Why QA systems extract answers in NLP - Challenge Your Understanding

Challenge - 5 Problems

🧠 Conceptual

intermediate

Purpose of Answer Extraction in QA Systems

Why do question answering (QA) systems extract specific answers from text instead of returning entire documents?

A To provide concise and relevant information quickly to users

B Because returning entire documents is faster and more efficient

C To confuse users by giving incomplete information

D Because QA systems cannot process full documents

💡 Hint

Think about what users want when they ask a question.

QA systems extract answers to give users quick, focused responses without needing to read long texts.

🧠 Conceptual

intermediate

Benefit of Extractive QA over Document Retrieval

What is a key benefit of extractive QA systems compared to just retrieving documents?

A They ignore the question and return random text

B They highlight exact answer spans within documents

C They always generate new text unrelated to the question

D They only work with images, not text

💡 Hint

Consider how extractive QA helps users find answers faster.

Extractive QA systems find and highlight exact answer parts, saving users time from reading whole documents.

❓ Model Choice

advanced

Choosing a Model for Answer Extraction

Which model type is best suited for extracting precise answers from a given text passage?

A Sequence labeling model that tags answer spans

B Regression model predicting numerical values

C Clustering model that groups similar documents

D Generative model that creates new text unrelated to input

💡 Hint

Think about models that identify parts of text as answers.

Sequence labeling models can mark exact answer spans, making them ideal for extractive QA.

❓ Metrics

advanced

Evaluating Extractive QA Systems

Which metric is commonly used to evaluate the quality of extracted answers in QA systems?

A BLEU score for machine translation quality

B Mean Squared Error (MSE) for numerical prediction accuracy

C Exact Match (EM) score measuring exact answer overlap

D Silhouette score for clustering quality

💡 Hint

Look for a metric that checks if the predicted answer exactly matches the true answer.

Exact Match score measures if the predicted answer exactly matches the correct answer, important for extractive QA.

🔧 Debug

expert

Debugging a QA System Extracted Answer

A QA system extracts answers but often misses the exact answer span, returning partial or unrelated text. What is the most likely cause?

A The system is using a perfect answer extraction model

B The system is correctly extracting answers but the evaluation metric is wrong

C The input documents are empty

D The model's token classification layer is not properly aligned with input tokens

💡 Hint

Consider how token alignment affects answer span prediction.

If token classification is misaligned, the model predicts wrong spans causing partial or unrelated answers.