Abstract
Task-oriented conversational bots allow users to access services and perform tasks through natural language conversations. However, integrating these bots and software-enabled services has not kept pace with our ability to deploy individual devices and services. The main drawbacks of current bots and services integration techniques stem from the inherent development and maintenance cost. In addition, existing Natural Language Processing (NLP) techniques automate various tasks but the synthesis of API calls to support broad range of potentially complex user intents is still largely a manual and costly process. In this paper, we propose three types of reusable patterns for recognising compositional conversational flows and therefore automatically support increased complexity and expressivity during the conversation.
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Notes
- 1.
Chatfuel: https://chatfuel.com/; FlowXO: https://flowxo.com/.
- 2.
DialogFlow: https://dialogflow.com/, Wit.ai: https://wit.ai/, Amazon Lex: https://aws.amazon.com/lex/, IBM Watson https://www.ibm.com/watson/.
- 3.
- 4.
Study materials and in-depth results available at https://tinyurl.com/25ad8jv6.
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Bouguelia, S., Brabra, H., Zamanirad, S., Benatallah, B., Baez, M., Kheddouci, H. (2021). Reusable Abstractions and Patterns for Recognising Compositional Conversational Flows. In: La Rosa, M., Sadiq, S., Teniente, E. (eds) Advanced Information Systems Engineering. CAiSE 2021. Lecture Notes in Computer Science(), vol 12751. Springer, Cham. https://doi.org/10.1007/978-3-030-79382-1_10
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