Superimposition of natural language conversations over software enabled services

Digital assistants and their instantiation in the form of messaging or chat bots, software robots, virtual assistants, have become the quintessential engine for understanding user needs, expressed in natural language, and on fulfilling such needs by invoking the appropriate back-end software services. The continuous improvement in Natural Language Processing (NLP), Artificial Intelligence (AI), messaging interfaces and devices allow natural language-based interactions between users and a deluge of software enabled services including interactions with “data sources”, “applications”, “resources” and “physical assets” (e.g., sensors). Increasingly, organisations leverage digital assistants to increase productivity and automate business processes in various application domains including office tasks, travel, healthcare, and e-government services.Nonetheless, despite the early adoption, digital assistant technologies are still only in their preliminary stages of development, with several unsolved theoretical and technical challenges stemming from the lack of effective support for wide range of possibly ambiguous user intents and to leverage the large and growing number of services. More specifically, the lack of latent knowledge to represent the different types of software services and the lack for supporting complex interactions between users and services inhibit design and engineering of effective and efficient techniques that harness the full potential of natural interactions between users and software enabled services.This thesis advances the fundamental and practical understanding of natural language based conversations between users, resources, services and devices. In this thesis we build upon ad- vances in NLP and entity recognition and devise novel concepts and techniques to address important shortcomings in natural-language based conversational systems. Inspired by word embeddings, their extensions and impacts, we develop novel vector space models and techniques tocapture, represent and reason about rich latent knowledge about user intents, semi-structured and textual artefacts (e.g., emails), data structures (e.g., attributes in an indexing schema over data sources) and API elements (e.g. API methods) to support potentially ambiguous natural language user requests and tasks. We develop extended state-machine based models to capture conversation patterns among users and services. We provide validation and evaluation of the proposed models and techniques

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