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Joint intent detection and slot filling using weighted finite state transducer and BERT

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Abstract

Intent detection and slot filling are the two most essential tasks of natural language understanding (NLU). Deep neural models have produced impressive results on these tasks. However, the predictive accuracy of these models heavily depends upon a massive amount of supervised data. In many applications collecting high-quality labeled data is a very expensive and time taking process. This paper proposes WFST-BERT model which augments the fine-tuning of BERT-like architecture with weighted finite-state transducer (WFST) to reduce the need for massive supervised data. The WFST-BERT employs regular expressions (REs) rules to encode domain knowledge and pre-trained BERT model to generate contextual representations of user sentences. In particular, the model converts REs into the trainable weighted finite-state transducer, which can generate decent predictions when limited or no training examples are available. Moreover, BERT contextual representation is combined with WFST and trained simultaneously on supervised data using a gradient descent algorithm. The experimental results on the ATIS dataset show that the F1-Score of the WFST-BERT improved by around 1.8% and 1.3% for intent detection and 0.9%, 0.7% for slot filling tasks as compared to its counterparts RE-NN and JointBERT models in limited data settings. Further, in full data settings, the proposed model generates better recall and F1-score than state-of-the-art models.

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Notes

  1. https://storage.googleapis.com/bert_models/2020_02_20/uncased_L-12_H-768_A-12.zip

  2. https://github.com/HadoopIt/rnn-nlu

  3. https://github.com/MiuLab/SlotGated-SLU

  4. https://github.com/czhang99/Capsule-NLU

  5. https://github.com/LeePleased/StackPropagation-SLU

  6. https://github.com/jeffchy/RE2RNN

References

  1. Tur G, De Mori R (2011) Spoken language understanding: Systems for extracting semantic information from speech. Wiley

  2. Liu B, Lane I (2016) Attention-based recurrent neural network models for joint intent detection and slot filling. In: Proceedings of the International Speech Communication Association (INTERSPEECH 2016), pp 685–689

  3. Goo C-W, Gao G, Hsu Y-K, Huo C-L, Chen T-C, Hsu K-W, Chen Y-N (2018) Slot-gated modeling for joint slot filling and intent prediction. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pp 753–757

  4. E H, Niu P, Chen Z, Song M (2019) A novel bi-directional interrelated model for joint intent detection and slot filling. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp 5467–5471

  5. Obuchowski A, Lew M (2020) Transformer-capsule model for intent detection. In: Proceedings of the AAAI conference on artificial intelligence, pp 13885–13886

  6. Casanueva I , Temčinas T, Gerz D, Henderson M, Vulić I (2020) Efficient intent detection with dual sentence encoders. In: Proceedings of the 2nd workshop on natural language processing for conversational AI, pp 38–45

  7. Wen T-H, Vandyke D, Mrkšić N, Gašić M, Rojas-Barahona L M, Su P-H, Ultes S, Young S (2017) A network-based end-to-end trainable task-oriented dialogue system. In: Proceedings of the 15th conference of the european chapter of the association for computational linguistics: volume 1, long papers, pp 438–449

  8. Devlin J, Chang M-W, Lee K, Toutanova K (2019) BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers), pp 4171–4186

  9. Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V (2019) Roberta: A robustly optimized bert pretraining approach. arXiv:1907.11692

  10. Arase Y, Tsujii J (2019) Transfer fine-tuning: A BERT case study. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp 5393–5404

  11. Chang A X, Manning C D (2014) Tokensregex: Defining cascaded regular expressions over tokens. Tech. Rep. CSTR 2014-02

  12. Zhang S, He L, Vucetic S, Dragut E (2018) Regular expression guided entity mention mining from noisy web data. In: Proceedings of the 2018 conference on empirical methods in natural language processing, pp 1991–2000

  13. Li Y, Krishnamurthy R, Raghavan S, Vaithyanathan S, Jagadish H V (2008) Regular expression learning for information extraction. In: Proceedings of the 2008 conference on empirical methods in natural language processing, pp 21–30

  14. Hu Z, Ma X, Liu Z, Hovy E, Xing E (2016) Harnessing deep neural networks with logic rules. In: Proceedings of the 54th annual meeting of the association for computational linguistics (volume 1: long papers), pp 2410–2420

  15. Li X L, Rush A (2020) Posterior control of blackbox generation. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 2731–2743

  16. Alashkar T, Jiang S, Wang S, Fu Y (2017) Examples-rules guided deep neural network for makeup recommendation. In: Proceedings of the thirty-first AAAI conference on artificial intelligence, pp 941–947

  17. Awasthi A, Ghosh S, Goyal R, Sarawagi S (2020) Learning from rules generalizing labeled exemplars. In: Proceedings of the international conference on learning representations

  18. Xu J, Zhang Z, Friedman T, Liang Y, Van den Broeck G (2018) A semantic loss function for deep learning with symbolic knowledge. In: Proceedings of the 35th international conference on machine learning, vol 80, pp 5502–5511

  19. Luo B, Feng Y, Wang Z, Huang S, Yan R, Zhao D (2018) Marrying up regular expressions with neural networks: a case study for spoken language understanding. In: proceedings of the 56th annual meeting of the association for computational linguistics (volume 1: long papers), pp 2083–2093

  20. Jiang C, Zhao Y, Chu S, Shen L, Tu K (2020) Cold-start and interpretability: Turning regular expressions into trainable recurrent neural networks. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 3193–3207

  21. Kim Y (2014) Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 1746–1751

  22. Zhang X, Zhao J, LeCun Y (2015) Character-level convolutional networks for text classification. In: Advances in neural information processing systems, pp 649–657

  23. Ravuri S, Stolcke A (2016) A comparative study of recurrent neural network models for lexical domain classification. In: Proceedings of the international conference on acoustics, speech, and signal processing, pp 6075–6079

  24. Abro W A, Qi G, Gao H, Khan M A, Ali Z (2019) Multi-turn intent determination for goal-oriented dialogue systems. In: Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), pp 1–8

  25. Mesnil G, Dauphin Y, Yao K, Bengio Y, Deng L, Hakkani-Tur D, He X, Heck L, Tur G, Yu D et al (2015) Using recurrent neural networks for slot filling in spoken language understanding. IEEE/ACM Trans Audio Speech Lang Process 23(3):530– 539

    Article  Google Scholar 

  26. Hakkani-Tür D, Tür G, Celikyilmaz A, Chen Y-N, Gao J, Deng L, Wang Y-Y (2016) Multi-domain joint semantic frame parsing using bi-directional rnn-lstm. In: Proceedings of the International Speech Communication Association (INTERSPEECH 2016), pp 715–719

  27. Zhang C, Li Y, Du N, Fan W, Yu P (2019) Joint slot filling and intent detection via capsule neural networks. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp 5259–5267

  28. Aamir M, Rahman Z, Abro W A, Tahir M, Ahmed S M (2019) An optimized architecture of image classification using convolutional neural network. Int J Image Graph Signal Process 10(10):30

    Article  Google Scholar 

  29. Xia C, Zhang C, Yan X, Chang Y, Yu P (2018) Zero-shot user intent detection via capsule neural networks. In: Proceedings of the 2018 conference on empirical methods in natural language processing, pp 3090–3099

  30. Abro W A, Aicher A, Rach N, Ultes S, Minker W, Qi G (2022) Natural language understanding for argumentative dialogue systems in the opinion building domain. Knowl-Based Syst 242:108318

    Article  Google Scholar 

  31. Henderson M, Casanueva I , Mrkšić N, Su P-H, Wen T-H, Vulić I (2020) ConveRT: Efficient and accurate conversational representations from transformers. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp 2161–2174

  32. Firdaus M, Kumar A, Ekbal A, Bhattacharyya P (2019) A multi-task hierarchical approach for intent detection and slot filling. Knowl-Based Syst 183:104846. https://doi.org/10.1016/j.knosys.2019.07.017

    Article  Google Scholar 

  33. Chen Q, Zhuo Z, Wang W (2019) Bert for joint intent classification and slot filling. arXiv:1902.10909

  34. Bunk T, Varshneya D, Vlasov V, Nichol A (2020) DIET: lightweight language understanding for dialogue systems. arXiv:2004.09936

  35. Cer D, Yang Y, Kong S-, Hua N, Limtiaco N, St. John R, Constant N, Guajardo-Cespedes M, Yuan S, Tar C, Strope B, Kurzweil R (2018) Universal sentence encoder for English. In: Proceedings of the 2018 conference on empirical methods in natural language processing: system demonstrations, pp 169–174

  36. Henderson M, Casanueva I , Mrkšić N, Su P-H, Wen T-H, Vulić I (2020) ConveRT: Efficient and accurate conversational representations from transformers. In: Findings of the association for computational linguistics: EMNLP 2020, pp 2161– 2174

  37. Guarasci R, Silvestri S, De Pietro G, Fujita H, Esposito M (2022) Bert syntactic transfer: a computational experiment on italian, french and english languages. Comput Speech Lang 71:101261. https://doi.org/10.1016/j.csl.2021.101261

    Article  Google Scholar 

  38. Guarasci R, Silvestri S, Pietro G D, Fujita H, Esposito M (2021) Assessing bert’s ability to learn italian syntax: a study on null-subject and agreement phenomena. J Ambient Intell Humani Comput:1–15

  39. Esposito M, Damiano E, Minutolo A, De Pietro G, Fujita H (2020) Hybrid query expansion using lexical resources and word embeddings for sentence retrieval in question answering. Inf Sci 514:88–105. https://doi.org/10.1016/j.ins.2019.12.002

    Article  Google Scholar 

  40. Pota M, Ventura M, Fujita H, Esposito M (2021) Multilingual evaluation of pre-processing for bert-based sentiment analysis of tweets. Expert Syst Appl 181:115119. https://doi.org/10.1016/j.eswa.2021.115119

    Article  Google Scholar 

  41. Li T, Srikumar V (2019) Augmenting neural networks with first-order logic. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp 292–302

  42. Ali Z, Qi G, Muhammad K, Ali B, Abro W A (2020) Paper recommendation based on heterogeneous network embedding. Knowl-Based Syst 210:106438

    Article  Google Scholar 

  43. Ali Z, Qi G, Kefalas P, Abro W A, Ali B (2020) A graph-based taxonomy of citation recommendation models. Artif Intell Rev 53(7)

  44. Waqas M, Khan Z, Anjum S, Tahir M A (2020) Lung-wise tuberculosis analysis and automatic ct report generation with hybrid feature and ensemble learning.. In: CLEF (Working Notes)

  45. Abro W A, Qi G, Ali Z, Feng Y, Aamir M (2020) Multi-turn intent determination and slot filling with neural networks and regular expressions. Knowl-Based Syst 208:106428

    Article  Google Scholar 

  46. Locascio N, Narasimhan K, DeLeon E, Kushman N, Barzilay R (2016) Neural generation of regular expressions from natural language with minimal domain knowledge. In: Proceedings of the 2016 conference on empirical methods in natural language processing, pp 1918–1923

  47. Thompson K (1968) Programming techniques: Regular expression search algorithm. Commun ACM 11(6):419–422. https://doi.org/10.1145/363347.363387

    Article  MATH  Google Scholar 

  48. Viterbi A (1967) Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans Inf Theory 13(2):260–269

    Article  MATH  Google Scholar 

  49. Schwartz R, Thomson S, Smith N A (2018) Bridging CNNs, RNNs, and weighted finite-state machines. In: Proceedings of the 56th annual meeting of the association for computational linguistics (volume 1: long papers), pp 295–305

  50. Rabin M O, Scott D (1959) Finite automata and their decision problems. IBM J Res Dev 3 (2):114–125

    Article  MathSciNet  MATH  Google Scholar 

  51. Hopcroft J (1971) An n log n algorithm for minimizing states in a finite automaton. In: Theory of machines and computations. Elsevier, pp 189–196

  52. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A N, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Proceedings of the Advances in neural information processing systems, pp 5998–6008

  53. Wu Y, Schuster M, Chen Z, Le Q V, Norouzi M, Macherey W, Krikun M, Cao Y, Gao Q, Macherey K et al (2016) Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv:1609.08144

  54. Hemphill C T, Godfrey J J, Doddington G R (1990) The atis spoken language systems pilot corpus. In: Speech and Natural Language: Proceedings of a Workshop Held at Hidden Valley, Pennsylvania, June 24-27, 1990, pp 24–27

  55. Coucke A, Saade A, Ball A, Bluche T, Caulier A, Leroy D, Doumouro C, Gisselbrecht T, Caltagirone F, Lavril T et al (2018) Snips voice platform: an embedded spoken language understanding system for private-by-design voice interfaces. arXiv:1805.10190

  56. Mesnil G, Dauphin Y, Yao K, Bengio Y, Deng L, Hakkani-Tur D, He X, Heck L, Tur G, Yu D, Zweig G (2015) Using recurrent neural networks for slot filling in spoken language understanding. IEEE/ACM Trans Audio Speech Lang Process 23(3):530–539. https://doi.org/10.1109/TASLP.2014.2383614

    Article  Google Scholar 

  57. Friedl JEF (2006) Mastering regular expressions. O’Reilly Media, Inc.

  58. Kingma D P, Ba J (2014) Adam: A method for stochastic optimization. arXiv:1412.6980

  59. Qin L, Che W, Li Y, Wen H, Liu T (2019) A stack-propagation framework with token-level intent detection for spoken language understanding. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp 2078–2087

  60. Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13:281–305

    MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. School of Computer Science and Engineering, Southeast University, Nanjing, China

    Waheed Ahmed Abro, Guilin Qi & Zafar Ali

  2. National University of Computer and Emerging Sciences, Karachi Campus, Pakistan

    Waheed Ahmed Abro

  3. Department of Computer Science, Huanggang Normal University, Huangzhou, Hubei, China

    Muhammad Aamir

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Abro, W.A., Qi, G., Aamir, M. et al. Joint intent detection and slot filling using weighted finite state transducer and BERT. Appl Intell 52, 17356–17370 (2022). https://doi.org/10.1007/s10489-022-03295-9

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