Matteo posted: " Chatbots are a type of software that enables machines to communicate with humans in a natural, conversational manner. Chatbots have numerous uses in different industries as they answer FAQs, communicate with customers, and provide better insights abou"
Chatbots are a type of software that enables machines to communicate with humans in a natural, conversational manner. Chatbots have numerous uses in different industries as they answer FAQs, communicate with customers, and provide better insights about customers' needs. With ~50% of large enterprises considering to invest in chatbot development, it is important to understand the underlying architecture of chatbots in order to reap the most of their benefits.
In this article, we explore how chatbots work, their components, and the steps involved in chatbot architecture.
How do chatbots work?
In simple words, chatbots aim to understand users' queries and generate a relevant response to meet users' needs. Simple chatbots scan users' input sentences for general keywords, skim through their predefined list of answers, and provide a response relevant to the user's query. On the other hand, more modern chatbots rely on AI and natural language processing (NLP) to recognize users' intent from the context of their input and generate correct responses.
Chatbots can be divided into 3 types based on the response-generation method:
Rule-based chatbots: Rule-based chatbots rely on if/then logic to generate responses based on predefined conditions and responses. These chatbots have limited customization capabilities but are reliable and are less likely to go off the rails.
AI-based chatbots: AI-enabled chatbots rely on NLP to scan users' queries and recognize keywords to determine the right way to respond. Additionally, some AI-based chatbots self-improve by using users' data as new training data in order to expand the knowledge database and improve their responses.
Hybrid chatbots: Hybrid chatbots rely both on rules and NLP to understand users and generate responses. These chatbots' databases are easier to tweak but have limited conversational capabilities compared to AI-based chatbots.
To explore in details, feel free to read our in-depth article on chatbot types.
What are the components of a chatbot?
Typically, chatbots consist of 7 components, and they are structured as following:
Natural language processing
Natural language processing (NLP) enables chatbots to convert users' text and speech into structured data to be understood by a machine. NLP consists of the following steps:
Tokenization: also called lexical analysis, is the process of splitting the string of words forming a sentence into smaller parts "tokens" based on its meaning and its relationship to the whole sentence.
Normalization: also called syntactic analysis, is the process of checking words for typos and changing them into the standard form. For example, the word "tmrw" will be normalized into "tomorrow",
Entity recognition: the process of looking for keywords to identify the topic of the conversation.
Semantic analysis: the process of inferring the meaning of a sentence by understanding the meaning of each word and its relation to the overall structure.
Natural language understanding
Natural language understanding (NLU) is a subfield of NLP which focuses on understanding the meaning of human speech by recognizing patterns in unstructured speech input. NLU solutions have 3 components:
Dictionary to determine the meaning of a word
Parser to determines if the syntax of the text conforms to the rules of the language
Grammar rules to break down the input based on sentence structure and punctuation
NLU enables chatbots to classify users' intents and generate a response based on training data.
Knowledge base
A knowledge base is a library of information that the chatbot relies on to fetch the data used to respond to users. Knowledge bases differ based on business needs. For instance, the knowledge base of an e-commerce website chatbot will contain information about products, features, and prices, whereas a knowledge base of a healthcare chatbot will have information about physicians' calendars, hospital opening hours, and pharmacy duties. Additionally, some chatbots are integrated with web scrapers to pull data from online resources and display it to users.
Data storage
Chatbot developers may choose to store conversations for customer service uses and bot training and testing purposes. Chatbot conversations can be stored in SQL form either on-premise or on a cloud.
Dialog manager
A dialog manager is the component responsible for the flow of the conversation between the user and the chatbot. It keeps a record of the interactions within one conversation in order to decide how to respond. For instance, if the user says "I want to order strawberry ice cream" and then within the conversation says "change my order to chocolate ice cream", the dialog manager will enable the bot to detect the change from "strawberry" to "chocolate" and change the order accordingly.
Source: CM.com
Natural language generation
Natural language generation (NLG) is the process of transforming machine-produced structured data into human-readable text. After understanding users' intent, NLG has 4 steps to generate a response:
Content determination: Filtering existing data in the knowledge base to choose what to include in the response.
Data interpretation: Understanding the patterns and answers available in the knowledge base.
Document planning: Structuring the answer in a narrative manner.
Sentence aggregation: Compiling the expressions and words for each sentence in the response.
Grammaticalization: Applying grammar rules such as punctuations and spell check.
Language implementations: Inputting the data into language templates to ensure a natural representation of the response.
User interfaces
Conversational user interfaces are the front-end of a chatbot that enable the physical representation of the conversation. They are classified into text-based or voice-based assistants. And they can be integrated into different platforms, such as Facebook Messenger, WhatsApp, Slack, Google Teams, etc.
Sponsored:
RMA insurance company integrated CM.com chatbot to their WhatsApp to communicate with customers. In the following video, you can watch a customer's journey communicating with the chatbot.
Source: CM.com
What are the best practices of chatbot development?
Best practices of the chatbot development process are:
Identifying target audience and understanding their needs
Setting realistic goals about chatbot implementation
Understanding which business area will benefit most from the chatbot
Selecting the right user platform
Improving usability and reachability of chatbot
To read in more details, feel free to read Top Chatbot Development Best Practices in 2021.
For more on chatbots
To explore chatbot success and fail stories, as well as business adoption and market statistics, feel free to read our article:
If you are interested in different chatbot use cases, feel free to read our in-depth articles:
And if you believe your business will benefit from a conversational AI solution, scroll down our data-driven lists of vendors:
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