International Workshop on Conversational Agents and Chatbots with Machine Learning (ChatbotML 2018)

 

 

A Workshop at

2018 IEEE International Conference on Big Data (IEEE Big Data 2018)

December 10-13, 2018, Seattle, WA, USA

 

 

With support from the Technical Committee on Cyber- Physical Cloud Systems of IEEE Systems, Man, and Cybernetics Society

 

Introduction

 

A chatbot (also known as a talkbot, chatterbot, Bot, IM bot, interactive agent, or Artificial Conversational Entity) is a computer program or an artificial intelligence which conducts a conversation via auditory or textual methods. Such programs are often designed to convincingly simulate how a human would behave as a conversational partner, thereby passing the Turing test. Chatbots are typically used in dialog systems for various practical purposes including customer service or information acquisition. Some chatterbots use sophisticated natural language processing systems, but many simpler systems scan for keywords within the input, then pull a reply with the most matching keywords, or the most similar wording pattern, from a database.

 

Interacting with the machine via natural language is one of the requirements for general artificial intelligence. This field of AI is called dialogue systems, spoken dialogue systems, or chatbots. The machine needs to provide you with an informative answer, maintain the context of the dialogue, and be indistinguishable from the human

 

There are generally two types of chatbots, namely, Retrieval-Based and Generative Models. Retrieval-based models use a repository of predefined responses and some kind of heuristic to pick an appropriate response based on the input and context. The heuristic could be as simple as a rule-based expression match, or as complex as an ensemble of Machine Learning classifiers. These systems don’t generate any new text; they just pick a response from a fixed set. On the contrary, generative models don’t rely on pre-defined responses. They generate new responses from scratch. Generative models are typically based on Machine Translation techniques, but instead of translating from one language to another, we “translate” from an input to an output (response).

 

Some common challenges include: incorporating context, coherent personality, evaluation of models, intention and diversity. Machine Learning represents promising opportunities for building and optimizing conversational agents and chatbots.

 

This Workshop will focus on the cutting-edge developments of applying machine learning to the building and optimization of conversational agents and chatbots.

 

This workshop is timely and interesting for researchers, academics and practitioners in conversational agents and chatbots and real-world domain applications. The workshop is very relevant to the big data driven artificial intelligence community, and will bring forth a lively forum on this exciting and challenging area at the conference.

 

Research Topics

 

The workshop only considers well-written manuscripts that describe original, unpublished, state-of-the-art research and practical work. Indicative topics for the workshop are as follows.

 

Learning systems for conversational agents / chatbots

 

-          machine learning based models and architectures of conversational agents / chatbots

-          natural language processing for learning based conversational agents / chatbots

-          computational linguistic models for learning based conversational agents / chatbots

-          software development methodologies of learning based conversational agents / chatbots

-          open frameworks and platforms of learning based conversational agents / chatbots

 

Machine learning techniques for conversational agents / chatbots

 

-          deep learning for conversational agents / chatbots

-          data mining for conversational agents / chatbots

-          data analytics for conversational agents / chatbots

-          neural networks for conversational agents / chatbots

-          stochastic learning models for conversational agents / chatbots

-          reinforcement learning for conversational agents / chatbots

 

Applications of conversational agents / chatbots

 

-          conversational agents / chatbots in finance & banking services

-          conversational agents / chatbots in customers & marketing services

-          conversational agents / chatbots in medical & healthcare services

-          conversational agents / chatbots in tourism and public services

-          conversational agents / chatbots in personalized education

-          conversational agents / chatbots for the elderly

-          conversational agents / chatbots for autism

 

To contribute towards advances of knowledge, the workshop solicits original manuscripts from researchers and practitioners who are actively working in Conversational Agents and Chatbots with Machine Learning.

 

Submission webpage

 

Each submission will be peer reviewed by 3 TC members.

 

Important Dates

 

Oct 10, 2018:                    Due date for full workshop papers submission

Nov 1, 2018:           Notification of paper acceptance to authors

Nov 15, 2018:                   Camera-ready of accepted papers

Dec 10-13   2018:     Workshops

 

Workshop Program Chair

 

Professor Huaglory Tianfield

Professor of Computing, Ph.D.

Director, Artificial Intelligence Research Lab

Department of Communications, Computer and Interactive Systems

Glasgow Caledonian University

United Kingdom

E-mail

 

International Technical Committee