Automated Creation of an Intent Model for Conversational Agents

References

• Alibrahim, H., and S. A. Ludwig. 2021. Hyperparameter optimization: Comparing genetic algorithm against grid search and bayesian optimization. In 2021 IEEE congress on evolutionary computation (cec), 1551–16. doi: 10.1109/CEC45853.2021.9504761
   Google Scholar
• Ankerst, M., M. M. Breunig, H. -P. Kriegel, and J. Sander. 1999. Optics: Ordering points to identify the clustering structure. In Proceedings of the 1999 acm sigmod international conference on management of data, 49–60. New York, NY, USA: Association for Computing Machinery. Retrieved from doi: 10.1145/304182.304187
   Google Scholar
• Casanueva, I., T. Temcinas, D. Gerz, M. Henderson, and I. Vulic. 2020, marth). Efficient intent detection with dual sentence encoders. In Proceedings of the 2nd workshop on nlp for convai - acl 2020, Retrieved from doi: 10.48550/arXiv.2003.04807
   Google Scholar
• Chatterjee, A., and S. Sengupta (2020, December). Intent mining from past conversations for conversational agent. In Proceedings of the 28th international conference on computational linguistics, 4140–52. Barcelona, Spain (Online): International Committee on Computational Linguistics. Retrieved from doi: 10.18653/v1/2020.coling-main.366
   Google Scholar
• Coucke, A., A. Saade, A. Ball, T. Bluche, A. Caulier, D. Leroy, C. Doumouro, T. Gisselbrecht, F. Caltagirone, T. Lavril, et al. 2018. Snips voice platform: An embedded spoken language understanding system for private-by-design voice interfaces. CoRR, abs/1805.10190. Retrieved from http://arxiv.org/abs/1805.10190
   Google Scholar
• Deloitte. 2019. What is conversational ai? Retrieved from https://www2.deloitte.com/content/dam/Deloitte/au/Documents/strategy/au-deloitte-conversational-ai.pdf
   Google Scholar
• Devlin, J., M. -W. Chang, K. Lee, and K. Toutanova. 2019. Bert: Pre-training of deep bidirectional transformers for language understanding. In NAACL-HLT 2019, ed. J. Burstein, C. Doran, and T. Solorio, 4171–4186. Minneapolis, MN, USA: Association for Computational Linguistics.
   Google Scholar
• Di Francescomarino, C., M. Dumas, M. Federici, C. Ghidini, F. M. Maggi, W. Rizzi, and L. Simonetto. 2018. Genetic algorithms for hyperparameter optimization in predictive business process monitoring. Information Systems 74:67–83. Retrieved from (Information Systems Engineering: selected papers from CAiSE 2016) doi:https://doi.org/10.1016/j.is.2018.01.003.
   Web of Science ®Google Scholar
• Ester, M., H. -P. Kriegel, J. Sander, and X. Xu. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the second international conference on knowledge discovery and data mining, 226–31. Portland, OR: AAAI Press.
   Google Scholar
• Goyal, A. K., A. Metallinou, and S. Matsoukas. (2018, June). Fast and scalable expansion of natural language understanding functionality for intelligent agents. In Proceedings of the 2018 conference of the north American chapter of the association for computational linguistics: Human language technologies, vol 3 (industry papers) 145–52. New Orleans - Louisiana: Association for Computational Linguistics. Retrieved from doi: 10.18653/v1/N18-3018
   Google Scholar
• Haponchyk, I., A. Uva, S. Yu, O. Uryupina, and A. Moschitti. 2018, October-November. Supervised clustering of questions into intents for dialog system applications. In Proceedings of the 2018 conference on empirical methods in natural language processing, 2310–21. Brussels, Belgium: Association for Computational Linguistics. Retrieved from doi: 10.18653/v1/D18-1254
   Google Scholar
• Hemphill, C. T., J. J. Godfrey, and G. R. Doddington. 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. Retrieved from https://www.aclweb.org/anthology/H90-1021
   Google Scholar
• Jolliffe, I. T. 1986. Principal components in regression analysis. In Principal component analysis, ed. I. T. Jolliffe, 129–155. New York, NY: Springer.
   Google Scholar
• Kelley, J. F. 1984. An iterative design methodology for user-friendly natural language office information applications. ACM Transactions on Information Systems 2 (1):26–41. doi:10.1145/357417.357420.
   Web of Science ®Google Scholar
• Lloyd, S. P. 1982. Least squares quantization in pcm. IEEE Transactions on Information Theory 28 (2):129–36. doi:10.1109/TIT.1982.1056489.
   Web of Science ®Google Scholar
• Lundberg, S., and S. Lee 2017. A unified approach to interpreting model predictions. CoRR, abs/1705.07874. Retrieved from http://arxiv.org/abs/1705.07874
   Google Scholar
• Mallinar, N., A. Shah, R. Ugrani, A. Gupta, M. Gurusankar, T. K. Ho …, and B. McGregor.2018. Bootstrapping conversational agents with weak supervision. arXiv Retrieved from. 10.48550/ARXIV.1812.06176.
   Google Scholar
• McInnes, L., J. Healy, and S. Astels. 2017, march. Hdbscan: Hierarchical density based clustering. Journal of Open Source Software 2(11):205. doi: 10.21105/joss.00205.
   Google Scholar
• McInnes, L., J. Healy, and J. Melville. 2020. Umap: Uniform manifold approximation and projection for dimension reduction. arXiv.
   Google Scholar
• Mockus, J. B., and L. J. Mockus. 1991, July. Bayesian approach to global optimization and application to multiobjective and constrained problems. Journal of Optimization Theory and Applications 70(1):157–72. doi: 10.1007/BF00940509.
   Web of Science ®Google Scholar
• Reimers, N., and I. Gurevych. 2019, November. Sentence-bert: Sentence embeddings using siamese bert-networks. In Proceedings of the 2019 conference on empirical methods in natural language processing, Association for Computational Linguistics. Retrieved from https://arxiv.org/abs/1908.10084
   Google Scholar
• Research, & Markets. 2021. Global conversational ai market (2021-2026). Retrieved from https://www.researchandmarkets.com/reports/5359446/global-conversational-ai-market-2021-2026-by
   Google Scholar
• Rosenberg, A., and J. Hirschberg. 2007, June. V-measure: A conditional entropy-based external cluster evaluation measure. In Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP-CoNLL), 410–20. Prague, Czech Republic: Association for Computational Linguistics. Retrieved from https://www.aclweb.org/anthology/D07-1043
   Google Scholar
• Salakhutdinov, R., and G. Hinton. 2009. Semantic hashing. International Journal of Approximate Reasoning 50 (7):969–78. doi:10.1016/j.ijar.2008.11.006.
   Web of Science ®Google Scholar
• Settles, B. 2009. Active learning literature survey. 1648, University of Wisconsin-Madison Department of Computer Sciences, Madison, WI.
   Google Scholar
• Shi, C., Q. Chen, L. Sha, S. Li, X. Sun, H. Wang, and L. Zhang. 2018, October-November. Auto-dialabel: Labeling dialogue data with unsupervised learning. In Proceedings of the 2018 conference on empirical methods in natural language processing, 684–89. Brussels, Belgium: Association for Computational Linguistics. Retrieved from doi: 10.18653/v1/D18-1072
   Google Scholar
• Thakur, N., N. Reimers, J. Daxenberger, and I. Gurevych. 2021. Augmented sbert: Data augmentation method for improving bi-encoders for pairwise sentence scoring tasks. arXiv.
   Google Scholar
• Thomas, P. 2009, January. Semi-supervised learning by Olivier Chapelle, Bernhard sch¨olkopf, and alexander zien (review). IEEE Transactions on Neural Networks 20(3):542. doi: 10.1109/TNN.2009.2015974.
   Google Scholar
• van der Maaten, L., and G. Hinton. 2008. Visualizing data using t-sne. Journal of Machine Learning Research 9 (86):2579–605. Retrieved from. http://jmlr.org/papers/v9/vandermaaten08a.html.
   Google Scholar
• Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. In NIPS 2017, Vol. 30. Long Beach, CA, USA: Curran Associates, Inc.
   Google Scholar
• Vinh, N. X., J. Epps, and J. Bailey. 2010. Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance. Journal of Machine Learning Research 11 (95):2837–54. Retrieved from. http://jmlr.org/papers/v11/vinh10a.html.
   Google Scholar
• Wen, T. -H., D. Vandyke, N. Mrkˇsi´c, M. Gaˇsi´c, L. M. Rojas-Barahona, P. -H. Su, Utlas, S. and S. Young. 2017, April. 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, 438–49. Valencia, Spain: Association for Computational Linguistics. Retrieved from https://www.aclweb.org/anthology/E17-1042
   Google Scholar
• Williams, J., N. Niraula, P. Dasigi, A. Lakshmiratan, C. Suarez, M. Reddy, and G. Zweig. 2015 January. Rapidly scaling dialog systems with interactive learning. Rapidly Scaling Dialog Systems with Interactive Learning 1–13. doi:10.1007/978-3-319-19291-81.
   Google Scholar