Conversational artificial intelligence - demystifying statistical vs linguistic NLP solutions

[EN] 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. Panesar, K. (2020). Conversational artificial intelligence - demystifying statistical vs linguistic NLP solutions. Journal of Computer-Assisted Linguistic Research. 4(1):47-79. https://doi.org/10.4995/jclr.2020.12932 AbuShawar, Bayan, and Eric Atwell. 2015. "ALICE chatbot: Trials and outputs." Computación y Sistemas 19 (4):625-632. https://doi.org/10.13053/cys-19-4-2326 Alammar, Jay 2018. "Visualizing A Neural Machine Translation Model (Mechanics of Seq2seq Models With Attention)." accessed 12 December 2019. https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/. Bahdanau, Dzmitry, Jan Chorowski, Dmitriy Serdyuk, Philemon Brakel, and Yoshua Bengio. 2016. "End-to-end attention-based large vocabulary speech recognition." 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP). https://doi.org/10.1109/ICASSP.2016.7472618 Ball, John. 2019. "The Problem with AI State of th