Guo J, Cheng X (2019) Recosa: Detecting the relevant contexts with self-attention for multi-turn dialogue generation. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp. 3721\u20133730","DOI":"10.18653\/v1\/P19-1362"},{"key":"7588_CR43","doi-asserted-by":"crossref","unstructured":"Cai D, Wang Y, Bi W, Tu Z, Liu X, Shi S (2019) Retrieval-guided dialogue response generation via a matching-to-generation framework. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, pp. 1866\u20131875","DOI":"10.18653\/v1\/D19-1195"},{"key":"7588_CR44","unstructured":"Zhang Z, Li J, Zhu P, Zhao H, Liu G (2018) Modeling multi-turn conversation with deep utterance aggregation. In: Proceedings of the 27th international conference on computational linguistics: system demonstrations, pp. 3740\u20133752"},{"key":"7588_CR45","doi-asserted-by":"crossref","unstructured":"Tan M, Wang D, Gao Y, Wang H, Potdar S, Guo X, Chang S, Yu M (2019) Context-aware conversation thread detection in multi-party chat. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, pp. 6456\u20136461","DOI":"10.18653\/v1\/D19-1682"},{"key":"7588_CR46","doi-asserted-by":"crossref","unstructured":"Zhang R, Lee H, Polymenakos L, Radev D (2018) Addressee and response selection in multi-party conversations with speaker interaction rnns. In: Proceedings of the AAAI conference on artificial intelligence, pp. 5690\u20135697","DOI":"10.1609\/aaai.v32i1.11937"},{"key":"7588_CR47","doi-asserted-by":"crossref","unstructured":"Le R, Hu W, Shang M, You Z, Bing L, Zhao D, Yan R (2019) Who is speaking to whom? learning to identify utterance addressee in multi-party conversations. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, pp. 1909\u20131919","DOI":"10.18653\/v1\/D19-1199"},{"key":"7588_CR48","doi-asserted-by":"crossref","unstructured":"Liu S, Chen H, Ren Z, Feng Y, Liu Q, Yin D (2018) Knowledge diffusion for neural dialogue generation. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 1489\u20131498","DOI":"10.18653\/v1\/P18-1138"},{"key":"7588_CR49","doi-asserted-by":"crossref","unstructured":"Zhou H, Young T, Huang M, Zhao H, Xu J, Zhu X (2018) Commonsense knowledge aware conversation generation with graph attention. In: IJCAI International joint conference on artificial intelligence, pp. 4623\u20134629","DOI":"10.24963\/ijcai.2018\/643"},{"key":"7588_CR50","doi-asserted-by":"crossref","unstructured":"Meng C, Ren P, Chen Z, Monz C, Ma J, de Rijke M (2020) Refnet: A reference-aware network for background based conversation. In: Proceedings of the AAAI conference on artificial intelligence, pp. 8496\u20138503","DOI":"10.1609\/aaai.v34i05.6370"},{"key":"7588_CR51","doi-asserted-by":"crossref","unstructured":"Liu Z, Niu Z.-Y, Wu H, Wang H (2019) Knowledge aware conversation generation with explainable reasoning over augmented graphs. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, pp. 1782\u20131792","DOI":"10.18653\/v1\/D19-1187"},{"key":"7588_CR52","doi-asserted-by":"crossref","unstructured":"Zhong P, Wang D, Miao C (2019) Knowledge-enriched transformer for emotion detection in textual conversations. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, pp. 165\u2013176","DOI":"10.18653\/v1\/D19-1016"},{"key":"7588_CR53","unstructured":"Tang D, Duan N, Qin T, Yan Z, Zhou M (2017) Question answering and question generation as dual tasks"},{"key":"7588_CR54","doi-asserted-by":"crossref","unstructured":"Du X, Cardie C (2017) Identifying where to focus in reading comprehension for neural question generation. In: Proceedings of the 2017 conference on empirical methods in natural language processing, pp. 2067\u20132073","DOI":"10.18653\/v1\/D17-1219"},{"key":"7588_CR55","doi-asserted-by":"crossref","unstructured":"Wang T, Yuan X, Trischler A (2017) A joint model for question answering and question generation","DOI":"10.18653\/v1\/D17-1090"},{"key":"7588_CR56","doi-asserted-by":"crossref","unstructured":"Guo H, Pasunuru R, Bansal M (2018) Soft layer-specific multi-task summarization with entailment and question generation. In: Proceedings of the 56th annual meeting of the association for computational linguistics, pp. 687\u2013697","DOI":"10.18653\/v1\/P18-1064"},{"key":"7588_CR57","doi-asserted-by":"crossref","unstructured":"Elsahar H, Gravier C, Laforest F (2018) Zero-shot question generation from knowledge graphs for unseen predicates and entity types. In: Proceedings of the 2018 conference of the north american chapter of the association for computational linguistics: human language technologies","DOI":"10.18653\/v1\/N18-1020"},{"key":"7588_CR58","doi-asserted-by":"crossref","unstructured":"Zhou X, Li L, Dong D, Liu Y, Chen Y, Zhao W.X, Yu D, Wu H (2018) Multi-turn response selection for chatbots with deep attention matching network. In: Proceedings of the 56th annual meeting of the association for computational linguistics, pp. 1118\u20131127","DOI":"10.18653\/v1\/P18-1103"},{"key":"7588_CR59","unstructured":"Ba JL, Kiros JR, Hinton GE (2016) Layer normalization. stat 1050:21"},{"key":"7588_CR60","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep Learning"},{"key":"7588_CR61","doi-asserted-by":"crossref","unstructured":"Nallapati R, Zhou B, Gulcehre C, Xiang B, et al: (2016) Abstractive text summarization using sequence-to-sequence rnns and beyond. In: Proceedings of the 20th SIGNLL conference on computational natural language learning, pp. 280\u2013290","DOI":"10.18653\/v1\/K16-1028"},{"key":"7588_CR62","doi-asserted-by":"crossref","unstructured":"Serban I, Sordoni A, Bengio Y, Courville A, Pineau J (2016) Building end-to-end dialogue systems using generative hierarchical neural network models. In: Proceedings of the AAAI conference on artificial intelligence, pp. 3776\u20133783","DOI":"10.1609\/aaai.v30i1.9883"},{"key":"7588_CR63","doi-asserted-by":"crossref","unstructured":"Lin C.-Y, Hovy E (2003) Automatic evaluation of summaries using n-gram co-occurrence statistics. In: Proceedings of the 2003 conference of the north american chapter of the association for computational linguistics: human language technologies, pp. 150\u2013157","DOI":"10.3115\/1073445.1073465"},{"key":"7588_CR64","doi-asserted-by":"crossref","unstructured":"Papineni K, Roukos S, Ward T, Zhu W.-J (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the association for computational linguistics, pp. 311\u2013318","DOI":"10.3115\/1073083.1073135"},{"key":"7588_CR65","doi-asserted-by":"crossref","unstructured":"Ke P, Guan J, Huang M, Zhu X (2018) Generating informative responses with controlled sentence function. In: Proceedings of the 56th annual meeting of the association for computational linguistics, pp. 1499\u20131508","DOI":"10.18653\/v1\/P18-1139"},{"key":"7588_CR66","doi-asserted-by":"crossref","unstructured":"Zhu Q, Cui L, Zhang W.-N, Wei F, Liu T (2019) Retrieval-enhanced adversarial training for neural response generation. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp. 3763\u20133773","DOI":"10.18653\/v1\/P19-1366"},{"key":"7588_CR67","doi-asserted-by":"crossref","unstructured":"Pitoura E, Tsaparas P, Flouris G, Fundulaki I, Papadakos P, Abiteboul S, Weikum G (2018) On measuring bias in online information. ACM SIGMOD Record, 16\u201321","DOI":"10.1145\/3186549.3186553"},{"issue":"1","key":"7588_CR68","first-page":"4","volume":"2015","author":"K Mahoney","year":"2015","unstructured":"Mahoney K (2015) Judicial bias: the ongoing challenge. J Disput Res 2015(1):4","journal-title":"J Disput Res"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07588-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07588-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07588-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,15]],"date-time":"2023-02-15T01:05:09Z","timestamp":1676423109000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07588-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,17]]},"references-count":68,"journal-issue":{"issue":"19","published-print":{"date-parts":[[2022,10]]}},"alternative-id":["7588"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07588-5","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,17]]},"assertion":[{"value":"8 November 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 June 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 August 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":"All the authors declare they have no Conflict of interest\/Competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"All the authors have given their consent to publish the paper.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"All the authors have given their consent to participate.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"The content of the paper is written in accordance to common ethical standards in the scientific community.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}}]}}