Xatkit: A Multimodal Low-Code Chatbot Development Framework

Abstract

Chatbot (and voicebot) applications are increasingly adopted in various domains such as e-commerce or customer services as a direct communication channel between companies and end-users. Multiple frameworks have been developed to ease their definition and deployment. While these frameworks are efficient to design simple chatbot applications, they still require advanced technical knowledge to define complex interactions and are difficult to evolve along with the company needs (e.g. it is typically impossible to change the NL engine provider). In addition, the deployment of a chatbot application usually requires a deep understanding of the targeted platforms, especially back-end connections, increasing the development and maintenance costs. In this paper, we introduce the Xatkit framework. Xatkit tackles these issues by providing a set of Domain Specific Languages to define chatbots (and voicebots and bots in general) in a platform-independent way. Xatkit also comes with a runtime engine that automatically deploys the chatbot application and manages the defined conversation logic over the platforms of choice. Xatkit's modular architecture facilitates the separate evolution of any of its components. Xatkit is open source and fully available online. INDEX TERMS Modeling, DSL, chatbot design, chatbot deployment.

A Wizard of Oz Study Simulating API Usage Dialogues With a Virtual Assistant

“…Data from Wizard of Oz experiments have a variety of direct applications in the development of a virtual agent; for instance, they can be used to improve a system's natural language understanding capabilities. Many modern natural language understanding frameworks (such as Alexa Skills [25] or Xatkit [26]) require explicit samples of user phrases that correspond to different dialogue acts; rather than attempt to intuit the phrases that real users would use, virtual assistant developers can extract real examples directly from the Wizard of Oz data. Other approaches to dialogue act classification use Wizard of Oz data to train statistical models to generalize to unseen inputs [11], [27].…”

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“…According to forecasts, by the year 2022, 80% of CC companies will be using chatbots and banks will be able to automate up to 90% of their customer-bot interactions. It is expected that the global chatbot market will reach USD 2 billion by 2024, growing at a CAGR (Compound Annual Growth Rate) of 29.7% [56]. Such a strong interest in this technology is driven by two main factors.…”

All Researchers Should Become Entrepreneurs

“…Our reflections are greatly influenced by the lessons learned while creating Xatkit. Xatkit is a model-based approach to (chat)bot development [8]. Xatkit started already as a researchindustry collaboration but after the partner company quit the project, we took the plunge and industrialized the tool ourselves, creating a spin-off in the process.…”