Quiz: Question Answering Systems

1. What is the main purpose of a question answering system in NLP?

To summarize documents

To understand natural language questions and provide accurate answers

To generate new questions

To extract metadata from text

2. Which of the following is typically the first step in a question answering system?

Database lookup

Translating the question

Answer synthesis

Parsing the question for structure and intent

3. Which component helps a QA system understand the context of a question?

User interface

Search history

Knowledge base or passage of text

File name

4. What kind of model is typically used in modern question answering systems?

Image classifier

Deep learning language model

Rule-based parser

Linear regression

5. Which dataset is commonly used to fine-tune QA models like BERT?

MNIST

SQuAD

ImageNet

COCO

6. How does a QA model determine the correct answer from a passage?

By checking grammar rules

By counting word frequency

By identifying and scoring the most relevant span of text

By matching the number of question marks

7. What is a potential challenge in open-domain question answering?

Converting speech to text

Having too few answer options

Lack of model parameters

Finding relevant passages across large datasets

8. Which of these applications commonly uses question answering systems?

Spam detection

Virtual assistants like Siri or Alexa

Machine translation

Optical character recognition

9. What makes deep learning effective in QA tasks?

It converts audio into text

It captures semantic relationships in text

It uses decision trees

It memorizes all passages

10. Which feature is most important when evaluating QA system performance?

Number of training epochs

Accuracy and relevance of the response

Length of the answer

Answer formatting

Quiz 26: Question Answering Systems