Reusable Abstractions and Patterns for Recognising compositional conversational flows

International audienceTask-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

Context Knowledge-aware Recognition of Composite Intents in Task-oriented Human-Bot Conversations

International audienceTask-oriented dialogue systems employ third-party APIs to serve end-users via natural language interactions. While existing advances in Natural Language Processing (NLP) and Machine Learning (ML) techniques have produced promising and useful results to recognize user intents, the synthesis of API calls to support a broad range of potentially complex user intents is still largely a manual and costly process. In this paper, we propose a new approach to recognize and realize complex user intents. Our approach relies on a new rule-based technique that leverages both (i) natural language features extracted using existing NLP and ML techniques and (ii) contextual knowledge to capture the different classes of complex intents. We devise a context knowledge service to capture the requisite contextual knowledge

Dialogue management in conversational systems: a review of approaches, challenges, and opportunities

International audienceAttracted by their easy-to-use interfaces and captivating benefits, conversational systems have been widely embraced by many individuals and organizations as side-by-side digital co-workers. They enable the understanding of user needs, expressed in natural language, and on fulfilling such needs by invoking the appropriate backend services (e.g., APIs). Controlling the conversation flow, known as Dialogue Management, is one of the essential tasks in conversational systems and the key to its success and adoption as well. Nevertheless, designing scalable and robust dialogue management techniques to effectively support intelligent conversations remains a deeply challenging problem. This article studies dialogue management from an in-depth design perspective. We discuss the state of the art approaches, identify their recent advances and challenges, and provide an outlook on future research directions. Thus, we contribute to guiding researchers and practitioners in selecting the appropriate dialogue management approach aligned with their objectives, among the variety of approaches proposed so far

Dialogue management in conversational systems: a review of approaches, challenges, and opportunities

International audienceAttracted by their easy-to-use interfaces and captivating benefits, conversational systems have been widely embraced by many individuals and organizations as side-by-side digital co-workers. They enable the understanding of user needs, expressed in natural language, and on fulfilling such needs by invoking the appropriate backend services (e.g., APIs). Controlling the conversation flow, known as Dialogue Management, is one of the essential tasks in conversational systems and the key to its success and adoption as well. Nevertheless, designing scalable and robust dialogue management techniques to effectively support intelligent conversations remains a deeply challenging problem. This article studies dialogue management from an in-depth design perspective. We discuss the state of the art approaches, identify their recent advances and challenges, and provide an outlook on future research directions. Thus, we contribute to guiding researchers and practitioners in selecting the appropriate dialogue management approach aligned with their objectives, among the variety of approaches proposed so far

Process-oriented intents: a cornerstone for superimposition of natural language conversations over composite services

International audienceTask-oriented conversational assistants are in very high demand these days. They employ third-party APIs to serve end-users via natural language interactions and improve their productivity. Recently, the augmentation of process-enabled automation with conversational assistants emerged as a promising technology to make process automation closer to users. This paper focuses on the superimposition of task-oriented assistants over composite services. We propose a Human-bot-Process interaction acts that are relevant to represent natural language conversations between the user and multi-step processes. In doing so, we enable human users to perform tasks by naturally interacting with processes