How To Get Started With Natural Language Question Answering Technology

How To Get Started With Natural Language Question Answering Technology

A short time ago, employees had to rely on busy co-workers or intensive research to get answers to their questions. This may have included Google searching, manually combing through documents or filling out internal tickets.

Many machine learning techniques are ridding employees of this issue with their ability to understand and process human language in written text or spoken words. While natural language processing (NLP) and natural language understanding (NLU) can process the meaning and understand the sentiment of the human language, natural language question answering (NLQA) takes it a step further by generating highly relevant answers to employee queries and questions.

This article will explore how NLQA technology can benefit a company’s operations and offer steps that companies can take to get started.

Why Companies Use NLQA

Speed, accuracy and efficiency go a long way in business. Employees do not want to be slowed down because they can’t find the answer they need to continue with a project. Technology that can give them answers directly into their workflow without waiting on colleagues or doing intensive research is a game-changer for efficiency and morale.

If the information is there, accessing it and putting it to use as quickly as possible should be easy. In this way, NLQA can also help new employees get up to speed by providing quick insights about the company and its processes. These tactics can cut training time and costs for businesses.

How To Get Started With NLQA

Like many new activities and goals in life, it is best to walk before you run. Analyzing what departments and business units need the most information is a great place to start. This way, you can choose one use case to get the ball rolling.


Customer service, for example, is one business area where NLQA gets utilized the most. Customers or companies often call or fill out support tickets for extremely specific questions, and these questions tend to have answers that could be automated. To achieve this, customer service departments typically deploy two methods: chatbots (to interpret the customer’s question and provide an answer) and troubleshooting (when chatbots cannot help achieve an answer).

For questions that may not be so popular (meaning the person is inexperienced with solving the customer’s issue), NLQA acts as a helpful tool. The employee can search for a question, and by searching through the company data sources, the system can generate an answer for the customer service team to relay to the customer.

To determine which departments might benefit most from NLQA, begin by exploring the specific tasks and projects that require access to various information sources. Who needs fast-acting insights in their everyday jobs? Research and development (R&D), for example, is a department that could utilize generated answers to keep business competitive and enhance products and services based on available market data.

Overall, the determination of exactly where to start comes down to a few key steps. Management needs to have preliminary discussions on the possible use cases for the technology. Following those meetings, bringing in team leaders and employees from these business units is essential for maximizing the advantages of using the technology. C-suite executives oversee a lot in their day-to-day, so feedback from the probable users is always necessary. Talking to the potential users will give CTOs and CIOs a significant understanding that deployment is worth their while.

By determining which departments can best benefit from NLQA, available solutions can help train your data to interpret specified documents and provide the department with relevant answers. This process can be used by any department that needs information or a question answered.


Once this has been determined and the technology has been implemented, it’s important to then measure how much the machine learning technology benefits employees and business overall. Looking at one area makes it much easier to see the benefits of deploying NLQA technology across other business units and, eventually, the entire workforce.

The metrics to determine how NLQA technology varies by department. For example, measuring customer satisfaction rate after solving a problem is a great way to measure the impact generated from the solutions. In other areas, measuring time and labor efficiency is the prime way to effectively calculate the ROI of an AI initiative. How long are certain tasks taking employees now versus how long did it take them prior to implementation? Each individual company’s needs will look a little different, but this is generally the rule of thumb to measure AI success.

Another important and often overlooked aspect is user feedback. Are they having an easier time with the solution, or is it adding little benefit to them? Companies must have a strong grasp on this to ensure the satisfaction of their workforce.


Whether it’s project management to search for details about a partner/client or the legal department to receive answers about specific types of cases or lawsuits, NLQA can provide a company with advantages in multiple areas since access to information and knowledge is a part of all jobs.

While any department can benefit from NLQA, it is important to discuss your company’s particular needs, determine where NLQA may be the best fit and analyze measurable analytics for individual business units. With these practices, especially involving the user in decision-making, companies can better ensure the successful rollouts of AI technology.