Intent Discovery Pipeline using Z-Bert-A
Abstract views: 25
/ 
PDF downloads: 3
Keywords:
Unknown Intents, Z-Bert-A, Intent Discovery Pipeline, Dialogue Systems, Deep LearningAbstract
This paper introduces a novel approach to handling unknown intents in dialogue systems by
proposing a custom intent discovery pipeline using Z-BERT-A. Developed in Python, this pipeline is
specifically designed to address intents that are not predefined within the system. The development of this
solution is guided by a comprehensive literature review, which examines existing models and techniques,
including rule-based, machine learning, and deep learning approaches. Experimental results on the SNLI
and banking datasets demonstrate that Z-BERT-A outperforms other models in managing unknown intents.
The proposed pipeline integrates Z-BERT-A and customizes its source code, creating a flexible and
generalized solution for intent discovery. Capable of handling unseen intents, this pipeline is crucial for
modern dialogue systems used in triage scenarios. Additionally, it is resource-efficient, easily adaptable to
various domains, and integrates seamlessly into existing systems. The pipeline also incorporates
preprocessing and postprocessing steps to ensure accuracy, efficiency, and scalability. The paper concludes
by evaluating the pipeline’s performance through multiple metrics, comparing it to other state-of-the-art
models.
Downloads
References
C. B. Chandrakala, R. Bhardwaj, and C. Pujari, “An intent recognition pipeline for conversational AI,” International Journal of Information Technology, vol. 16, no. 2, pp. 731–743, Feb. 2024, doi: 10.1007/s41870-023-01642-8.
D. Comi, D. Christofidellis, P. F. Piazza, and M. Manica, “Z-BERT-A: a zero-shot Pipeline for Unknown Intent detection,” Aug. 2022.
D. Comi, D. Christofidellis, P. Piazza, and M. Manica, “Zero-Shot-BERT-Adapters: a Zero-Shot Pipeline for Unknown Intent Detection,” in Findings of the Association for Computational Linguistics: EMNLP 2023, Stroudsburg, PA, USA: Association for Computational Linguistics, 2023, pp. 650–663. doi: 10.18653/v1/2023.findings-emnlp.47.
A. S. Adekotujo, T. Enikuomehin, B. Aribisala, M. Mazzara, and A. F. Zubair, “Computational treatment of natural language text for intent detection,” Computer Research and Modeling, vol. 16, no. 7, pp. 1539–1554, Dec. 2024, doi: 10.20537/2076-7633-2024-16-7-1539-1554.
H. Zhang et al., “Generic Intent Representation in Web Search,” in Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, NY, USA: ACM, Jul. 2019, pp. 65–74. doi: 10.1145/3331184.3331198.
J. Atuhurra, H. Kamigaito, T. Watanabe, and E. Nichols, “Domain Adaptation in Intent Classification Systems: A Review,” Mar. 2024.
R. V. V. Vishruth and S. G. Mohan, “Online video conference analytics: A systematic review,” in Applied Data Science and Smart Systems, London: CRC Press, 2024, pp. 435–447. doi: 10.1201/9781003471059-57.
J. Singh, S. Goyal, R. Kumar Kaushal, N. Kumar, and S. Singh Sehra, Applied Data Science and Smart Systems. London: CRC Press, 2024. doi: 10.1201/9781003471059.
E. Tabaku, E. Duçi, and A. Lazaj, “From Physical Stores to Virtual Marketplaces: The Evolution of Shopping,” Interdisciplinary Journal of Research and Development, vol. 11, no. 3, p. 175, Dec. 2024, doi: 10.56345/ijrdv11n324.
R. Mohammad, O. S. Alkhnbashi, and M. Hammoudeh, “Optimizing Large Language Models for Arabic Healthcare Communication: A Focus on Patient-Centered NLP Applications,” Big Data and Cognitive Computing, vol. 8, no. 11, p. 157, Nov. 2024, doi: 10.3390/bdcc8110157.
A. Hrytsyna and R. Alves, “From Representation to Response: Assessing the Alignment of Large Language Models with Human Judgment Patterns,” ACM Trans Intell Syst Technol, Dec. 2024, doi: 10.1145/3709148.
H. Hettiarachchi et al., “CODE-ACCORD: A Corpus of building regulatory data for rule generation towards automatic compliance checking,” Sci Data, vol. 12, no. 1, p. 170, Jan. 2025, doi: 10.1038/s41597-024-04320-x.
R. Mohammad, O. Favell, S. Shah, E. Cooper, and E. Vakaj, “Utilisation of open intent recognition models for customer support intent detection,” Jul. 2023.
D. Al-Turki et al., “Human-in-the-Loop Learning with LLMs for Efficient RASE Tagging in Building Compliance Regulations,” IEEE Access, pp. 1–1, 2024, doi: 10.1109/ACCESS.2024.3512434.
J. R. Hershey, Z. Chen, J. Le Roux, and S. Watanabe, “Deep clustering: Discriminative embeddings for segmentation and separation,” in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Mar. 2016, pp. 31–35. doi: 10.1109/ICASSP.2016.7471631.
K. Berahmand, S. Bahadori, M. N. Abadeh, Y. Li, and Y. Xu, “SDAC-DA: Semi-Supervised Deep Attributed Clustering Using Dual Autoencoder,” IEEE Trans Knowl Data Eng, vol. 36, no. 11, pp. 6989–7002, Nov. 2024, doi: 10.1109/TKDE.2024.3389049.
P. Liu, Y. Ning, K. K. Wu, K. Li, and H. Meng, “Open Intent Discovery through Unsupervised Semantic Clustering and Dependency Parsing,” Apr. 2021.
L. W. Y. Yang et al., “Development and testing of a multi-lingual Natural Language Processing-based deep learning system in 10 languages for COVID-19 pandemic crisis: A multi-center study,” Front Public Health, vol. 11, 2023, doi: 10.3389/fpubh.2023.1063466.
A. B. Saka, L. O. Oyedele, L. A. Akanbi, S. A. Ganiyu, D. W. M. Chan, and S. A. Bello, “Conversational artificial intelligence in the AEC industry: A review of present status, challenges and opportunities,” 2023. doi: 10.1016/j.aei.2022.101869.
E. Tabaku, E. Vyshka, R. Kapçiu, A. Shehi, and E. Smajli, “UTILIZING ARTIFICIAL INTELLIGENCE IN ENERGY MANAGEMENT SYSTEMS TO IMPROVE CARBON EMISSION REDUCTION AND SUSTAINABILITY,” Jurnal Ilmiah Ilmu Terapan Universitas Jambi, vol. 9, no. 1, pp. 393–405, Feb. 2025, doi: 10.22437/jiituj.v9i1.38665.
E. Tabaku and E. Duçi, “Optimizing High Availability in Educational Systems Using Xen Paravirtualization,” Journal of Educational and Social Research, vol. 15, no. 2, p. 205, Mar. 2025, doi: 10.36941/jesr-2025-0054.
S. C. Ivan, R. Ş. Győrödi, and C. A. Győrödi, “Sentiment Analysis Using Amazon Web Services and Microsoft Azure,” Big Data and Cognitive Computing, vol. 8, no. 12, p. 166, Nov. 2024, doi: 10.3390/bdcc8120166.
Y. Ren, K. Hu, X. Dai, L. Pan, S. C. H. Hoi, and Z. Xu, “Semi-supervised deep embedded clustering,” Neurocomputing, vol. 325, pp. 121–130, Jan. 2019, doi: 10.1016/j.neucom.2018.10.016.
E. Tabaku, “Improving High Availability Services Using KVM Full Virtualization,” European Journal of Computer Science and Information Technology, vol. 13, no. 1, pp. 1–15, Jan. 2025, doi: 10.37745/ejcsit.2013/vol13n1115.
A. Y. Yousif and B. Al Sarray, “Convex Optimization Techniques for High-Dimensional Data Clustering Analysis: A Review,” Iraqi Journal for Computer Science and Mathematics, vol. 5, no. 3, Jan. 2024, doi: 10.52866/ijcsm.2024.05.03.022.
P. Liu, Y. Ning, K. K. Wu, K. Li, and H. Meng, “Open Intent Discovery through Unsupervised Semantic Clustering and Dependency Parsing,” Apr. 2021.
S. Moradizeyveh, “Intent Recognition in Conversational Recommender Systems,” Dec. 2022.
A. Chatterjee and S. Sengupta, “Intent Mining from past conversations for conversational agent,” May 2020.
D. Comi, D. Christofidellis, P. F. Piazza, and M. Manica, “Z-BERT-A: a zero-shot Pipeline for Unknown Intent detection,” Aug. 2022.
E. Tabaku, “The Evolution of Technology in Accounting and Corporate Finance: Implications for Business Adaptation and Competitiveness,” International Research Journal of Modernization in Engineering Technology and Science, Jan. 2025, doi: 10.56726/IRJMETS66318.
S. Kumar, S. Deep, and P. Kalra, “Enhancing Customer Service in Banking with AI: Intent Classification Using Distilbert,” International Journal of Current Science Research and Review, vol. 07, no. 05, May 2024, doi: 10.47191/ijcsrr/V7-i5-32.
