Computer Science ›› 2020, Vol. 47 ›› Issue (1): 205-211.doi: 10.11896/jsjkx.181202269

• Artificial Intelligence • Previous Articles     Next Articles

Intention Detection in Spoken Language Based on Context Information

XU Yang,WANG Jian-cheng,LIU Qi-yuan,LI Shou-shan   

  1. (School of Computer Science & Technology,Soochow University,Suzhou,Jiangsu 215006,China)
  • Received:2018-12-05 Published:2020-01-19
  • About author:XU Yang,born in 1994,postgraduate,is member of China Computer Federation (CCF).His main research interests include natural language processing and Dialogue system;LI Shou-shan,born in 1980,professor,is member of China Computer Federation (CCF).His main research interests include natural language processing,dialogue system and emotion analysis.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (6133101,61375073).

Abstract: In recent years,with the development of artificial intelligence and the popularization of smart devices,human-computer intelligent dialogue technology has received extensive attention.Spoken language understanding is an important task dialogue system,and spoken language intention detection is a key technology in spoken language understanding.Due to complex language phenomena such as semantic missing,frame representation and intent conversion in multiple rounds of dialogue,the intent detection task for spoken language is very challenging.In order to solve the above problems,a gated mechanism based information sharing neural network method was proposed in this paper,which can take advantages of contextual information in multiple rounds of dialogue to improve detection performance.Specifically,first the current round text and context text initial representation are constructed in combination with the phonetic features to reduce the impact of speech recognition errors on semantic representation.Secondly,a semantic encoder based on hierarchical attention mechanism is used to obtain deep semantic representations of the current round and contextual text,including multi-level semantic information from word to sentence to multiple rounds of text.Finally,the gated mechaniam based information sharing neural network is constructed to use the context semantic information to help the intent detection of the current round of text.The experimental results show that the proposed method can effectively use context information to improve the detection of spoken language intentions,and achieves 88.1% accuracy and 88.0% F1 value in dataset of CCKS2018 shared task-2,which is significantly improved performance compared with the existing methods.

Key words: Spoken language understanding, Intent detection, Context information, Gated neural network

CLC Number: 

  • TP391
[1]WANG Y,REN F J,QUAN C Q.A Summary of Research on Dialogue Management Methods in Spoken Dialogue System[J].Computer Science,2015,42(6):1-7,27.
[2]CHEN H,LIU X,YIN D,et al.A survey on dialogue systems:Recent advances and new frontiers[J].ACM SIGKDD Explorations Newsletter,2017,19(2):25-35.
[3]HENDERSON M,THOMSON B,WILLIAMS J D.The second dialog state tracking challenge[C]∥Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse andDia-logue (SIGDIAL).ACL.Stroudsburg,PA.2014:263-272.
[4]ZONG C Q,WU H,HUANG T Y,et al.Analysis of Spoken Dia- log Corpus in Restricted Domain[C]∥Proceedings of the 5th National Conference on Computational Languages.1999:115-122.
[5]SONG H Y,ZHANG W N,LIU T.DQN based Policy Learning for Open Domain Multi-turn Dialogues[J].Journal of Chinese Information Processing,2018,32(7):99-108,136.
[6]SENEFF S.TINA:A natural language system for spoken language applications[J].Computational Iinguistics,1992,18(1):61-86.
[7]YAN P,ZHENG F,XU M.Robust parsing in spoken dialogue systems[C]∥Seventh European Conference on Speech Communication and Technology.Academic.Amsterdam.2001.
[8]HUANG Y F,ZHENG F,YAN P J,et al.The Design and Implementation of Campus Navigation System:Easy Nav[J].Journal of Chinese Information Processing,2001,15(4):36-41.
[9]DENG Y,XU B,HUANG T.Chinese spoken language understanding across domain[C]∥Sixth International Conference on Spoken Language Processing.IEEE,2000.
[10]MINKER W,BENNACEF S K,GAUVAIN J L.A stochastic case frame approach for natural language understanding[C]∥Fourth International Conference on Spoken Language Proces-sing.IEEE,1996.
[11]HAFFNER P,TUR G,WRIGHT J H.Optimizing SVMs for complex call classification[C]∥2003 IEEE International Conference on Acoustics,Speech,and Signal Processing(ICASSP’03).IEEE,2003.
[12]FREUND Y,SCHAPIRE R E.A decision-theoretic generalization of on-line learning and an application to boosting[J].Journal of computer and system sciences,1997,55(1):119-139.
[13]SARIKAYA R,HINTON G E,RAMABHADRAN B.Deep belief nets for natural language call-routing [C]∥Proceeding of the IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP).IEEE,2011:5680-5683.
[14]TUR G,DENG L,HAKKANI-TÜR D,et al.Towards deeper understanding:Deep convex networks for semantic utterance classification [C]∥Proceeding of the IEEE International Confe-rence on Acoustics,Speech and Signal Processing (ICASSP).IEEE,2012:5045-5048.
[15]HASHEMI H B,ASIAEE A,KRAFT R.Query intent detection using convolutional neural networks [C]∥International Confe-rence on Web Search and Data Mining,Workshop on Query Understanding.New York:ACM,2016.
[16]RAVURI S,STOICKE A.A comparative study of neural network models for lexical intent classification[C]∥2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU).IEEE,2015:368-374.
[17]LIU B,LANE I.Attention-based recurrent neural network mo- dels for joint intent detection and slot filling[J].arXiv:1609.01454.
[18]FIRDAUS M,BHATNAGAR S,EKBAL A,et al.Intent Detection for Spoken Language Understanding Using a Deep Ensemble Model[C]∥Pacific Rim International Conference on Artificial Intelligence.Cham:Springer,2018:629-642.
[19]BARAHONA L M R,GASIC M,MRKŠIC' N,et al.Exploiting Sentence and Context Representations in Deep Neural Models for Spoken Language Understanding[J].arXiv:1610.04120,2016.
[20]XIE Z,LING G.Dialogue Breakdown Detection using Hierarchical Bi-Directional LSTMs [C]∥Proceedings of the Dialog System Technology Challenges Workshop (DSTC6).Elsevier.Amsterdam.2017.
[21]BENGIO Y,DUCHARME R,VINCENT P,et al.A neural probabilistic language model[J].Journal of machine learning research,2003,3(2):1137-1155.
[22]MIKOLOV T,CHEN K,CORRADO G,et al.Efficient estimation of word representations in vector space[J].arXiv:1301.3781,2013.
[23]ZHANG X,ZHAO J,LECUN Y.Character-level convolutional networks for text classification [C]∥Advances in Neural Infor- mation Processing Systems.New York:Curran Associates,2015:649-657.
[24]ZHANG X,LECUN Y.Which Encoding is the Best for Text Classification in Chinese,English,Japanese and Korean?[J].arXiv:1708.02657,2017.
[25]SORDONI A,BENGIO Y,VAHABI H,et al.A hierarchical recurrent encoder-decoder for generative context-aware query suggestion [C]∥Proceedings of the 24th ACM International on Conference on Information and Knowledge Management.New York:ACM,2015:553-562.
[26]HOCHREITER S,SCHMIDHUBER J.Long short-term memory[J].Neural Computation,1997,9(8):1735-1780.
[27]BAHDANAU D,CHO K,BENGIO Y.Neural machine translation by jointly learning to align and translate[J].arXiv:1409.0473,2014.
[28]CARUNA R.Multitask learning:A knowledge based source of inductive bias[C]∥Machine Learning:Proceedings of the Tenth International Conference.New York:ACM,1993:41-48.
[29]LIU P,QIU X,HUANG X.Recurrent neural network for text classification with multi-task learning[J].arXiv:1605.05101,2016.
[30]SØGAARD A,GOLDBERG Y.Deep multi-task learning with low level tasks supervised at lower layers[C]∥Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics.ACL:Stroudsburg.2016:231-235.
[31]KINGMA D P,BA J.Adam:A method for stochastic optimization[J].arXiv:1412.6980,2014.
[32]WILLIAMS J D.Web-style ranking and SLU combination for dialog state tracking [C]∥Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL).Stroudsburg:ACL,2014:282-291.
[33]WANG Y,SHEN Y,JIN H.A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling[C]∥Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics Stroudsburg:ACP,2018:309-314.
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