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Let’s start with how a chatbot typically works before diving into some of the frameworks.
- Understand: The first step for any chatbot is to understand the user input. This is made possible using pattern matching and intent classification techniques. ‘Intents’ are the tasks that users might want to perform with a chatbot. Machine learning, NLP and speech recognition techniques are typically used to identify the intent of the message and extract named entities. Entities are the specific pieces of information extracted from the user’s response i.e. the content associated with an intent.
- Respond: After understanding, the next goal is to generate a response. This is based on the current input message and the context of the conversation. After specifying the intents and entities, a dialog flow is constructed. This is basically the replies/feedback expected from a chatbot.
- Learn: Chatbots use AI techniques such as natural language understanding and pattern recognition to store and distinguish between the context of the information provided, and elicit a suitable response for future replies. This is important because different requests might have different meanings depending on previous requests.
Top chatbot development frameworks
A bot development framework is a set of predefined classes, functions, and utilities that a developer can use to build chatbots easier and faster. They vary in the level of complexity, integration capabilities, and functionalities. Let us look at some of the development platforms utilized for chatbot building.
API. AI, a code based framework with a simple web-based interface, allows users to build engaging voice and text-based conversational apps using a large number of libraries and SDKs including Android, iOS, Webkit HTML5, Node.js, and Python API. It also supports nearly 32 one-click platform integrations such as Google, Facebook Messenger, Twitter and Skype to name a few.
API. AI makes use of an agent – a container that transforms natural language based user requests into actionable data. The software tries to find the intent behind a user’s reply and matches it to the default or the closest match. After intent matching, it executes the actions and responses the developer has defined for that intent. API.AI also makes use of entities. Once the intents and entities are specified, the bot is trained. API.AI’s training module efficiently tracks each user’s request and lets developers see how they are parsed and matched to an intent. It also allows for correction of any errors and change requests thus retraining the bot.
API.AI streamlines the entire bot-creating process by helping developers provide domain-specific knowledge that is unique to a bot’s needs while working on speech recognition, intent and context management in the backend.
Google has recently partnered with API.AI to help them build conversational tools like Apple’s Siri.
Microsoft Bot Framework
Microsoft Bot Framework allows building and deployment of chatbots across multiple platforms and services such as web, SMS, non-Microsoft platforms, Office 365, Skype etc. The Bot Framework includes two components – The Bot Builder and the Microsoft Cognitive Services.
The Bot Builder comprises of two full-featured SDKs – for the.NET and the Node.js platforms along with an emulator for testing and debugging. There’s also a set of RESTful APIs for building code in other languages. The SDKs support features for simple and easy interactions between bots. They also have a large collection of prebuilt sample bots for the developer to choose from.The Microsoft Cognitive Services is a collection of intelligent APIs that simplify a variety of AI tasks such as allowing the system to understand and interpret the user’s needs using natural language in just a few lines of code. These APIs allow integration to most modern languages and platforms and constantly improve, learn, and get smarter.
Developers can build bots in the Bot Builder SDK using C# or Node.js. They can then add AI capabilities with Cognitive Services. Finally, they can register the bots on the developer portal, connecting it to users across platforms such as Facebook and Microsoft Teams and also deploy it on the cloud like Microsoft Azure. For a step-by-step guide for chatbot building using Microsoft Bot Framework, you can refer to one of our books on the topic.
Sabre Corporation, a customer service provider for travel agencies, have recently announced the development of an AI-powered chatbot that leverages Microsoft Bot Framework and Microsoft Cognitive Services.
IBM’s Watson Conversation helps build chatbot solutions that understand natural-language input and use machine learning to respond to customers in a way that simulates conversations between humans. It is built on a neural network of one million Wikipedia words. It offers deployment across a variety of platforms including mobile devices, messaging platforms, and robots. The platform is robust and secure as IBM allows users to opt out of data sharing. The IBM Watson Tone Analyzer service can help bots understand the tone of the user’s input for better management of the experience.
The basic steps to create a chatbot using Watson Conversation are as follows.
- We first create a workspace – a place for configuring information to maintain separate intents, user examples, entities, and dialogues for each application. One workspace corresponds to one bot.
- Next, we create Intents. Watson Conversation makes use of multiple conditioned responses to distinguish between similar intents. For example, instead of building specific intents for locations of different places, it creates a general intent “location” and adds an entity to capture the response, like the “location- bedroom” – to the right, near the stairs, “location-kitchen”- to the left.
- The third step is entity establishment. This involves grouping entities that might trigger a similar response in the dialog.
- The dialog flow, thus generated after specifying the intents and entities, goes through testing followed by embedding this into an application. It is then connected with other services by using the conversation API.
CXP Designer and Aspect NLU
Aspect Customer Experience Platform is an application lifecycle management tool to build text and voice-based applications such as chatbots. It provides deployment options across multiple communication channels like text, voice, mobile web and social media networks.
The Aspect CXP typically includes a CXP designer to build chatbots and the inbuilt Aspect NLU to provide advanced natural language capabilities.
CXP designer works by creating dialog objects to provide a menu of options for frontend as well as backend. Menu items for the frontend are used to create intents and modules within those intents. The developer can then modify labels (of those intents and modules) manually or use the Aspect NLU to disambiguate similar questions for successful extraction of meaning and intent. The Aspect NLU includes tools for spelling correction, linguistic lexicons such as nouns, verbs etc. and options for detecting and extracting common data types such as date, time, numbers, etc. It also allows a developer to modify the meaning extraction based on how they want it if they want it!
CXP designer also allows skipping of certain steps in chatbots. For instance, if the user has already provided their tracking id for a particular package, the chatbot will skip the prompt of asking them the tracking id again.
Another popular chatbot development platform worth mentioning is the Facebook messenger with over 100,000 monthly active bots, but without cross-platform deployment features. The above bot frameworks are typically used by developers to build chatbots from scratch and require some programming skills. However, there has been a rise in automated bot development tools of late. Some of these include Chatfuel and Motion AI and typically involve drag and drop functionalities. With such tools, beginners and non-programmers can create and deploy chatbots within few minutes. But, they lack the extended functionalities supported by typical code based frameworks such as the flexibility to store data, produce analytics or incorporate customized AI tasks. Every chatbot development system, whether framework or tool, serves a different purpose. Choosing the right one depends on the type of application to build, organizational needs, and the developer’s expertise.