If the word “chatbot” conjures memories of frustrating and unnatural conversations, worry not. The chatbots of today are sleek and sophisticated. In fact, with the use of machine learning technology, they can even feel human.
These AI-powered chatbots use a branch of AI called natural language processing (NLP) to provide a better user experience. Often referred to as virtual agents or intelligent virtual assistants, these NLP chatbots help human agents by taking over repetitive and time consuming communications. This frees up the human agent to concentrate on those more complex cases that require human input. But how does this all work? Understanding how your NLP text-based chatbot operates will help you ensure it stays on track.
What’s the difference between NLP, NLU, and NLG?
There are several acronyms in the world of automation and AI that are relevant for understanding how your virtual agent or NLP chatbot works. Here are three key terms that you need to know:
NLP, or Natural Language Processing, is a branch of AI that helps computers read and understand natural human language. Its main goal is to improve human-machine communication.
NLU, or Natural Language Understanding, is a branch of NLP. It is all about machine reading comprehension and making sure the machine understands the text’s actual meaning. In more scientific terms, NLU takes place when the machine converts the user’s inputted data (what they’re saying) into a logical form that the computer's algorithms understand. And the more accurate and reliable an AI engine is in identifying a user’s intent, the more powerful the solution that it drives will be.
NLG, or Natural Language Generation, is another subset of NLP, which is essentially NLU in reverse: the machine generates a logical response which it then converts to a natural language response that a human reader can easily understand.
Rule-based chatbots: The old-school solution
The stilted, buggy chatbots of yesteryear are called rule-based chatbots.These bots aren't very flexible in how they interact with your users because they're based on simple keywords or pattern matching.
So, take this question, for example: “What is the price of your membership?”
This question can be matched with similar questions that will be asked by customers in the future. The rule-based chatbot is taught how to respond to these questions—but the wording must be an exact match. This means manually programming all the different ways to ask how much a membership costs, for every possible question a customer may ask, which is incredibly time-consuming.
That’s not to say this type of chatbot can’t be useful: if your company tends to get only a certain number of questions that are usually asked in just a few ways, then a simple rule-based chatbot is probably the way to go. But for many companies these days, this technology is not powerful enough to keep up with their customer queries.
NLP-powered chatbots: The next generation of virtual agents
The new generation of chatbots are NLP-powered virtual agents that get smarter each day. They keep track of information throughout the conversation and learn as they go. Here are some of the most important elements of an NLP-powered chatbot:
Dialogue Management: This tracks the state of the conversation. The core components of dialogue management in AI chatbots include a context—saving and sharing data exchanged in the conversation—and session—one conversation from start to finish.
Human Handoff: This refers to the seamless communication and execution of a handoff from the AI chatbot to a human agent.
Business Logic Integration: It’s important that your chatbot has been programmed with your company’s unique business logic.
Rapid Iteration: You want your bot to be slick and easily programmable. Rapid iteration refers to the fastest route to the right solution.
Training and Iteration: To ensure your NLP-powered chatbot doesn’t go awry, it’s necessary to systematically train and send feedback to improve its understanding of customer intents using real-world conversation data being generated across channels.
Natural Language Processing: Your chatbot’s NLP works off the following keys: utterances (ways the user refers to a specific intent), intent (the meaning behind the words a user types), entity (details that are important to the intent like dates and locations), context (which helps to save and share parameters across a session), and session (one conversation from start to finish, even if interrupted).
Simplicity: To get the most out of your virtual agent, you’ll want it to be set up as simply as possible, with all the functionality that you need—but no more than that. There is, of course, always the potential to upgrade or add new features as you need later on.
How AI chatbots and virtual agents work
Now it’s time to really get into the nitty-gritty of how today’s AI chatbots work. There are five major steps involved—tokenizing, normalizing, recognizing entities, dependency parsing, and generation—for the chatbot to read, interpret, understand, and formulate and send a response. Let’s take a closer look.
- Tokenizing: The chatbot starts by chopping up text into pieces (also called ‘tokens’) and removing punctuation.
- Normalizing: Next, the bot removes details that aren't relevant and converts words to their "normal" version, for example by making everything lowercase.
- Recognizing Entities: Now that the words are all normalized, the chatbot seeks to identify which type of thing is being referred to. For example, it would identify North America as a location, 67% as a percentage, and Google as an organization.
- Dependency Parsing: For the next step, the bot identifies the role each word plays in the sentence, such as noun, verb, adjective, or object.
- Generation: Finally, the chatbot generates a number of responses using the information determined in all the other steps and selects the most appropriate response to send to the user.
Caring for your virtual agent
One of the most impressive things about virtual agents is that they get smarter with each interaction. However, in the beginning, NLP chatbots are still learning and should be monitored carefully. It can take some time to make sure the NLP technology understands your users and provides the right responses.
Now that you understand the inner workings of NLP, machine learning, and AI-powered chatbots, you’re ready to build and deploy your virtual agent on the frontlines of your customer support team.