ABSTRACT
"Chatbot" is a colloquial term used to refer to software components that possess the ability to interact with the end-user using natural language phrases. Many commercial platforms are offering sophisticated dashboards to build these chatbots with no or minimal coding. However, the job of composing the chatbot from real-world scenarios is not a trivial activity and requires a significant understanding of the problem as well as the domain. In this work, we present the concept of Intent Sets - an Architectural choice, that impacts the overall accuracy of the chatbot. We show that the same chatbot can be built choosing one out of many possible Intent Sets. We also present our observations collected through a set of experiments while building the same chatbot over three commercial platforms - Google Dialogflow, IBM Watson Assistant and Amazon Lex.
- Junwei Bao, Duyu Tang, Nan Duan, Zhao Yan, Yuanhua Lv, Ming Zhou, and Tiejun Zhao. 2018. Table-to-text: Describing table region with natural language. In Thirty-Second AAAI Conference on Artificial Intelligence.Google Scholar
- Petter Bae Brandtzaeg and Asbjørn Følstad. 2018. Chatbots: changing user needs and motivations. interactions 25, 5 (2018), 38--43.Google Scholar
- Ana Paula Chaves and Marco Aurelio Gerosa. 2019. How should my chatbot interact? A survey on human-chatbot interaction design. arXiv preprint arXiv:1904.02743 (2019).Google Scholar
- ESPNcricinfo.com. 1993. Statsguru | Searchable Cricket Statistics database. http://stats.espncricinfo.com/ci/engine/stats/index.html [On line; accessed 9-December-2019].Google Scholar
- Ethan Fast, Binbin Chen, Julia Mendelsohn, Jonathan Bassen, and Michael S Bernstein. 2018. Iris: A conversational agent for complex tasks. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, 473.Google ScholarDigital Library
- Jianfeng Gao, Michel Galley, and Lihong Li. 2019. Neural Approaches to Conversational AI: Question Answering, Task-oriented Dialogues and Social Chatbots. Now Foundations and Trends.Google Scholar
- Gidi Shperber. 2017. ChatBots vs Reality: how to build an efficient chatbot, with wise usage of NLP. https://towardsdatascience.com/chatbots-vs-reality-how-to-buildan-efficient-chatbot-with-wise-usage-of-nlp-77f41949bf08[Online; accessed 9-December-2019].Google Scholar
- Robin Håvik, Jo Dugstad Wake, Eivind Flobak, Astri Lundervold, and Frode Guribye. 2018. A Conversational Interface for Self-screening for ADHD in Adults. In International Conference on Internet Science. Springer, 133--144.Google Scholar
- Kezban Dilek Onal, Ye Zhang, Ismail Sengor Altingovde, Md Mustafizur Rahman, Pinar Karagoz, Alex Braylan, Brandon Dang, Heng-Lu Chang, Henna Kim, Quinten McNamara, et al. 2018. Neural information retrieval: At the end of the early years. Information Retrieval Journal 21, 2-3 (2018), 111--182.Google ScholarDigital Library
- Saurabh Srivastava and TV Prabhakar. 2019. Hospitality of chatbot building platforms. In Proceedings of the 2nd ACM SIGSOFT International Workshop on Software Qualities and Their Dependencies. ACM, 12--19.Google ScholarDigital Library
- theta.co.nz. 1995. FAQ Bot - AI chatbot that answers questions, instantly, 24/7. https://www.theta.co.nz/technologies/faq-bot/ [Online; accessed 9-December-2019].Google Scholar
- Svitlana Vakulenko and Vadim Savenkov. 2017. Tableqa: Question answering on tabular data. arXiv preprint arXiv:1705.06504 (2017).Google Scholar
- Lisa Waldera. 2019. Development of a Preliminary Measurement Tool of User Satisfaction for Information-Retrieval Chatbots. B.S. thesis. University of Twente.Google Scholar
- Xinyi Wang, Samuel S Sohn, and Mubbasir Kapadia. 2019. Towards a Conversational Interface for Authoring Intelligent Virtual Characters. In Proceedings of the 19th ACM International Conference on Intelligent Virtual Agents. ACM, 127--129.Google ScholarDigital Library
- Zhao Yan, Nan Duan, Junwei Bao, Peng Chen, Ming Zhou, Zhoujun Li, and Jianshe Zhou. 2016. Docchat: An information retrieval approach for chatbot engines using unstructured documents. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 516--525.Google ScholarCross Ref
Index Terms
- Intent Sets: Architectural Choices for Building Practical Chatbots
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