The Best Chatbots Can Handle Natural Language With Grace Learn From Continuous Interaction And Escalate Efficiently When Their Limitations Are Hit

SUMIT PANDEY

A chatbot (sometimes referred to as a chatterbot) is a computer program that conducts a conversation via auditory or textual methods. Such programs are often designed to convincingly simulate how a human would behave as a conversational partner, thereby passing the Turing test.

Chatbots are typically used in dialog systems for various practical purposes including customer service or information acquisition. Some chatbots use sophisticated natural language processing systems, but many simpler ones scan for keywords within the input, then pull a reply with the most matching keywords, or the most similar wording pattern, from a database.

Fields of Natural Language Processing

Natural language processing is a field that focuses on the interactions between computers and humans (or other natural languages). The goal of NLP is to make computers easier to use through the use of natural language processing.

Natural language understanding:

In natural language understanding, the computer program is able to go beyond simply identifying keywords in a sentence. Instead, it is able to make judgments about what exactly the meaning of those words are and how they should be used in a sentence.

This requires some basic understanding of the structure of sentences and how the words within those sentences relate to each other. It also requires the ability for the computer to recognize the different types of words and how they are used.

Natural language generation:

Natural language generation (NLG) is concerned with computer programs that create text or speech that mimic human language to communicate with humans in a natural way.

NLG is related to natural language understanding as NLU systems are designed to understand human speech while NLG systems are designed to generate human speech.

Even though this area is much more advanced than NLU, there are still limitations within the field that need to be addressed such as determining how best to generate text based on content and tone, and how best to evaluate the effectiveness of generated text.

Natural language interaction:

Natural Language Interaction (or NLI) is an area that focuses on dialogue between humans and computers.

This area is related to natural language understanding as well as natural language generation because it deals with both receiving input from humans in a way that can be understood by computers, as well as generating output that humans can understand.

NLI typically involves a two-way exchange of information between a user and a computer system. One part of this exchange would be natural language queries and another would be natural language commands. A command is simply a sentence or sentence fragment that tells the system exactly what actions need to be taken.

Natural language interaction also involves intonation and other types of modifications that would be used in normal human interaction, which means that NLI systems must also be able to discern these types of elements in spoken or written input from users.

Some examples of NLI include chatbot systems, voice command systems and question-answering systems for virtual assistants.

Types of Chatbots

People have many different types of relationships with chatbots today. These relationships span from fully human-like conversations with chatbots such as Xiaoice through short messaging conversations on platforms such as Slack, Twitter, or Facebook all the way down to direct customer service conversations with "abbreviated" chatbots (such as Siri or Alexa) through voice commands.

Each type of conversation provides different opportunities for businesses to engage with their customers at scale across multiple platforms including websites, mobile messaging apps, social media platforms, and smart home devices.

Types of Chatbots

People have many different types of relationships with chatbots today. These relationships span from fully human-like conversations with chatbots such as Xiaoice through short messaging conversations on platforms such as Slack, Twitter, or Facebook all the way down to direct customer service conversations with "abbreviated" chatbots (such as Siri or Alexa) through voice commands.

Each type of conversation provides different opportunities for businesses to engage with their customers at scale across multiple platforms including websites, mobile messaging apps, social media platforms, and smart home devices.

Scripted chatbots

Scripted chatbots are non-automated chatbots that are programmed to respond to specific conversations and interactions. Created using a variety of different programming techniques, including rule-based systems, machine learning and AI, scripted chatbots can be invoked using SMS, voice call, or a website.

Some of the most advanced scripted chatbots are "trained" using machine learning techniques such as reinforcement learning.

Artificially intelligent chatbots

Artificially intelligent chatbots are designed to simulate human conversation and/or emotions through artificial intelligence.

The most advanced artificially intelligent chatbots even demonstrate "learning" behavior when exposed to new stimuli. These bots use natural language processing (NLP), speech recognition, visual recognition and other types of AI technologies (e.g., predictive analytics and machine learning) to simulate how a human would behave as a conversational partner.

Some simple examples include Microsoft's "Tay" chatbot that uses AI to engage with millennials through Twitter and text messaging; Amazon's "Echo" speaker which can be queried to provide information about weather, news, sports scores, etc.; Apple's "Siri" intelligent personal assistant; and Google's "Google Now" intelligent personal assistant.

Challenges For Your Chatbot

1. Limited Natural Language Understanding

2. Limited Natural Language Generation

3. Limited Natural Language Interaction Capability

4. Limited Context Awareness

5. Limited Learning Ability

6. Limited User Engagement Across Channels

How to Use Chatbots in Business?

In recent years, chatbots have come up as a new trend in the business world. The popularity of chatbots is increasing day by day. Chatbots are no more limited to Twitter but are now being used on Facebook, Skype, Slack, Kik, etc.

Let's see how you can use a chatbot for your business.

Customer service chatbot:

A customer service chatbot is designed to answer customer queries in real-time. A customer service chatbot has a knowledge base that provides answers to questions. This knowledge base can be updated manually or automatically. A customer service chatbot can save your support team tons of time and energy by answering your customers’ questions accurately 24/7 without requiring breaks, holidays or paychecks.

Medicine:

Medicine is a booming industry today; people need medical assistance at any point of time and anywhere. This is where chatbots can play an important role. A chatbot in medicine should be capable of providing accurate medical and health-related information to patients and doctors.

This will allow doctors to focus more on their actual jobs rather than spending time with patients answering irrelevant questions. With the power of artificial intelligence, the chatbot can provide suggestions for medicines and procedures based on the patient’s condition and the latest medical techniques.

Tourism:

Tourism is a huge industry that employs millions of people worldwide. But at the same time, it is also an industry where customer satisfaction is important as nobody wants to be treated poorly by hotel staff or travel agents when abroad. An effective chatbot can help the tourism industry by providing information about tours and packages instantly and answering customers’ queries quickly and accurately in order to improve experiences for both tourists and tour operators alike.

Tickets booking:

A chatbot can help you book tickets for events, concerts and shows. It can also be used to find out information on the latest live events in your city. You can ask a chatbot about upcoming shows and concerts or ask your favorite artists about their future plans through a chatbot.

In-app support:

An app is an important part of the business. It is helpful for the business to provide in-app support to the customers. They can resolve issues and help the user get the required information (product-related or not).

A chatbot can answer or guide the user to the correct place where he/she can find answers to his/her questions. This will reduce app abandonment rates.

A chatbot can interact with the customer directly on Facebook Messenger, Skype, WeChat, etc. So that if you are driving on your way to work and forgot to buy something, you can simply message your favorite store and do the same without leaving your cab or bus.

News:

Chatbots are used as news aggregators nowadays. You may have read news on Facebook messenger or WhatsApp but how many of those were machine-generated? With AI advancements, more and more news outlets are using chatbots to deliver news to people.

How to Build a Chatbot Using NLP: 5 Steps to Take

A chatbot is a service powered by artificial intelligence that interacts with users over the internet. The most common place where we see chatbots is social media, where a user can query for information and receive valuable insights in the form of text.

Most of the chatbots are built using natural language processing, which is a branch of artificial intelligence. Natural language processing is a way of analyzing human speech and text, and then coming up with a proper response to what they have heard or read.

Natural language processing is a complex field of study, where we need to take into consideration syntax, semantics, pragmatics, and other such factors.

This section is not an introduction to NLP, but it assumes that you are familiar with its basic concepts. If you are not familiar with these concepts, I suggest you read the following resources:

Business logic analysis:

The first step in building a chatbot is to identify the business requirements. Let's take an example:

Let's say that we have a chatbot for a restaurant booking service. A user can ask the following questions:

1: For how many people?

2: When do you want to go?

3: Which location do you prefer?

4: How many times have you been there before?

5: What is your favorite cuisine?

6: What is your budget per head?

7: Do you mind if the table is inside or outside?

8: What kind of ambience do you prefer (Casual, formal, romantic, etc.)?

9: Do you want alcohol served?

10: Do you want to be picked up from home or at the location?

From this list of questions, we can start identifying the variables that we need to capture in our chatbot. This information is crucial for building a chatbot because it tells us which entities we will have to deal with and what kind of data we need from our users.

For example, the question number 3 tells us that the user will be able to choose from a list of locations. The number 5 tells us that we have to capture the user's budget preference and so on. This information allows us to identify entities like location, cuisine type, budget, etc., which are mentioned in our business logic.

Channel and technology stack:

Chatbots are available in multiple channels like Web/Desktop, Mobile (Android/iOS), Facebook Messenger, WeChat, Slack, Skype and others. Each of these channels has its unique characteristics, and so it is important to decide which channel will work best for us.

For example, Facebook Messenger requires us to use a webhook at our end so that it can send messages to our server and trigger our code. This means that the bot will be active only while the user is inside the messenger app. On the other hand, Skype allows direct integration with our code using an API call. This means that the bot can be active even if the user is not using Skype at all times. Therefore, choosing the right channel is critical for success as it will be directly linked to our bottom line.

Development & NLP Integration:

Let's say that we chose Skype as our channel. Now we can start our development process by building the user interface for the chatbot. We could go for a web application or a mobile app, or even a desktop app. It all depends on the type of experience we want to provide to the user. Once we have the user interface ready, we can integrate it with Skype. Skype provides a standard REST API, which is quite easy to use and has well documented documentation. Once we have the server-side integration ready, we can start integrating natural language processing into our code.

Testing:

This step is critical for success because it will help us identify problems in our business logic before our users figure them out. There are various types of testing that you can perform:

Unit testing:

This type of testing ensures that your code is working properly and will not break with changes in other parts of your code base. For example, if you change the logic used to process a command from a user, then this unit test will tell you if you have broken any other part of your code base.

Integration testing:

This type of testing is performed to ensure that when different components of your code are integrated together, they are working as expected. For example, if you integrate your code with your database and verify if it is working as expected.

End-to-end testing:

This type of testing is performed to ensure that the complete flow from start to end is working as expected. For example, if you integrate your code with your database and check if different endpoints are working fine.

Acceptance testing: This type of testing is done once you are done with development, and ensure that all requirements are met during test runs.

Deployment:

Deploying your chatbot is a critical step because this step might be different for every channel you choose; therefore, it's important to choose your channel wisely from the beginning so that deployment becomes easier in the long run.

Conclusion

In this article, I discussed the basic steps involved in building a chatbot using NLP technology and explained how those steps will help you go through the process smoothly and effectively. I also covered some basic concepts in NLP and gave examples of variables that are useful to capture while developing a chatbot. I provided a list of resources that you