Tag Archive : nlp applications

/ nlp applications

The need for high-quality chatbot training data

Humans and computers have been interacting ever since the beginning and this interaction has been improving with innovation over the years. From setting medical appointments to doing online check-ins for flights, AI chatbots that imitate human conversations have been gaining momentum.

What is a Chatbot?

A chatterbot, also known as a chatbot is a software of Artificial Intelligence that can simulate a chat or conversation with a user. The medium used is a natural language through applications, websites or telephone conversations. If it is to be defined technically, a chatbot is simply a representation of the natural evolution of a system made solely for answering questions using Natural Language Processing (NLP).

Chatbots learn from interactions and grow with time. Their working is based on rule-based and smart machine working. Rule-based chatbots use predefined responses from a database. The database is searched using keywords. Smart machine-based chatbots use Artificial Intelligence and Cognitive Computing. They develop according to the interactions.

what is a chatbot

So, why are chatbots so popular?

Artificial intelligence finds its application in several fields. Chatbots are one of the most popular examples of Artificial Intelligence. They are an important asset for many businesses as they assist in customer support. According to a 2011 research by Gartner, around 85% of our interactions will be handled by bots rather than humans. Chatbots aren’t just used for answering questions, but also play a vital role in collecting information about them, creating databases, etc.

Chatbots help with:

  • Customer service marketplace’s first priority is its customers. Their experience determines the success or failure of a company. When online shopping is considered, it has been observed that most of the shoppers need some kind of support. They need help at each step of the purchasing process, which is where chatbots come into action and make the process smooth and quick.
  • Customer information strategizes their customer service based on the data that they collect about the consumers. Chatbots take information from the reviews and feedback and use the information to help determine how the company can make its product better.
  • Lesser workforce work done by one chatbot is better than getting it done by a large number of employees. Companies can cut down on costs by using chatbots that can handle a variety of customer interactions, thus making the work simpler and more efficient as human errors are reduced.
  • Avoids redundancy tasks can be avoided within company call centers and the employees, that is, they will help in ensuring that the employees spend their time on important tasks rather than repetitive tasks.

Chatbots today can answer simple questions using prebuilt responses. If a user says A, respond with B and so on. After this kind of development, expectations have increased. We look for more advanced chatbots which can perform several tasks.

Conversational AI chatbots can be divided into a number of categories based on their level of maturity:

Level 1: This is the basic level where the chatbot can answer questions with pre-built responses. It is capable of sending notifications and reminders.

Level 2: At this level, the chatbot can answer questions and can also improvise a little during a follow-up.

Level 3: The assistant is now capable of engaging in a conversation with the user wherein it can offer more than the prebuilt answers. It gets an idea of the context and can help you make decisions with ease.

Level 4: Now, the conversational chatbot knows you better. It knows your preferences and can make recommendations based on them.

Level 5 and beyond: Now the assistant is capable of monitoring several assistants to perform certain tasks. They can do efficient promotions, help in specific targeting of certain groups based on trends and feedback.

So, what goes on behind building a chatbot?

Building a conversational chatbot is a long process, which needs innovation at every step. The first and the most important decision to be made is how the bot will process the inputs and produce the reply. Most systems today used rule-based or retrieval-based methods. Other areas of research are grounded learning and interactive learning.

  1. Rule-based
    The chatbots are trained using a set of rules that automatically convert the input into a predefined output or action. It is a simple system, but highly dependent on keywords.
  2. Retrieval-based
    In this system, the bot on receiving the input locates the best response from a database and displays it. It requires a high level of data pre-processing. This system is difficult to personalize and scale.
  3. Generative
    As the demand for chatbots is increasing, more innovation is demanded. The limitations of the above-mentioned systems are overcome by this one. The bot is trained using a large amount of chatbot training data. Generative systems are trained end-to-end instead of step-by-step. The system remains scalable in the long run.
  4. Ensemble
    All advanced chatbots like Alexa have been built with ensemble methods, which are a mixture of all the three approaches. They use different approaches for different activities. These methods still need a lot of work.
  5. Grounded learning
    Most human knowledge isn’t in the form of structured datasets and is present in the form of text and images. Grounded learning involves knowledge that is based on real-world conversations.

For a chatbot to function as per requirements, it is important to provide it with high-quality chatbot training data. What exactly is AI training data?

A chatbot converts raw data into a conversation. This raw data is unstructured. For example, consider a customer service chatbot. The chatbot needs to have a rough base of what questions people might ask and the answers to those questions. For this, it retrieves data from emails, databases or transcripts. This is the training data.

The process of formulating a response by a chatbot

The Importance of High-Quality Chatbot Training Data

Most of the chatbots today don’t work properly because they either have no training or use little data. The implementation of machine learning technology to train the bot is what differentiates a good chatbot from the rest.

Training is an on-going process. This development happens in 5 stages:

  1. Warm-up training
    The client data is used to start the chatbot. This is the first and most important step.
  2. Real-time training
    The incoming conversations are tracked and tell the bot what people are asking or saying, instead of working purely based on assumptions.
  3. Sentiment training
    The way people are talking to the bot is used to train language and functions. For example, an angry user is dealt with differently as compared to a happy user.
  4. Effectiveness training
    In this method, the result of the conversations is analyzed and the bot is trained accordingly to reach more people faster.

These are a few ways how high-quality chatbot training data can enable a conversational bot to produce optimal results. After this, the chatbot is checked for improvement at every stage. 

Chatbots make interactions between people and organizations simpler, enhancing customer service. They allow companies to improve their customer experience and efficiency. Human intervention is important in building, training and optimizing the chatbot system.