Towards automating the synthesis of chatbots for conversational model query

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Abstract

Conversational interfaces (also called chatbots) are being increasingly adopted in various domains such as e-commerce or customer service, as a direct communication channel between companies and end-users. Their advantage is that they can be embedded within social networks, and provide a natural language (NL) interface that enables their use by non-technical users. While there are many emerging platforms for building chatbots, their construction remains a highly technical, challenging task. In this paper, we propose the use of chatbots to facilitate querying domain-specific models. This way, instead of relying on technical query languages (e.g., OCL), models are queried using NL as this can be more suitable for non-technical users. To avoid manual programming, our solution is based on the automatic synthesis of the model query chatbots from a domain meta-model. These chatbots communicate with an EMF-based modelling backend using the Xatkit framework.

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Pérez-Soler, S., Daniel, G., Cabot, J., Guerra, E., & de Lara, J. (2020). Towards automating the synthesis of chatbots for conversational model query. In Lecture Notes in Business Information Processing (Vol. 387 LNBIP, pp. 257–265). Springer. https://doi.org/10.1007/978-3-030-49418-6_17

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