{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T15:56:22Z","timestamp":1781106982876,"version":"3.54.1"},"reference-count":23,"publisher":"IGI Global Scientific Publishing","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,10]]},"abstract":"<jats:p>Intent determination (ID) and slot filling (SF) are two critical steps in the spoken language understanding (SLU) task. Conventionally, most previous work has been done for each subtask respectively. To exploit the dependencies between intent label and slot sequence, as well as deal with both tasks simultaneously, this paper proposes a joint model (ABLCJ), which is trained by a united loss function. In order to utilize both past and future input features efficiently, a joint model based Bi-LSTM with contextual information is employed to learn the representation of each step, which are shared by two tasks and the model. This paper also uses sentence-level tag information learned from a CRF layer to predict the tag of each slot. Meanwhile, a submodule-based attention is employed to capture global features of a sentence for intent classification. The experimental results demonstrate that ABLCJ achieves competitive performance in the Shared Task 4 of NLPCC 2018.<\/jats:p>","DOI":"10.4018\/ijdcf.2020100103","type":"journal-article","created":{"date-parts":[[2020,9,10]],"date-time":"2020-09-10T09:15:52Z","timestamp":1599729352000},"page":"32-43","source":"Crossref","is-referenced-by-count":0,"title":["Joint Model-Based Attention for Spoken Language Understanding Task"],"prefix":"10.4018","volume":"12","author":[{"given":"Xin","family":"Liu","sequence":"first","affiliation":[{"name":"Dalian University of Foreign Languages, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"RuiHua","family":"Qi","sequence":"additional","affiliation":[{"name":"Dalian University of Foreign Languages, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Shao","sequence":"additional","affiliation":[{"name":"Dalian University of Foreign Languages, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJDCF.2020100103-0","doi-asserted-by":"crossref","unstructured":"Chelba, C., Mahajan, M., & Acero, A. (2003). Speech utterance classification. Paper presented at the ICASSP Conference, Hong Kong, China.","DOI":"10.1109\/ICASSP.2003.1198772"},{"issue":"Aug","key":"IJDCF.2020100103-1","first-page":"1871","article-title":"LIBLINEAR: A library for large linear classification.","volume":"9","author":"R. E.Fan","year":"2008","journal-title":"Journal of Machine Learning Research"},{"key":"IJDCF.2020100103-2","doi-asserted-by":"publisher","DOI":"10.1080\/02763869.2018.1404391"},{"key":"IJDCF.2020100103-3","unstructured":"Huang, Z., Xu, W., & Yu, K. (2015). Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991"},{"key":"IJDCF.2020100103-4","unstructured":"Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. Paper presented at the ICLR Conference, Banff, Canada."},{"key":"IJDCF.2020100103-5","first-page":"282","article-title":"Conditional random fields: Probabilistic models for segmenting and labeling sequence data.","author":"J.Lafferty","year":"2001","journal-title":"Proceedings of the Eighteenth International Conference on Machine Learning"},{"key":"IJDCF.2020100103-6","first-page":"260","article-title":"Neural architectures for named entity recognition.","author":"G.Lample","year":"2016","journal-title":"Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies"},{"key":"IJDCF.2020100103-7","doi-asserted-by":"crossref","unstructured":"Lin, B. Y., Xu, F., Luo, Z., & Zhu, K. (2017). Multi-channel bilstm-crf model for emerging named entity recognition in social media. In Proceedings of the 3rd Workshop on Noisy, User-generated Text (W-NUT) at EMNLP (pp. 160-165). Copenhagen, Denmark: Academic Press.","DOI":"10.18653\/v1\/W17-4421"},{"key":"IJDCF.2020100103-8","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-4012"},{"key":"IJDCF.2020100103-9","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2016-1352"},{"issue":"3","key":"IJDCF.2020100103-10","first-page":"530","article-title":"Using recurrent neural networks for slot filling in spoken language understanding. IEEE\/ACM Transactions on Audio","volume":"23","author":"G.Mesnil","year":"2015","journal-title":"Speech, and Language Processing"},{"key":"IJDCF.2020100103-11","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.1992.225939"},{"key":"IJDCF.2020100103-12","doi-asserted-by":"crossref","unstructured":"Ravuri, S., & Stolcke, A. (2015). Recurrent neural network and lstm models for lexical utterance classification. Paper presented at Annual Conference of the International Speech Communication Association, Dresden, Germany.","DOI":"10.21437\/Interspeech.2015-42"},{"key":"IJDCF.2020100103-13","doi-asserted-by":"crossref","unstructured":"Raymond, C., & Riccardi, G. (2007). Generative and discriminative algorithms for spoken language understanding. Paper presented at Annual Conference of the International Speech Communication Association, Antwerp, Belgium.","DOI":"10.21437\/Interspeech.2007-448"},{"key":"IJDCF.2020100103-14","unstructured":"Reimers, N., & Gurevych, I. (2017). Optimal hyperparameters for deep lstm-networks for sequence labeling tasks. Paper presented at the EMNLP Conference, Copenhagen, Denmark."},{"key":"IJDCF.2020100103-15","first-page":"956","article-title":"Reinforcement learning for spoken dialogue systems.","author":"S. P.Singh","year":"2000","journal-title":"Advances in Neural Information Processing Systems"},{"key":"IJDCF.2020100103-16","unstructured":"Sun, J. Y. (2014). Jieba Chinese text segmentation. Retrieved from https:\/\/github.com\/fxsjy\/ jieba."},{"key":"IJDCF.2020100103-17","doi-asserted-by":"publisher","DOI":"10.1002\/9781119992691"},{"key":"IJDCF.2020100103-18","first-page":"5998","article-title":"Attention is all you need.","author":"A.Vaswani","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"IJDCF.2020100103-19","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2016.7472841"},{"key":"IJDCF.2020100103-20","doi-asserted-by":"crossref","unstructured":"Wen, L., Wang, X., Dong, Z., & Chen, H. (2017). Jointly Modeling Intent detection and Slot Filling with Contextual and Hierarchical Information. Paper presented at National CCF Conference on Natural Language Processing and Chinese Computing, Dalian, China.","DOI":"10.1007\/978-3-319-73618-1_1"},{"key":"IJDCF.2020100103-21","unstructured":"Zaremba, W., Sutskever, I., & Vinyals, O. (2014). Recurrent neural network regularization. arXiv preprint arXiv:1409.2329"},{"key":"IJDCF.2020100103-22","unstructured":"Zhang, X., & Wang, H. (2016). A Joint Model of Intent Determination and Slot Filling for Spoken Language Understanding. Paper presented at the IJCAI Conference, Macao, China."}],"container-title":["International Journal of Digital Crime and Forensics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=262154","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,5]],"date-time":"2022-05-05T17:56:21Z","timestamp":1651773381000},"score":1,"resource":{"primary":{"URL":"http:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/IJDCF.2020100103"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2020,10]]},"references-count":23,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.4018\/ijdcf.2020100103","relation":{},"ISSN":["1941-6210","1941-6229"],"issn-type":[{"value":"1941-6210","type":"print"},{"value":"1941-6229","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10]]}}}