{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T17:54:42Z","timestamp":1764784482565,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030583224"},{"type":"electronic","value":"9783030583231"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-58323-1_57","type":"book-chapter","created":{"date-parts":[[2020,9,3]],"date-time":"2020-09-03T13:48:44Z","timestamp":1599140924000},"page":"532-540","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Graph Convolutional Networks for Student Answers Assessment"],"prefix":"10.1007","author":[{"given":"Nisrine","family":"Ait Khayi","sequence":"first","affiliation":[]},{"given":"Vasile","family":"Rus","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,1]]},"reference":[{"key":"57_CR1","unstructured":"Ait Khayi, N., Rus, V.: Bi-GRU Capsnet for student answers assessment. In: The 2019 KDD Workshop on Deep Learning for Education (DL4Ed) in Conjunction With the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019), Anchorage, Alaska, USA (2019)"},{"key":"57_CR2","doi-asserted-by":"crossref","unstructured":"Ait Khayi, N., Rus, V.: Attention based transformer for student answers assessment. In: The Flairs-33rd International Conference (2020)","DOI":"10.1007\/978-3-030-58323-1_57"},{"key":"57_CR3","doi-asserted-by":"crossref","unstructured":"Banjade, R., Maharjan, N., Niraula, N.B., Gautam, D., Samei, B., Rus, V.: Evaluation dataset (DT-Grade) and word weighting approach towards constructed short answers assessment in tutorial dialogue context. In: The 11th Workshop on Innovative Use of NLP for Building Educational Applications, pp. 182\u2013187 (2016)","DOI":"10.18653\/v1\/W16-0520"},{"key":"57_CR4","unstructured":"Battaglia, P.W., et al: Relational inductive biases, deep learning, and graph network. arXiv preprint arXiv:1806.01261 (2018)"},{"key":"57_CR5","unstructured":"Bianchi, F.M., Grattarola, D., Alippi, C., Livi, L.: Graph neural networks with convolutional ARMA filters. arXiv preprint arXiv:1901.01343 (2019)"},{"issue":"9","key":"57_CR6","doi-asserted-by":"publisher","first-page":"1616","DOI":"10.1109\/TKDE.2018.2807452","volume":"30","author":"H Cai","year":"2018","unstructured":"Cai, H., Zheng, V.W., Chang, K.: A comprehensive survey of graph embedding problems, techniques and applications. IEEE Trans. Knowl. Data Eng. 30(9), 1616\u20131637 (2018)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"57_CR7","unstructured":"Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in Neural Information Processing Systems, pp. 3844\u20133852 (2016)"},{"key":"57_CR8","unstructured":"Duvenaud, D., et al.: Convolutional networks on graphs for learning molecular fingerprints. In: NIPS (2015)"},{"issue":"6","key":"57_CR9","first-page":"127","volume":"8","author":"T Gong","year":"2019","unstructured":"Gong, T., Yao, X.: An attention-based deep model for automatic short answer score. Int. J. Comput. Sci. Softw. Eng. 8(6), 127\u2013132 (2019)","journal-title":"Int. J. Comput. Sci. Softw. Eng."},{"key":"57_CR10","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"57_CR11","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/978-3-319-93846-2_35","volume-title":"Artificial Intelligence in Education","author":"N Maharjan","year":"2018","unstructured":"Maharjan, N., Gautam, D., Rus, V.: Assessing free student answers in tutorial dialogues using LSTM models. In: Penstein Ros\u00e9, C., Mart\u00ednez-Maldonado, R., Hoppe, H.U., Luckin, R., Mavrikis, M., Porayska-Pomsta, K., McLaren, B., du Boulay, B. (eds.) AIED 2018. LNCS (LNAI), vol. 10948, pp. 193\u2013198. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-93846-2_35"},{"key":"57_CR12","doi-asserted-by":"crossref","unstructured":"Marcheggiani, D., Titov, I.: Encoding sentences with graph convolutional networks for semantic role labeling. In: EMNLP (2017)","DOI":"10.18653\/v1\/D17-1159"},{"issue":"3","key":"57_CR13","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1609\/aimag.v34i3.2485","volume":"34","author":"V Rus","year":"2013","unstructured":"Rus, V., D\u2019Mello, S.K., Hu, X., Graesser, A.C.: Recent advances in intelligent tutoring systems with conversational dialogue. AI Mag. 34(3), 42\u201354 (2013)","journal-title":"AI Mag."},{"key":"57_CR14","doi-asserted-by":"crossref","unstructured":"Sahu, S.K., Christopoulou, F., Miwa, M., Ananiadou, S.: Inter-sentence relation extraction with document-level graph convolutional neural network. In: ACL (2019)","DOI":"10.18653\/v1\/P19-1423"},{"key":"57_CR15","unstructured":"Kipf, T., Welling, M.: Semi supervised classification with graph convolutional networks. In: ICLR (2017)"},{"key":"57_CR16","doi-asserted-by":"crossref","unstructured":"Yao, L., Mao, C., Luo, Y.: Graph convolutional networks for text classification. In: The AAAI Conference on Artificial Intelligence, vol. 33, pp. 7370\u20137377 (2019)","DOI":"10.1609\/aaai.v33i01.33017370"},{"key":"57_CR17","doi-asserted-by":"crossref","unstructured":"Zhang, N., et al.: Long-tail relation extraction via knowledge graph embeddings and graph convolution networks. In: NAACL-HLT (2019)","DOI":"10.18653\/v1\/N19-1306"}],"container-title":["Lecture Notes in Computer Science","Text, Speech, and Dialogue"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58323-1_57","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T15:49:29Z","timestamp":1709826569000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58323-1_57"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030583224","9783030583231"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58323-1_57","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"1 September 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"TSD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Text, Speech, and Dialogue","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brno","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Czech Republic","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"tsd2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.tsdconference.org\/tsd2020\/","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":"In-house","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"110","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":"54","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":"49% - 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":"3","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":"The conference was held virtually due to the COVID-19 pandemic.","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)"}}]}}