融合多约束条件的意图和语义槽填充联合识别

doi:10.3778/j.issn.1673-9418.1909009

摘要/Abstract

摘要：

口语理解包含两个子任务——意图识别和语义槽填充，现有的联合建模方法虽然实现了模型参数的共享，并将意图识别的结果作用于语义槽填充，但是对于语义槽填充任务没有考虑到标签前后的依赖关系。采用双向长短时记忆网络（BLSTM），由BLSTM得到隐藏层状态后，对两任务分别加入注意力机制，通过语义槽门控机制将意图识别的结果作用于语义槽填充任务，并在语义槽填充任务中加入条件随机场（CRF）模型，该模型考虑了标签前后的依赖关系从而使得标注结果更为准确。实验数据选择航空信息领域的查询语句，得到的结果是意图识别的准确率达到93.20%，语义槽填充的F1 值达到99.28%，并在SMP中文人机对话技术评测数据集上验证模型的性能。实验结果证明该模型优于其他联合识别模型。

关键词: 联合建模, 意图识别, 语义槽填充, 注意力机制, slot-gated机制, 条件随机场（CRF）

Abstract:

Spoken language understanding (SLU) consists of two sub-tasks, which are intent detection and semantic slot filling. Although the existing joint modeling methods realize the sharing of model parameters and apply the result of intent detection to semantic slot filling, the dependency before and after annotation is not considered for semantic slot filling task. A joint recognition model based on bidirectional long- short term memory (BLSTM) is adopted. After the hidden layer state is obtained by BLSTM, the attention mechanism is added to the two tasks respectively, and the result of intent detection is applied to the semantic slot filling by slot-gated mechanism. Considering the dependency before and after annotation, conditional random field (CRF) model is added into the semantic slot filling task to make the annotation result more accurate. Experimental data select the query in the field of flight information, the accuracy of the intent detection is 93.20% and F1 score of semantic slot filling is 99.28%. The performance of the model is verified on the SMP Chinese human-machine dialogue technology evaluation dataset. The results prove that the method is superior to other joint recognition models.

Key words: joint modeling, intent detection, semantic slot filling, attention mechanism, slot-gated mechanism, conditional random field (CRF)

侯丽仙，李艳玲，林民，李成城. 融合多约束条件的意图和语义槽填充联合识别[J]. 计算机科学与探索, 2020, 14(9): 1545-1553.

HOU Lixian, LI Yanling, LIN Min, LI Chengcheng. Joint Recognition of Intent and Semantic Slot Filling CombiningMultiple Constraints[J]. Journal of Frontiers of Computer Science and Technology, 2020, 14(9): 1545-1553.

参考文献

[1] Xu K, Feng Y S, Zhao D Y, et al. Automatic understanding of natural language questions for querying Chinese knowledge bases[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2014, 50(1): 85-92.许坤, 冯岩松, 赵东岩, 等. 面向知识库的中文自然语言问句的语义理解[J]. 北京大学学报(自然科学版), 2014, 50(1): 85-92.
[2] Angelino E, Larus-Stone N, Alabi D, et al. Learning certi-fiably optimal rule lists for categorical data[J]. Journal of Machine Learning Research, 2017, 18(1): 8753-8830.
[3] Chen H S, Liu X R, Yin D W, et al. A survey on dialogue sys-tems: recent advances and new frontiers[J]. SIGKDD Explo-rations, 2017, 19(2): 25-35.
[4] Li Y L, Yan Y H. Research on execution strategy about stati-stical spoken language understanding[J]. Journal of Frontiers of Computer Science and Technology, 2017, 11(6): 980-987.李艳玲, 颜永红. 统计中文口语理解执行策略的研究[J]. 计算机科学与探索, 2017, 11(6): 980-987.
[5] Surendran D, Levow G A. Dialog act tagging with support vector machines and hidden Markov models[C]//Proceedings of the 9th International Conference on Spoken Language Processing, Pittsburgh, Sep 17-21, 2006: 1950-1953.
[6] Keize S, op den Akker R, Nijholt A. Dialogue act recognition with Bayesian networks for Dutch dialogues[C]//Proceedings of the 3rd Annual Meeting of the Special Interest Group on Discourse and Dialogue, Philadelphia, Jul 11-12, 2002. Strou-dsburg: ACL, 2002: 88-94.
[7] Ali S A, Sulaiman N, Mustapha A, et al. Improving accuracy of intention-based response classification using decision tree[J]. Information Technology Journal, 2009, 8(6): 923-928.
[8] Laffery J, McCallum A, Pereira F C N. Conditional random fields: probabilistic models for segmenting and labeling seq-uence data[C]//Proceedings of the 18th International Conference on Machine Learning.Williamstoun: ICML, 2001: 282-289.
[9] Zhou G D, Su J. Named entity recognition using an HMM-based chunk tagger[C]//Proceedings of the 40th Annual Meeting on ACL, 2002: 473-480.
[10] Borthwick A E. A maximum entropy approach to named entity recognition[D]. New York: New York University, 1999.
[11] Zhang Y C, Yang Y, Jiang R, et al. Commercial intelligence entity recognition model based on BiLSTM-CRF[J]. Computer Engineering, 2019, 45(5): 308-314.张应成, 杨洋, 蒋瑞, 等. 基于BiLSTM-CRF的商情实体识别模型[J]. 计算机工程, 2019, 45(5): 308-314.
[12] Zhang R B, Liu J Y, He X. Named entity recognition for vulnerabilities based on BLSTM-CRF model[J]. Journal of Sichuan University (Natural Science Edition), 2019, 56(3): 469-475.张若彬, 刘嘉勇, 何祥. 基于BLSTM-CRF模型的安全漏洞领域命名实体识别[J]. 四川大学学报(自然科学版), 2019, 56(3): 469-475.
[13] Wang L L, Wumaier A, Yibulayin T, et al. Uyghur named entity recognition based on deep neural network[J]. Journal of Chi-nese Information Processing, 2019, 33(3): 64-70.王路路, 艾山·吾买尔, 吐尔根·依布拉音, 等. 基于深度神经网络的维吾尔文命名实体识别研究[J]. 中文信息学报, 2019, 33(3): 64-70.
[14] Guo D, Tür G, Yih W, et al. Joint semantic utterance classi-fication and slot filling with recursive neural networks[C]//Proceedings of the 2014 IEEE Spoken Language Technology Workshop, South Lake Tahoe, Dec 7-10, 2014. Piscataway: IEEE, 2014: 554-559.
[15] Hakkani-Tür D, Tür G, ?elikyilmaz A, et al. Multi-domain joint semantic frame parsing using bi-directional RNN-LSTM[C]//Proceedings of the 17th Annual Conference of the Inter-national Speech Communication Association, San Francisco, Sep 8-12, 2016: 715-719.
[16] Hua B T, Yuan Z X, Xiao W M, et al. Joint slot filling and intent detection with BLSTM-CNN-CRF[J]. Computer Engi-neering and Applications, 2019, 55(9): 139-143.华冰涛, 袁志祥, 肖维民, 等. 基于BLSTM-CNN-CRF模型的槽填充与意图识别[J]. 计算机工程与应用, 2019, 55(9): 139-143.
[17] Jeong M, Lee G G. Jointly predicting dialog act and named entity for spoken language understanding[C]//Proceedings of the 2006 IEEE ACL Spoken Language Technology Workshop, Palm Beach, Dec 10-13, 2006. Piscataway: IEEE, 2006: 66-69.
[18] Xu P Y, Sarikaya R. Convolutional neural network based triangular CRF for joint intent detection and slot filling[C]//Proceedings of the 2013 IEEE Workshop on Automatic Speech Recognition and Understanding, Olomouc, Dec 8-12, 2013. Piscataway: IEEE, 2013: 78-83.
[19] Zhang X D, Wang H F. A joint model of intent determi-nation and slot filling for spoken language understanding[C]//Proceedings of the 25th International Joint Conference on Artificial Intelligence, New York, Jul 9-15, 2016. Menlo Park: AAAI, 2016: 2993-2999.
[20] Liu B, Lane I. Attention-based recurrent neural network models for joint intent detection and slot filling[C]//Proceedings of the 17th Annual Conference of the International Speech Com-munication Association, San Francisco, Sep 8-12, 2016: 685-689.
[21] Tür G, Hakkani-Tür D, Heck L P. What is left to be under-stood in ATIS?[C]//Proceedings of the 2010 IEEE Spoken Language Technology Workshop, Berkeley, Dec 12-15, 2010. Piscataway: IEEE, 2010: 19-24.
[22] Goo C W, Gao G, Hsu Y K, et al. Slot-gated modeling for joint slot filling and intent prediction[C]//Proceedings of the 2018 Conference of the North American Chapter of the ACL: Human Language Technologies, New Orleans, Jun 1-6, 2018. Stroudsburg: ACL, 2018: 753-757.
[23] Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space[J]. arXiv:1301.3781, 2013.
[24] Turian J P, Ratinov L A, Bengio Y. Word representations: a simple and general method for semi-supervised learning[C]// Proceedings of the 48th Annual Meeting of the ACL, Uppsala, Jul 11-16, 2010. Stroudsburg: ACL, 2010: 384-394.
[25] Lai S W, Liu K, He S Z, et al. How to generate a good word embedding[J]. IEEE Intelligent Systems, 2016, 31(6): 5-14.
[26] Gers F A, Schmidhuber J, Cummins F. Learning to forget: continual prediction with LSTM[J]. Neural Computation, 2000, 12(10): 2451-2471.
[27] Yao K S， Peng B L, Zhang Y, et al. Spoken language under-standing using long short-term memory neural networks[C]//Proceedings of the 2014 IEEE Spoken Language Tech-nology Workshop, South Lake Tahoe, Dec 7-10, 2014. Pis-cataway: IEEE, 2014: 189-194.
[28] Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate[J]. arXiv:1409.0473, 2014.
[29] Zhang C. Research on text classification technology based on attention-based LSTM model[D]. Nanjing: Nanjing Uni-versity, 2016.张冲. 基于Attention-Based LSTM模型的文本分类技术的研究[D]. 南京: 南京大学, 2016.
[30] Devlin J, Chang M W, Lee K, et al. BERT: pre-training of deep bidirectional transformers for language understanding[J]. arXiv:1810.04805, 2018.