In this tutorial, we’ll take a look at what a smart chatbot means. Then, we’ll go over the architecture of a smart chatbot and how it works. Finally, we’ll walk through the steps to building a chatbot capable of carrying on a meaningful conversation.
2. What Is a Chatbot?
A chatbot is an AI-based software that conducts conversations. That means it interacts with humans in their natural language. These chatbots typically communicate via voice or text, and they can easily mimic human languages to communicate with humans in a human-like manner. One of the first and most important goals of a chatbot is to resemble an intelligent person and make it difficult for others to understand their true nature, as shown here:
Chatbots use a question-and-answer pattern to manage conversations. Whereas artificial intelligence and machine learning are used to recognize the question and deliver an appropriate answer.
3. Different Types of Chatbots
Artificial intelligence and machine learning represent the heart of chatbots’ functioning. In general, a chatbot recognizes requests by comparing incoming user questions to predefined instructions. It accomplishes this by running the requests through advanced algorithms and then responding appropriately. This leads to classifying chatbots into two categories based on how they analyze input and reply to it. Let’s take a look at the two types of chatbots:
- Rule-based approach: This approach instructs a chatbot to answer questions based on a set of pre-determined rules that it was initially trained on. These set rules can be extremely simple or highly complex. While rule-based chatbots are capable of handling simple queries, they frequently fail to handle more complex queries/requests
- Self-learning approach: As the name implies, they are chatbots that can learn on their own. These use advanced technologies such as Artificial Intelligence and Machine Learning to train themselves based on instances and behaviors. Naturally, these chatbots are far more intelligent than rule-based bots. Let’s take a look at the two types of Self-learning bots: retrieval-based bots and generative bots
3.1. Retrieval-Based Chatbot
A chatbot that works with predefined input and predefined responses is known as a retrieval-based chatbot. Once the question/pattern is entered, the chatbot employs a heuristic approach to deliver the appropriate response. These chatbots only respond to predefined questions and do not generate new content. The retrieval-based model is commonly used to create goal-oriented chatbots with customized features such as the bot’s flow and tone. Here’s an example of how a retrieval-based chatbot would respond to a user’s query:
3.2. Generative Chatbot
In contrast to retrieval-based chatbots, generative chatbots are generally open-source chatbot programs. Rather than selecting from pre-defined responses, it generates unique and new responses without requiring an extensive database of examples. If the model is appropriately trained, it is an easy task to generate novel responses. Here’s an example of how a generative chatbot would respond to a user’s query:
Chatbot has become an essential customer interaction tool that allows for instant messaging between the brand and the customer. Consider Siri from Apple, Cortana from Microsoft, and Alexa from Amazon. Aren’t these wonderful? Don’t you want to learn how to make your own chatbot?
4. Chatbot Architecture Diagram
A chatbot is made up of many components that work together to achieve a common objective. Let’s take a look at the relationships between the various components of a conversational agent:
When the chatbot receives a new message, the language identification module is the first to handle it. This might include anything from simple tag retrieval to more complex statistical techniques. Then the intent classifier module receives three inputs which are the new message, along with the language and any previous discussion messages obtained from the backend. The Intent classifier module is responsible for inferring the user’s purpose.
The message’s metadata inferred intent, and other backend data will then be utilized to identify a suitable action or series of actions. For example, if the intent is still unclear, a chatbot may choose to respond with a question, or it may choose to reactivate a user account if the user’s intent is to ask permission to do so.
Finally, the action handler module accepts an action as input and executes it appropriately. This is advantageous because the same action may be carried out in many ways depending on the agent’s surroundings. For example, depending on whether the bot works on a company’s website or the Messenger platform, the performed action might be totally different.
5. Chatbot Architecture and Classification Methods
Let’s understand the workflow of these bots in order to build a chatbot that offers appropriate outcomes.
5.1. Classification Methods
The performance of chatbots is based on three classification methods.
- Patter matches: This method uses pattern matches to classify the text and respond appropriately to users. These patterns are based on the standard model of Artificial Intelligence Markup Language (AIML). Here’s an example:
We use these patterns to match parts of user messages. To provide a reasonable response, a remarkable pattern must be available in the database for each type of question.
- Natural Language Understanding (NLU): This approach is composed of three concepts:
- Entity: This concept represents the main idea of the chatbot. For example, a chatbot for an online store may have an order tracking system or payment system as an entity
- Context: When the algorithm of NLU analysis a sentence, it doesn’t have the history of the previous query of the user in a conversation. So, it understands the message based on the context, such as “Restaurant” and “Ordering Pizza”. This concept helps the bot to extract intents without relying on previous messages
- Intent: This concept means the action or the intent from the chatbot in response to the message. In this step, the bot will give the same answer to the user against queries, such as ” I want a white bag”, ” show me a white bag” and “I want to purchase a white bag” since they trigger the same command “white bag”
Here’s an example of a chatbot architecture using the NLU method:
- Natural Language Processing (NLP): This approach is used by intelligent AI-based Chatbots to understand human commands (voice and text) and learn from experience. So, it figures out how to turn the text or speech of a user into structured data. As a result, the structured data will be used to select the appropriate answer. Here are the steps of Natural Language Processing:
- Tokenization: This concept divides a string of words into pieces, which are known as tokens. These tokens have a linguistic representation with a different value for the application
- Normalization: It examines the text for typographical errors or misspellings that could alter the true intent of the users’ message
- Entity Recognition: This is where the program interprets the text to determine what the topic of discussion is. It identifies the entity by looking for a similar category of words, user data, or any other necessary information
- Dependency Parsing: It scans the text for verbs, nouns, subjects, common phrases, and objects to identify any relevant information conveyed by the user
- Sentiment Analysis: It investigates and evaluates the user’s experience (sentiment). If necessary, it will then hand over the inquiry to a human
Here’s an example of a chatbot architecture using the NLP method:
5.2. Language Identification
In some instances, determining the language of a text is an essential initial step in a broader natural language processing chain. Some languages even share homographs (for example, room, which appears in both English and Dutch despite having a different meaning in each language). This can cause algorithms to become confused about the semantics of these words and necessitates the need to identify the correct language for a given text before processing it further.
5.3. Intent Classification
The conversational agent must be able to recognize the goal the user is attempting to achieve when it receives a new message. This is often modeled as a multiclassification issue, with labels corresponding to the names of the possible user intentions. To address this issue, various techniques are available, ranging from basic keyword extraction to Bayesian inference. However, these techniques employ a large number of messages to identify the user’s request.
5.4. Knowledge Management
Without knowledge, an intelligent agent can only accomplish so much. Knowledge engineering is highly beneficial for conversational bots, such as answering simple queries about broad facts. Siri and Amazon Alexa utilize internal knowledge inference methods to obtain information from the web and other sources (for example, asking Siri about trains departing from Tunis today may result in an internal inference operation of the type train(Tunis, D, today), where D is an anonymous destination). Nowadays, knowledge management is primarily accomplished through API calls and optimized database requests.
5.5. Responses Generation
A conversational agent must be able to respond in order to converse. Furthermore, the responses must be coherent in relation to the context of the discourse. This problem may be solved by combining two modules: one that creates a list of potential responses and the other that chooses or ranks them based on certain criteria. The two most prevalent methods to this problem are retrieval-based and generative-based strategies. Here are some examples of generated answers using a bot:
6. How to Create a Chatbot
Let’s go through the fundamentals of creating a purpose-driven chatbot:
- Define goals for the chatbot: The first step is to identify the chatbot’s goals. Why are you creating a chatbot? What are you aiming to achieve? The answers to these questions will facilitate the selection of the chatbot’s type.
- Select a communication channel: Here, we need to select a platform that we are comfortable with. We might, for example, create a chatbot for a website or mobile app. Similarly, we may link a chatbot with Skype, Facebook Messenger, or any other messaging service, as well as SMS channels.
- Select the technology stack: The frameworks will most likely be chosen based on the developers’ skills as well as the availability of open-source and third-party NLP (Natural Language Processing) libraries such as ChatterBot.
- Design the conversation flow: Then, we need to write the dialogue. Designing the conversational flow means organizing the dialogue and framing the bot’s responses. So, a ‘happy flow’ is a conversation in which everything happens as it should.
- Train the chatbot: This step focuses on teaching the bot different variants that the user may ask. This step is critical for understanding the user’s intent. We can do this by training the bot on existing data sets such as support tickets, emails, and so on. We can also obtain a third-party data set containing the information needed by the bot, such as the NPS Chat Corpus, the Question-Answer Dataset, or any chatbot dataset for machine learning.
- Test the chatbot: Because the chatbot is still in its early stages, it is best to test it using various scenarios with graceful general responses that steer the conversation toward actual goals.
- Implementation of the chatbot: The deployment of a chatbot does not take long. Simply put, we need to ensure that all endpoints are linked and that the bot is integrated into the entire infrastructure (using an ERP, CRM, or similar software system). To keep users satisfied, we need to review the statistics and improve the responses.
In this article, we’ve discussed the fundamentals of a smart chatbot and how understanding its architecture can help develop an intelligent bot capable of carrying on a meaningful conversation. We’ve also gone over creating a smart chatbot that can serve as the primary source of customer support or supplement human staff.