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    Conversational artificial intelligence - demystifying statistical vs linguistic NLP solutions

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    Publication date
    2020-05
    Author
    Panesar, Kulvinder
    Keyword
    Conversational artificial intelligence
    Knowledge representation
    Machine/deep learning
    Natural language understanding
    Natural language processing
    Role and reference grammar
    Rights
    © 2020 The Author. Published by Universitat Politecnica de Valencia. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. (http://creativecommons.org/licenses/by-nc-nd/4.0/).
    Peer-Reviewed
    yes
    
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    Abstract
    This paper aims to demystify the hype and attention on chatbots and its association with conversational artificial intelligence. Both are slowly emerging as a real presence in our lives from the impressive technological developments in machine learning, deep learning and natural language understanding solutions. However, what is under the hood, and how far and to what extent can chatbots/conversational artificial intelligence solutions work – is our question. Natural language is the most easily understood knowledge representation for people, but certainly not the best for computers because of its inherent ambiguous, complex and dynamic nature. We will critique the knowledge representation of heavy statistical chatbot solutions against linguistics alternatives. In order to react intelligently to the user, natural language solutions must critically consider other factors such as context, memory, intelligent understanding, previous experience, and personalized knowledge of the user. We will delve into the spectrum of conversational interfaces and focus on a strong artificial intelligence concept. This is explored via a text based conversational software agents with a deep strategic role to hold a conversation and enable the mechanisms need to plan, and to decide what to do next, and manage the dialogue to achieve a goal. To demonstrate this, a deep linguistically aware and knowledge aware text based conversational agent (LING-CSA) presents a proof-of-concept of a non-statistical conversational AI solution.
    URI
    http://hdl.handle.net/10454/18121
    Version
    Published version
    Citation
    Panesar K (2020) Conversational artificial intelligence – demystifying statistical vs linguistic NLP solutions. Journal of Computer-Assisted Linguistic Research. 4: 47-79
    Link to publisher’s version
    https://doi.org/10.4995/jclr.2020.12932
    Type
    Article
    Collections
    Engineering and Informatics Publications

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