Building and Integrating a Chatbot in 30 minutes Visagan Santhanam, Lead Engineer Muhammed Aslam, Information Architect Unisys India December 2018

Disclaimer The contents and opinions expressed in the following slides and during this presentation session are purely the views of the presenters and not necessarily that of Unisys. All third-party trademark and registered trademark rights are duly acknowledged and due credit is given.

Microsoft BoB 1995- 1996 Microsoft Bob presented screens showing a "house", with "rooms" that the user could go to containing familiar objects corresponding to computer applications—for instance, a desk with pen and paper, a checkbook, and other items. In this case, clicking on the pen and paper would open the word processor.

Clippy 1997- 2007 Clippy: Love it or hate it, everyone had an opinion about it. Despite the wide range of user opinions, this helpful paperclip was a forerunner in the area of chatbots.

Why Chatbots? Give me only one answer Contrary to just publishing the information, people who use a chatbot can get to the information they desire more directly by asking questions. chatbot can guide the user to the most relevant answer, instead of presenting a set of texts that might contain the correct answer. More personalized user experience. Personalized User Assistance and Help Content Interactive experience. More efficient than search

Chatbot ready content It makes sense for chatbots to be a channel for our existing content, rather than give us extra work to do. Write content in a way that it can be used in by a chatbot as well. In other words, write transformable content. Underlying semantic structure of Chatbots can be mapped to the existing structured content. Metadata and taxonomy in a structured content help the chatbot know which piece of information to serve the user.

Write at a Granular Level With chatbots using question and answer key pairs as their ground truth, we could use this as our basis for writing content. We might have Help topics that contain the specific answer to a specific question, with metadata and taxonomies to describe the context for when it applies.

Building a Chatbot Build your own AI engine Use an existing solution AIML, or Artificial Intelligence Markup Language

Input for Chatbots Unstructured content Custom Question & Answers Semi-structured content (FAQ, Manuals) Unstructured: Platforms such as IBM Watson can process a corpus of unstructured content.

Major Solutions Enterprise Chabot solutions with Built-in NLP

Enterprise Chatbots features Delivers natural and rich conversational experiences Works with an array of platforms Cross-device support Support 14+ languages Built-in Analytics Less Coding*

Architecture

Chatbot Terminologies Intent An Intent is a specific action that the user can invoke by using one of the defined terms in the console. For example, the user could ask “Is it going to rain today?” or “Where is the nearest pizza restaurant”. Entities An Entity is a property which can be used by chatbot engine to answer the request from the user — the entity will usually be a keyword within the request such as a name, date, location etc. Many chatbots has already contains a set of pre-defined system entities which can be used when constructing intent.

Chatbot Terminologies Response The response is the content which chatbot will deliver to our user once the request for fulfilment has completed. On devices with screens this will consist of textual content and rich content if present  the textual content will be spoken to the user. On hardware devices without screens the content will only be read out to the user. Context The Context is used to keep a reference to parameter values as the user moves between intents throughout the conversation. Context is a powerful concept as it allows us to make decisions in our responses based off of these previous responses, repair conversations that may become broken for any reason and also branch off into different intents to create a fluid conversation with the user.

Demo Create Agent Add Intent Add Response Publish Integrate

Q&A Visagan Santhanam Visagan.santhanam@unisys.com linkedin.com/in/visagan-s-8830b911 Muhammed Aslam Muhammed.Paravankuzhiyil@unisys.com linkedin.com/in/muhammed-aslam-5724b521