{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T15:32:44Z","timestamp":1781019164976,"version":"3.54.1"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783319937939","type":"print"},{"value":"9783319937946","type":"electronic"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-319-93794-6_3","type":"book-chapter","created":{"date-parts":[[2018,6,29]],"date-time":"2018-06-29T16:05:05Z","timestamp":1530288305000},"page":"35-48","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Textual Entailment in Legal Bar Exam Question Answering Using Deep Siamese Networks"],"prefix":"10.1007","author":[{"given":"Mi-Young","family":"Kim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yao","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Randy","family":"Goebel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2018,6,30]]},"reference":[{"key":"3_CR1","unstructured":"Kim, M.-Y., Kang, S.-J., Lee, J.-H.: Resolving ambiguity in inter-chunk dependency parsing. In: Proceedings of 6th Natural Language Processing Pacific Rim Symposium, pp. 263\u2013270 (2001)"},{"issue":"1","key":"3_CR2","first-page":"51","volume":"4","author":"WN Bdour","year":"2013","unstructured":"Bdour, W.N., Gharaibeh, N.K.: Development of yes\/no arabic question answering system. Int. J. Artif. Intell. Appl. 4(1), 51\u201363 (2013)","journal-title":"Int. J. Artif. Intell. Appl."},{"key":"3_CR3","unstructured":"Nielsen, R.D., Ward, W., Martin, J.H.: Toward dependency path based entailment. In: Proceedings of the Second PASCAL Challenges Workshop on RTE (2006)"},{"key":"3_CR4","unstructured":"Yu, L., Hermann, K.M., Blunsom, P., Pulman, S.: Deep learning for answer sentence selection. arXiv preprint arXiv:1412.1632 (2014)"},{"key":"3_CR5","doi-asserted-by":"crossref","unstructured":"Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences. In: Proceedings of ACL (2014)","DOI":"10.3115\/v1\/P14-1062"},{"key":"3_CR6","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1007\/978-3-319-61572-1_20","volume-title":"New Frontiers in Artificial Intelligence","author":"M-Y Kim","year":"2017","unstructured":"Kim, M.-Y., Xu, Y., Lu, Y., Goebel, R.: Question answering of bar exams by paraphrasing and legal text analysis. In: Kurahashi, S., Ohta, Y., Arai, S., Satoh, K., Bekki, D. (eds.) JSAI-isAI 2016. LNCS (LNAI), vol. 10247, pp. 299\u2013313. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-61572-1_20"},{"key":"3_CR7","doi-asserted-by":"crossref","unstructured":"Yih, W., He, X., Meek, C.: Semantic parsing for single-relation question answering. In: Proceedings of ACL (2014)","DOI":"10.3115\/v1\/P14-2105"},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"Dahl, G.E., Sainath, T.N., Hinton, G.E.: Improving deep neural networks for LVCSR using rectified linear units and dropout. In: Proceedings of Acoustics, Speech and Signal Processing (ICASSP), pp. 8609\u20138613 (2013)","DOI":"10.1109\/ICASSP.2013.6639346"},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"Deng, L., Abdel-Hamid, O., Yu, D.: A deep convolutional neural network using heterogeneous pooling for trading acoustic invariance with phonetic confusion. In: Proceedings of Acoustics, Speech and Signal Processing (ICASSP), pp. 6669\u20136673. IEEE (2013)","DOI":"10.1109\/ICASSP.2013.6638952"},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"Bordes, A., Chopra, S., Weston, J.: Question answering with subgraph embeddings. In: Proceedings of EMNLP (2014)","DOI":"10.3115\/v1\/D14-1067"},{"key":"3_CR11","unstructured":"Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012)"},{"key":"3_CR12","doi-asserted-by":"crossref","unstructured":"Kouylekov, M., Magnini, B.: Tree edit distance for recognizing textual entailment: estimating the cost of insertion. In: Proceedings of the Second PASCAL Challenges Workshop on RTE (2006)","DOI":"10.1007\/11736790_12"},{"key":"3_CR13","doi-asserted-by":"crossref","unstructured":"Goyal, N.: LearningToQuestion at SemEval 2017 task 3: ranking similar questions by learning to rank using rich features. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pp. 310\u2013314 (2017)","DOI":"10.18653\/v1\/S17-2050"},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Mueller, J., Thyagarajan, A.: Siamese recurrent architectures for learning sentence similarity. In: AAAI, pp. 2786\u20132792, February 2016","DOI":"10.1609\/aaai.v30i1.10350"},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"Das, A., Yenala, H., Chinnakotla, M., Shrivastava, M.: Together we stand: siamese networks for similar question retrieval. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 378\u2013387 (2016)","DOI":"10.18653\/v1\/P16-1036"},{"key":"3_CR16","doi-asserted-by":"crossref","unstructured":"Bromley, J., Guyon, I., LeCun, Y., S\u00e4ckinger, E., Shah, R.: Signature verification using a \u201csiamese\u201d time delay neural network. In: Advances in Neural Information Processing Systems, pp. 737\u2013744 (1994)","DOI":"10.1142\/9789812797926_0003"},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: IEEE Computer Vision and Pattern Recognition, pp. 539\u2013546 (2005)","DOI":"10.1109\/CVPR.2005.202"},{"key":"3_CR18","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1007\/978-3-319-50953-2_20","volume-title":"New Frontiers in Artificial Intelligence","author":"M-Y Kim","year":"2015","unstructured":"Kim, M.-Y., Xu, Y., Goebel, R.: Applying a convolutional neural network to legal question answering. In: Otake, M., Kurahashi, S., Ota, Y., Satoh, K., Bekki, D. (eds.) JSAI-isAI 2016, pp. 282\u2013294. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-50953-2_20"},{"key":"3_CR19","doi-asserted-by":"crossref","unstructured":"Lyu, C., Lu, Y., Ji, D., Chen, B.: Deep learning for textual entailment recognition. In: IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 154\u2013161 (2015)","DOI":"10.1109\/ICTAI.2015.35"},{"key":"3_CR20","doi-asserted-by":"crossref","unstructured":"Neculoiu, P., Maarten, V., Mihai, R.: Learning text similarity with siamese recurrent networks. In: Proceedings of the 1st Workshop on Representation Learning for NLP, pp. 148\u2013157 (2016)","DOI":"10.18653\/v1\/W16-1617"},{"key":"3_CR21","doi-asserted-by":"crossref","unstructured":"Bowman, S.R., Angeli, G., Potts, C., Manning, C.D.: A large annotated corpus for learning natural language inference. arXiv preprint arXiv:1508.05326 (2015)","DOI":"10.18653\/v1\/D15-1075"},{"key":"3_CR22","unstructured":"Rocktaschel, T., Grefenstette, E., Hermann, K.M., Kocisky, T., Blunsom, P.: Reasoning about entailment with neural attention. arXiv preprint arXiv:1509.06664 (2015)"},{"key":"3_CR23","doi-asserted-by":"crossref","unstructured":"Yin, W., Schutze, H., Xiang, B., Zhou, B.: ABCNN: attention-based convolutional neural network for modeling sentence pairs. arXiv preprint arXiv:1512.05193 (2015)","DOI":"10.1162\/tacl_a_00244"},{"key":"3_CR24","doi-asserted-by":"crossref","unstructured":"Liu, P., Qiu, X., Huang, X.: Modelling interaction of sentence pair with coupled-LSTMs. arXiv preprint arXiv:1605.05573 (2016)","DOI":"10.18653\/v1\/D16-1176"},{"key":"3_CR25","unstructured":"Vendrov, I., Kiros, R., Fidler, S., Urtasun, R.: Order-embeddings of images and language. arXiv preprint arXiv:1511.06361 (2015)"},{"key":"3_CR26","doi-asserted-by":"crossref","unstructured":"Mou, L., Men, R., Li, G., Xu, Y., Zhang, L., Yan, R., Jin, Z.: Natural language inference by tree-based convolution and heuristic matching. In: Proceedings of the Conference on Association for Computational Linguistics (2016)","DOI":"10.18653\/v1\/P16-2022"},{"key":"3_CR27","doi-asserted-by":"crossref","unstructured":"Wang, S., Jiang, J.: Learning natural language inference with LSTM. arXiv preprint arXiv:1512.08849 (2015)","DOI":"10.18653\/v1\/N16-1170"},{"key":"3_CR28","unstructured":"Liu, Y., Sun, C., Lin, L., Wang, X.: Learning natural language inference using bidirectional LSTM model and inner-attention. arXiv preprint arXiv:1605.09090 (2016)"},{"key":"3_CR29","doi-asserted-by":"crossref","unstructured":"Cheng, J., Dong, L., Lapata, M.: Long short-term memory-networks for machine reading. arXiv preprint arXiv:1601.06733 (2016)","DOI":"10.18653\/v1\/D16-1053"},{"key":"3_CR30","doi-asserted-by":"crossref","unstructured":"Bowman, S.R., Gauthier, J., Rastogi, A., Gupta, R., Manning, C.D., Potts, C.: A fast unified model for parsing and sentence understanding. arXiv preprint arXiv:1603.06021 (2016)","DOI":"10.18653\/v1\/P16-1139"},{"key":"3_CR31","doi-asserted-by":"crossref","unstructured":"Parikh, A.P., Tackstrom, 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":"3_CR32","unstructured":"Sha, L., Chang, B., Sui, Z., Li, S.: Reading and thinking: re-read LSTM unit for textual entailment recognition. In: Proceedings of COLING: Technical Papers, pp. 2870\u20132879 (2016)"}],"container-title":["Lecture Notes in Computer Science","New Frontiers in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-93794-6_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,5]],"date-time":"2025-07-05T12:50:01Z","timestamp":1751719801000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-93794-6_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783319937939","9783319937946"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-93794-6_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"30 June 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"JSAI-isAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"JSAI International Symposium on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tsukuba","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2017","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 November 2017","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 November 2017","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"jsai2017","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.ai-gakkai.or.jp\/isai\/archives\/522","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":"109","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":"30","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":"0","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":"28% - 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":"2","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":"1","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)"}}]}}