Question answering (QA) is a field of natural language processing (NLP) and artificial intelligence (AI) that aims to develop systems that can understand and answer questions posed in natural language.
The point of a QA system is to understand the question and give an answer that is correct and helpful.
QA systems can be based on various techniques, including information retrieval, knowledge-based, generative, and rule-based approaches. Each method has its strengths and weaknesses, and the choice of method depends on the project’s specific needs.
QA systems can be used in many places, like customer service, search engines, healthcare, education, finance, e-commerce, voice assistants, chatbots, and virtual assistants.
In this post, we will discuss the techniques used in QA systems, their strengths and weaknesses, and the various applications of QA systems.
We’ll also give an overview of the tools and frameworks used to set up a QA system.
QA systems can be used in many places
A natural language question-answering (QA) system is a computer program that automatically answers questions using NLP. The basic process of a natural language QA system includes the following steps:
The specific methods used in each step and the system’s architecture will depend on the QA system’s design and the type of questions it intends to answer.
For example, some systems are based on a knowledge base, others on information retrieval, and others on generative models. Hybrid systems can also be designed to combine several approaches to improve overall performance.
It’s also worth noting that the quality of the input data, pre-processing, tokenisation, and the model’s architecture are essential to achieve an excellent question-answering system.
Training a QA model requires a large dataset of questions and corresponding answers.
Question answering (QA) implementation in natural language processing (NLP) involves using various NLP techniques to answer questions in natural language automatically. There are several different approaches to QA implementation in NLP.
Information retrieval-based question answering (QA) is a method of automatically answering questions by searching for relevant documents or passages that contain the answer. This approach uses information retrieval techniques, such as keyword or semantic search, to identify the documents or passages most likely to hold the answer to a given question.
Information retrieval-based QA systems are generally easy to implement and can be used to answer a wide range of questions. However, their performance can be limited by the quality and relevance of the indexed text and the effectiveness of the retrieval and extraction methods used.
It’s also important to note that IR-based QA systems are often used with other types of QA, like knowledge-based or generative QA, to improve the system’s overall performance.
Knowledge-based question answering (QA) automatically answers questions using a knowledge base, such as a database or ontology, to retrieve the relevant information. This strategy’s foundation is that searching for a structured knowledge base for a question can yield the answer.
Knowledge-based QA systems are generally more accurate and reliable than other QA approaches based on structured and well-curated knowledge. But their performance can be limited by how well the knowledge base is covered and how well the methods used to make queries and get information from their work.
It’s also important to note that knowledge-based QA systems are often used with other QA methods, like information retrieval-based or generative QA, to improve the overall performance of the QA system.
Generative question answering (QA) automatically answers questions using a generative model, such as a neural network, to generate a natural language answer to a given question.
This method is based on the idea that a machine can be taught to understand and create text in natural language to provide a correct answer in terms of grammar and meaning.
Generative QA systems are powerful as they can answer a wide range of questions and generate more human-like answers.
However, their performance can be limited by the training data’s quality and diversity and the model’s complexity.
It’s also worth noting that Generative QA systems are often used with other QA approaches, such as information retrieval-based or knowledge-based QA, to improve the overall performance of the QA system.
These combinations are known as Hybrid QA systems.
Hybrid question answering (QA) automatically answers questions by combining multiple QA approaches, such as information retrieval-based, knowledge-based, and generative QA. This approach is based on the idea that different QA approaches have their strengths and weaknesses, and by combining them, the overall performance of the QA system can be improved.
Hybrid QA systems are considered more robust and accurate than a single QA approach, as they can leverage the strengths of multiple QA methods. Hybrid QA systems can also be more flexible, as they can adapt to different types of questions and different levels of complexity. But designing and putting together a hybrid QA system can be more complex and take more resources than a single QA method.
Hybrid QA systems can be built to be used in a specific domain or a general-purpose QA system. In both cases, the system’s performance will depend on the data quality, pre-processing, tokenisation, and the model’s architecture.
Rule-based question answering (QA) automatically answers questions using a predefined set of rules based on keywords or patterns in the question. This approach is based on the idea that many questions can be answered by matching the question to a set of predefined rules or templates.
Rule-based QA systems are generally simple and easy to implement. Still, their performance can be limited by the coverage and completeness of the rules and the effectiveness of the pattern matching and extraction methods used. In addition, rule-based QA systems are more prone to errors and can only handle questions covered by predefined rules.
It’s also worth noting that rule-based QA systems are often combined with other QA approaches, such as information retrieval-based, knowledge-based, or generative QA, to improve the overall performance of the QA system. In these cases, the rule-based QA can filter out irrelevant answers and improve the efficiency of the comprehensive system.
All of these approaches require significant training data, including questions and their corresponding answers, to improve the accuracy of the QA system.
Additionally, the quality of the input data, pre-processing, tokenisation, and the model’s architecture is essential to achieve a good question-answering system.
Question-answering (QA) systems have various applications in various industries and domains. Some of the most common applications of QA systems include:
These are some examples of the applications of QA systems, but there are many more depending on the domain and the type of question being asked. As technology advances, we can expect to see more of these systems in various industries, automating many tasks that humans once did.
Several NLP tools and frameworks are available for implementing a question-answering (QA) system. Some of the most popular include:
These are some examples of the tools and frameworks that can be used to implement a QA system, but there are many more depending on the project’s specific needs.
A question-answering (QA) system is a computer programme that can automatically answer questions posed using NLP.
QA systems can be based on various techniques, including information retrieval, knowledge-based, generative, and rule-based approaches. Each method has its strengths and weaknesses, and the choice of technique depends on the project’s specific needs.
QA systems can be used in many places, like customer service, search engines, healthcare, education, finance, e-commerce, voice assistants, chatbots, and virtual assistants.
Improving the accuracy of a QA system requires a significant amount of training data, including questions and their corresponding answers.
A sound QA system also depends on the quality of the input data, pre-processing, tokenisation, and model architecture.
What application are you considering for your QA system? Let us know in the comments.
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Madam, how can i increase disk space for google colab to train deep learning models without updating colab? In the mid way of running my code for a "QA system fine tuned using Bert ", terminate sa th ing disk storage is full
Hi Mariyam,
Google Colab only gives away limited resources for free. You can either upgrade your account or do what we do and set up your own server to train your models. We really like using AWS. It's also not free but you pay for the hours that you use making it a really good option.