SỌ̀RỌ̀: A Yorùbá Language Task Oriented Dialogue System

  • O. Ipinnimo Department of Systems Engineering, University of Lagos, Nigeria
  • T. Adegbola African Languages Technology Initiative (Alt-i)
  • C. Folorunsho Department of Systems Engineering, University of Lagos, Nigeria
Keywords: Chatbot, linguistic investigation, sọ̀rọ̀, task-oriented-dialogue, Yorùbá


This paper presents Sọ̀rọ̀: a task-oriented dialogue system, developed utilising a rule-based technique. Sọ̀rọ̀ is fit for having a trade of four key sorts: greetings, small talk, basic number-crunching, and time/date. This dialogue framework has been fabricated following a linguistic investigation of the Yorùbá language and the rules defined from the analysis of publicly supported information. Sọ̀rọ̀'s conversational capacities are restricted to text-based trade and revolved only around a small domain of topics due to its limited vocabulary data sets. The framework involves three primary and auxiliary scripts each. The primary scripts are the bag-of-words, natural language understanding and natural language processing scripts while the auxiliary scripts are the task manager, the properties script, and the main script where dialogue sessions occur. This study developed rules that identify sentence types in utterances, split sentences into intent and entity, perform a list of tasks as identified in utterances and provide a response to this effect. This paper characterises rules that relate 66.7% accuracy the type of sentence contained within a sample utterance, with a precision of 94.7%. This study demonstrates the practicality of a Yorùbá language dialogue framework and simultaneously, design a dynamic dialogue system architecture likely to be improved upon with additional data.


Abdul-Kader, S. A., and Woods, J. C. (2015). Survey on chatbot design techniques in speech conversation systems. International Journal of Advanced Computer Science and Applications, 6 (7): 72-80.
Adegbola, T., Owolabi, K., and Odejobi, T. (2011). Localising for Yorùbá: Experience, challenges and future direction. Proceedings of Conference on Human Language Technology for Development: 3–5.
Baez, M., Daniel, F., and Fabio Casati. (2019). Conversational web interaction: proposal of a dialog-based natural language interaction paradigm for the web. International Workshop on Chatbot Research and Design, Springer, Cham: 94–110.
Bahdanau, D., Cho, K., and Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. ArXiv Preprint ArXiv:1409.0473: 1-15.
Balint. (2017). What is a chatbot? – Everything that you need to know about chatbots – Part 1. Available at https://chatbottutorial.com/chatbot/, retrieved on April 28, 2018. Bhattad, H., and Atkar, M. G. (2021). Review on Different types of Chatbots. International Research Journal of Modernization in Engineering Technology and Science (IRJMETS), 3 (5): 1347-1349.
Burgan, D. (2016). Dialogue systems and dialogue management. DST Group Edinburgh Edinburgh SA Australia, available at https://apps.dtic.mil/sti/pdfs/AD1027343.pdf, retrieved on February 26, 2018.
Coursera. (2018). Natural Language Processing (A. Zimovnov, Ed.). Advanced Machine Learning Specialization. Available at https://www.coursera.org/learn/language-processing, retrieved on June 9, 2018.
Covington, M. A. (1994). Natural language processing for Prolog programmers. Prentice hall Englewood Cliffs (NJ). Available at https://www.e-booksdirectory.com/details.php?ebook=9368, retrieved on May 7, 2018.
De Los Riscos, A. M., and D’Haro, L. F. (2021). ToxicBot: A Conversational Agent to Fight Online Hate Speech. In Conversational Dialogue Systems for the Next Decade, Springer, Singapore: 15–30.
Discover.bot. (2019). Chatbot Development: Building Bots with Wit.ai. Available at https://discover.bot/bot-talk/guide-to-bot-buiding-frameworks/wit-ai/#:~:text=Wit.ai is an open,%2C websites%2C and IoT devices, retrieved on November 2, 2020.
Duraj, M. (2020). How to Build a Basic Chatbot with Motion.AI. Available at https://www.pluralsight.com/guides/how-to-build-a-basic-chatbot-with-motion.ai, retrieved on November 2, 2020.
EF Education First. (2019). EF English Proficiency Index Available at https://www.ef.com/wwen/epi/reports/epi-s/, retrieved on October 24, 2020.
Fox, G. (2020). Botsify. Available at https://www.garyfox.co/startup-resources/botsify/, retrieved on June 9, 2020.
Gardner, M., Grus, J., Neumann, M., Tafjord, O., Dasigi, P., Liu, N., and Zettlemoyer, L. (2018). AllenNLP: A deep semantic natural language processing platform. ArXiv Preprint ArXiv:1803.07640: 1-6.
Harms, J.-G., Kucherbaev, P., Bozzon, A., and Houben, G.-J. (2018). Approaches for Dialog Management in Conversational Agents. IEEE Internet Computing 23 (2): 13-22. Haruna, U., Maitalata, U. S., Mohammed, M., and Maitama, J. Z. (2020). Hausa Intelligence Chatbot System. In International Conference on Information and Communication Technology and Applications Springer, Cham, 206-219. Hasal, M., Nowaková, J., Ahmed Saghair, K., Abdulla, H., Snášel, V., and Ogiela, L. (2021). Chatbots: Security, privacy, data protection, and social aspects. Concurrency and Computation: Practice and Experience, e6426: 1-13.
Klüwer, T. (2011). From Chatbots to Dialog Systems. In D. Perez-Marin & I. Pascual-Nieto (Eds.), In Conversational agents and natural language interaction: Techniques and Effective Practices, https://doi.org/http://doi:10.4018/978-1-60960-617-6.ch001.
Larsson, S. (2002). Issue-based dialogue management. Department of Linguistics, Göteborg University. Available at http://citeseerx.ist.psu.edu/viewdoc/download?doi=, retrieved on April 28, 2018.
Liang, P. (2017). Natural Language Understanding: Foundations and State-of-the-Art. Foundations of Machine Learning Boot Camp. Available at https://www.youtube.com/watch?v=mhHfnhh-pB4, retrieved on October 23, 2019.
Lucas, T. (2016). Introducing Converse.AI Chatflow! Available at https://blog.converse.ai/introducing-converse-ai-chatflow-969ded6b3462, retrieved on November 2, 2020.
Martin, J. H., and Jurafsky, D. (2009). Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition. Pearson/Prentice Hall Upper Saddle River.
Mei, H., Bansal, M., and Walter, M. R. (2015). What to talk about and how? selective generation using lstms with coarse-to-fine alignment. ArXiv Preprint ArXiv:1509.00838: 1-11.
Milhorat, P., Lala, D., Inoue, K., Zhao, T., Ishida, M., Takanashi, K., and Kawahara, T. (2019). A conversational dialogue manager for the humanoid robot ERICA. In Advanced Social Interaction with Agents, Springer: 119–131.
Mirkovic, D., Cavedon, L., Purver, M., Ratiu, F., Scheideck, T., Weng, F., and Xu, K. (2011). Dialogue management using scripts and combined confidence scores. Google Patents.
Nallapati, R., Zhou, B., Gulcehre, C., and Xiang, B. (2016). Abstractive text summarization using sequence-to-sequence rnns and beyond. ArXiv Preprint ArXiv:1602.06023: 1-12.
Ndlovu, F. N. (2019). Tutorial : Azure QnA Maker Chatbot for Students. Available at https://medium.com/@fanienocholasndlovu/tutorial-azure-qna-maker-chatbot-for-students-1565ae327781, retrieved on November 2, 2020.
Oh, A. H., and Rudnicky, A. I. (2000). Stochastic language generation for spoken dialogue systems. ANLP-NAACL 2000 Workshop: Conversational Systems: 27-31.
Oluwatoyin, E. A. (2015). A Computerized Identification System for Verb Sorting and Arrangement in a Natural Language: Case Study of the Nigerian Yorùbá Language. European Journal of Computer Science and Information Technology, 3: 43–52.
Paliwal, S., Bharti, V., and Mishra, A. K. (2020). Ai Chatbots: Transforming the Digital World. Recent Trends and Advances in Artificial Intelligence and Internet of Things, Springer, Cham: 455–482.
Porter, S. (2015). A simple introduction to IBM Watson and what it can do for your business. Available at https://www.linkedin.com/pulse/simple-introduction-ibm-watson-what-can-do-your-business-simon-porter/, retrieved on Noevember 2, 2020.
Reiter, E., and Dale, R. (2001). Building natural language generation systems. Available at https://www.researchgate.net/publication/239283413_Ehud_Reiter_and_Robert_Dale_Building_Natural_Language_Generation_Systems, retrieved on April 24, 2019.
Rush, A. M., Chopra, S., and Weston, J. (2015). A neural attention model for abstractive sentence summarization.

ArXiv Preprint ArXiv:1509.00685: 1-11.
Schank, R. C., and Abelson, R. P. (2013). Scripts, plans, goals, and understanding: An inquiry into human knowledge structures. Psychology Press.
Serban, I. V, Sankar, C., Germain, M., Zhang, S., Lin, Z., Subramanian, S., and Ke, N. R. (2017). A deep reinforcement learning chatbot. ArXiv Preprint ArXiv:1709.02349: 1- 40.
Simons, G. F., and Fennig, C. D. (2018). Ethnologue: Languages of the World (Twenty-fir). Available at http://www.ethnologue.com, retrieved on July 5, 2020.
Skantze, G. (2007). Error Handling in Spoken Dialogue Systems-Managing Uncertainty, Grounding and Miscommunication. Gabriel Skantze.
Stent, A., Doweling, J., Gawron, J. M., Bratt, E. O., and Moore, R. (2004). The CommandTalk spoken dialogue system. Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics: 183-190.
Tran, V.-K., and Nguyen, L.-M. (2019). Gating mechanism based Natural Language Generation for spoken dialogue systems. Neurocomputing, 325: 48–58.
Vinyals, O., and Le, Q. (2015). A neural conversational model. ArXiv Preprint ArXiv:1506.05869: 1-8.
Wang, M., Lu, Z., Li, H., Jiang, W., and Liu, Q. (2015). A Convolutional Architecture for Word Sequence Prediction. ArXiv Preprint ArXiv:1503.05034: 1567-1576.
Wen, T.-H., Gasic, M., Kim, D., Mrksic, N., Su, P.-H., Vandyke, D., and Young, S. (2015). Stochastic language generation in dialogue using recurrent neural networks with convolutional sentence reranking. ArXiv Preprint ArXiv:1508.01755: 1-10.
Wen, T.-H., Gašic, M., Mrkšic, N., Rojas-Barahona, L. M., Su, P.-H., Vandyke, D., and Young, S. (2015). Toward multi-domain language generation using recurrent neural networks. The Proceedings of the 29th Annual Conference on Neural Information Processing Systems (NIPS), Workshop on Machine Learning for Spoken Language Understanding and Interaction: 1-7.
Wen, T.-H., Gasic, M., Mrksic, N., Su, P.-H., Vandyke, D., and Young, S. (2015). Semantically conditioned lstm-based natural language generation for spoken dialogue systems. ArXiv Preprint ArXiv:1508.01745: 1-11.
Wen, T.-H., Vandyke, D., Mrksic, N., Gasic, M., Rojas-Barahona, L. M., Su, P.-H., and Young, S. (2016). A network-based end-to-end trainable task-oriented dialogue system. ArXiv Preprint ArXiv:1604.04562: 1-12.
Williams, S. (1996). Dialogue Management in a mixed-initiative, cooperative, spoken language system. Available at http://users.mct.open.ac.uk/sw6629/Publications/twlt96.pdf, retrieved on October 25, 2019.
Yang, Z., Yuan, Y., Wu, Y., Cohen, W. W., and Salakhutdinov, R. R. (2016). Review networks for caption generation. Advances in Neural Information Processing Systems: 2361–2369.
Yao, M. (2017). Four Approaches To Natural Language Processing and Understanding. Available at https://www.freecodecamp.org/news/how-natural-language-processing-powers-chatbots-4-common-approaches-a077a4de04d4/, retrieved on September 23, 2020.
How to Cite
Ipinnimo, O., Adegbola, T., & Folorunsho, C. (2022). SỌ̀RỌ̀: A Yorùbá Language Task Oriented Dialogue System. Journal of Engineering Research, 27(1), 26-38. Retrieved from http://jer.unilag.edu.ng/article/view/1591