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What is a Question Answering System?

This article looks at the various aspects of a question-and-answer system and its applications and sees the endless possibilities.

Image: Ask a question to a robot and get answers from the content of the knowledge base

The answer to the question is Cloud-based Natural Language Processing (NLP), which allows you to create a natural conversation layer on your data. Your custom knowledge base (KB) information is used to find the most appropriate answer for each entry.

Answering questions is commonly used to build conversational client applications, including social media applications, chatbots, and desktop applications with speech capabilities. Several new features have been added, including enhanced connectivity using in-depth learning ratings, accurate answers, and point-to-point area support.

These documents include the following types of articles:

• Quick start with step-by-step instructions that allow you to contact the service and get results in a short time.
• Instructions on how to follow the instructions for using the service in more specific or customized ways.
• Conceptual articles provide in-depth descriptions of the service’s performance and features.
• Tutorials are longer guides that show you how to use services as components in broader business solutions.

When to use the answer to the question?

When you have static information – Use the answer to the question when you have fixed information in your knowledge base. This knowledge base is customized for your needs with documents such as PDFs and URLs.

The same answer is returned when you try to answer the same request, question, or command – when different users post the same question.
Add metadata tags to provide additional filtering options for client application users and information when you want to filter static information based on meta-information. Common metadata information includes chat and a short talk, content type or format, content purpose, and content freshness.

When you want to manage a robotic conversation that contains factual information – your knowledge base takes the text of the conversation or the user’s command and responds to it. If the answer is part of a predetermined conversation flow displayed in your knowledge base with a multi-turn field, the robot can easily provide this flow.

What is a knowledge base?

Answering a question brings your content into a Q&A knowledge base. The import process extracts information about the relationship between your structured and semi-structured content sections to show the relationship between the Q&A pairs. You can edit these Q&A pairs or add new pairs.

The content of the Q&A pair includes the following:

• All intermittent forms of questions
• Metadata tags are used to filter response options during a search
• Follow-up prompts search modification

After publishing your knowledge base, a client application will send the user query to your endpoint. Your question answering service processes the question and answers it with the best answer.

Create a programming chat robot

When a question-answering knowledge base is published, a client application sends a question to the endpoint of your knowledge base and receives the results as a JSON answer. A common client application for answering a question is a chat robot.

Action step

1. The client program sends the user’s text (text in its language) to the endpoint of your knowledge base, “How do I programmatically update my knowledge base?”
2. Answer the question, use the trained student database to provide the correct answer and any subsequent requests that can be used to refine the search for the best answer. The answer to the question returns an answer in JSON format.
3. The client application uses the JSON response to decide how to continue the conversation. These decisions can include showing the best answer and offering more options to refine the search for the best answer.

Build low-code chatbots

The language studio portal provides a complete project/knowledge base writing experience. You can import documents into your knowledge base in their current form. These documents (such as frequently asked questions, product manuals, spreadsheets, or web pages) become a pair of Q&A. Each pair is analyzed for subsequent notifications and linked to other pairs. The final markup format supports rich presentation, including images and links.

Once your knowledge base has been edited, publish the knowledge base to the Azure Web App without writing any code. Test your robot on the Azure portal or download it and continue development.

High-quality answers with layered rankings

The question and answer system use a layered ranking approach. The data is stored in Azure Search, which acts as the first ranking layer. The top results of the Azure search are then transmitted through the NLP reclassification model to answer the question to generate the final results and reliability score.

Multi-shift conversations

Question Answers provides multi-turn notifications and active learning to help you improve your original question and answer pairs.

Multi-turn notifications allow you to connect question-and-answer pairs. So This connection to the client application allows for a definitive answer and offers more questions to refine the search for the final answer.

Therefore After the knowledge base receives questions from users at the published endpoint, the answer to the question applies active learning to these real-world questions to suggest changes to your knowledge base to improve quality.

Development life cycle

Provides answers to questions, authorship, training, publication, and collaborative licenses for integration into the full development lifecycle.

Complete a quick start

Quick Starts are provided in the most popular programming languages, designed to teach you basic design patterns and execute code in less than 10 minutes.

You can start answering questions with the customer library. Answering the question provides virtually everything you need to create, manage, and deploy your custom knowledge base.

Again about question answering systems

I recently came across a library that allows one to create large-scale, distributed question-and-answer systems, and solves the significant problem we typically face in the vast realm of natural language processing, which is modeling. Are those that work with a large amount of data accurately. Hence, my interest in the field of question and answer systems arose.

Here I have tried to put together the general trends in question and answer systems. Once we have a good picture of the area, we will look at the Haystack Library, which came to my attention in the first place.

The purpose of question answering systems

What do question-and-answer systems want to achieve? Given a question, should the system give the closest answer? What if the answer is a mixture of several answers that have been taught? We strive to build intelligent systems that not only retrieve question documents but also extract relevant information into documents and provide accurate answers, such as what a human could achieve. This task falls under the fields of Information Retrieval (IR), Information Extraction (IE), and Natural Language Processing (NLP).

Google is a search engine. However, some queries are generated automatically when searching. This is because of Google’s knowledge charts to show large amounts of data.

google python_example

Question structure structure

The design of the question and answer system has certain vital components. So Three separate modules are used in a Q&A system:

• Query Processing Module: Classifies questions by context. This module identifies context and focus, categorizes the type of question, and sets expectations for the type of answer.
• Document processing module: Information retrieval module that focuses on collecting relevant documents.
• Response processing module: After retrieving the relevant documents, they must be analyzed to obtain an accurate and appropriate response.

In addition, Each of these modules performs different tasks to provide related answers. The image below briefly shows the entire architecture.

Image: Q&A system architecture

Query processing module

As mentioned earlier, the query processing system has three main functions:

• Question analysis to get basic information from it.
• Classify the question type to understand better the context required for the answer. For example, the expression Python error should lead to coding responses, and Python bites should lead to snake bites.
• Rephrase the question to get relevant answers. Thus, the question becomes a pre-trained vector with examples of several questions and answer pairs. This aspect is responsible for information retrieval.

Document processing module

The document processing module accepts the formulated query as its input. The document processing module uses an internal information retrieval (IR) system to plot the documents closest to the query. Also, The documents are arranged according to their similarity and relationship to the question. The document processing module performs three main tasks:

• Retrieves document collection from the IR system.
•  Filters the collection of documents obtained from the previous step and reduces the amount of text in each document to create a short answer.
• Sorts documents based on similarity and relevance to the question.

Response processing module

The final module receives a list of concise documents that have been filtered and sorted by the previous module. The response processing module considers a set of documents and performs three main tasks:

1. Identifies statements/responses in a concise set of documents.
2.  Selecting the appropriate phrases and words to answer the question extracts the relevant answer. This used to be exploratory. Innovative algorithms are a way of solving problems in which the best possible solution is given. Parameters such as cost, complexity in place and time, accuracy, or speed are ignored, and instead, emphasis is placed on finding a solution close to the actual answer.Innovative algorithms are commonly used to solve NP-complete problems, where there is no known efficient way to find a solution. However, by entering the parameters into the algorithm, we can verify that the answer is clear. Therefore, the development of heuristic algorithms involves the interchange between the generated solution and the algorithm itself.

   Due to the limitations, the algorithm is said to work well when the algorithm solution is as efficient as possible.

3. Validation of the answers obtained in the previous step. This is commonly used to design such question and answer systems to evaluate the output obtained. During deployment, evaluation criteria are reduced to achieve high data throughput of the system.

Evaluate the answers obtained

Criteria can vary from paper to paper. But more generally, we want the question-and-answer system to provide relevant, correct, and complete answers to the subject. Hence many evaluation criteria were developed to measure such vague terms. Some of these criteria are F1 scores, accuracy, reminders, etc.

Applications

The applications of such systems are varied and exciting. These, currently used in commercial applications, have a wide range in almost every aspect. Humans are question-based organisms, so these systems are the easiest way for humans to interact with machines.

Customer Support

The most common use of such systems is to reduce the burden on customer service teams. The idea behind introducing such systems was to eliminate the initial concerns that these systems face with query uncertainty. This team enables customer service to focus on the things that matter. Today, however, the quest for full automation continues, and question-and-answer systems integrated into talking robots rapidly transform the digital experience.

Tutorial

Google is a great search engine. But Chegg is a great search engine built to answer questions. Imagine a world in which such devices enhance the whole thought process needed for a child to learn. The child is asked to ask the right questions to understand the concepts. This is an example of a question and answer system integrated into personal learning systems.

Search engines

As mentioned above, Google uses such systems to provide questions and answers. Clicking on any of the questions will increase the number of questions shown. Also, The updated list is more like a question clicked on. If not, be sure to check it out the next time you click on a question on Google.

For research

It isn’t easy to build such distributed systems. Real-time Q&A machines are challenging because of the scale at which they operate. So Today’s databases are in petabytes, and fast access to information is a big challenge.

There are many solutions for efficient storage and computing. However, ensuring that algorithms work properly with such solutions is an important challenge.