Research on Joint Modeling of Intent Detection and Slot Filling

[1] Research on how to understand natural language in task dialogues based on deep learning [D] Xi'an University of Electronic Science and Technology 2021, DOI: 10.27389/d.cnki.gxadu.2021.003266. Research on how to understand natural language in task dialogues based on deep learning [D] Xi'an University of Electronic Science and Technology 2021 10.27389/d.cnki.gxadu.2021.003266 Open DOISearch in Google Scholar

[2] Guo Suchao, Hao Xia, Yao Xiaobo, and Li Lin. Study on Quiz intention recognition and slot filling joint model of agricultural pest knowledge [J]. Journal of Agricultural Machinery, 2023, 54 (01): 205–215 GuoSuchao HaoXia YaoXiaobo LiLin Study on Quiz intention recognition and slot filling joint model of agricultural pest knowledge [J] Journal of Agricultural Machinery 2023 54 01 205 215 Search in Google Scholar

[3] Zhang J, Bui T, Yoon S, et al. Few Shot Intent Detection via Contrastive Pre Training and Fine Tuning [J]. 2021. ZhangJ BuiT YoonS Few Shot Intent Detection via Contrastive Pre Training and Fine Tuning [J] 2021 Search in Google Scholar

[4] Goo CW, Gao G, Hsu YK, et al. Slot strobe modeling is used for joint time slot filling and intention prediction. In: Proc of Conf., North American Chapter of Computational Linguistics Association.: Human Language Technology, Vol. 2. 2018. 753 – 757. GooCW GaoG HsuYK Slot strobe modeling is used for joint time slot filling and intention prediction In: Proc of Conf., North American Chapter of Computational Linguistics Association.: Human Language Technology 2 2018 753 757 Search in Google Scholar

[5] Haihong E, Beiqing Niu, Zhong Fuchen, and Meinasong, “a novel two-way interconnection model is used for joint intention detection and slotting,” in ACL Proc., 2019. HaihongE BeiqingNiu ZhongFuchen Meinasong “a novel two-way interconnection model is used for joint intention detection and slotting,” in ACL Proc. 2019 Search in Google Scholar

[6] Liu Yijin, fan Dongmeng, Zhang Jinchao, Zhou Jie, Chen Yufeng, and Jinan Xu, “CM net: novel collaborative memory network for oral understanding,” in Proc., 2019 of emnlp. LiuYijin fanDongmeng ZhangJinchao ZhouJie ChenYufeng JinanXu “CM net: novel collaborative memory network for oral understanding,” in Proc., 2019 of emnlp Search in Google Scholar

[7] Teng, Qin, Automobile, ecc. Improve your understanding of spoken Chinese through the [C]//International Conference on Voice and Signal Processing IEEE, 2020. QinTeng Automobile, ecc. Improve your understanding of spoken Chinese through the [C]// International Conference on Voice and Signal Processing IEEE 2020 Search in Google Scholar

[8] Qin l, Liu T, cut W, etc. Interactive Codec for Joint Slot Filling and Intent Detection [C]//International Conference on Acoustics, Sound, and Signal Processing IEEE, 2021. Qinl LiuT cutW Interactive Codec for Joint Slot Filling and Intent Detection [C]// International Conference on Acoustics, Sound, and Signal Processing IEEE 2021 Search in Google Scholar

[9] Zhang, H. Wang. A Joint Model for Identifying Understanding Oral Intention and Filling Gaps. Extracted from: The Process of the International Federation of Artificial Intelligence 25th edition 2016.2993–2999 ZhangH. Wang A Joint Model for Identifying Understanding Oral Intention and Filling Gaps Extracted from: The Process of the International Federation of Artificial Intelligence 25th edition 2016 2993 2999 Search in Google Scholar

[10] Haffner P, Tur G, Wright JH. Optimize SVM for complex call classification. In: Process International Acoustics Conference and Signal Processing. IEEE (ICASSP 2003) Volume 1 IEEE, 2003.632–635 HaffnerP TurG WrightJH Optimize SVM for complex call classification In: Process International Acoustics Conference and Signal Processing. IEEE (ICASSP 2003) Volume 1 IEEE 2003 632 635 Search in Google Scholar

[11] Shapire RE, Singer Y. BoosTexter: A Text Sorting System Based on Boosting. Machine Learning, 2000, 39 (2): 135–168. ShapireRE SingerY BoosTexter: A Text Sorting System Based on Boosting Machine Learning 2000 39 2 135 168 Search in Google Scholar

[12] Wei P.F., Zeng B., Wang M.H., and Zeng A. Review of Speech Understanding Joint Modeling Algorithms Based on Deep Learning [J]Software Magazine, 2022, 33 (11): 41924216.DOI:10.13328/j.cnki.jos.006385. WeiP.F. ZengB. WangM.H. ZengA. Review of Speech Understanding Joint Modeling Algorithms Based on Deep Learning [J] Software Magazine 2022 33 11 41924216 10.13328/j.cnki.jos.006385 Open DOISearch in Google Scholar

[13] Yu Bengang, mladší bratr Fan Zhao Review of Natural language processing conditions and airport model research [J]Journal of information resource management, 2020, 10 (05): 96111. doi:10.13365/j.jirm.2020.05.096. YuBengang mladší bratr Fan Zhao Review of Natural language processing conditions and airport model research [J] Journal of information resource management 2020 10 05 96111 10.13365/j.jirm.2020.05.096 Open DOISearch in Google Scholar

[14] Xu P, Sarikaya R. Triangle CRF based on convolutional neural network is used for joint intention detection and slot filling [C] / / / automatic speech recognition and understanding (asru), IEEE workshop in 2013. IEEE, 2013.DOI:10.1109/ASRU.2013.6707709. Neter J R, Guzide O. Deep learning in natural language processing [J]. 2018(1). XuP SarikayaR Triangle CRF based on convolutional neural network is used for joint intention detection and slot filling [C] / / / automatic speech recognition and understanding (asru), IEEE workshop in 2013 IEEE 2013 10.1109/ASRU.2013.6707709 Neter J R, Guzide O. Deep learning in natural language processing [J]. 2018(1). Open DOISearch in Google Scholar

[15] Ravuri S V, Stolcke A. Repetitive Neural Networks and LSTM Models for Word Pronunciation Classification [C]//2015DOI:10.21437/Interlingua2015 42. RavuriS V StolckeA Repetitive Neural Networks and LSTM Models for Word Pronunciation Classification [C]// 2015 DOI:10.21437/Interlingua2015 42. Search in Google Scholar

[16] Nij, Young T, pandelea V et coll. Research progress in dialogue systems based on deep learning [J]2021. Nij YoungT pandeleaV Research progress in dialogue systems based on deep learning [J] 2021 Search in Google Scholar

[17] Yu, Xie En, JinRNN semantic frame analysis model, using dual models for intention detection and time slot filling [J]2018. YuXie En JinRNN semantic frame analysis model, using dual models for intention detection and time slot filling [J] 2018 Search in Google Scholar

[18] Zhang C, Li Yi, doon et al. Filling and intention testing combination based on neural capsule network [C]//annual paper collection 57 Society for Computational linguistics 2019. ZhangC LiYi doon Filling and intention testing combination based on neural capsule network [C]// annual paper collection 57 Society for Computational linguistics 2019 Search in Google Scholar