Answer span extraction in NLP - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is answer span extraction in NLP?

Answer span extraction is the task of finding the exact part (span) of a text that answers a question. It locates the start and end positions of the answer within a passage.

beginner

Why do models predict start and end positions for answers instead of generating text?

Predicting start and end positions helps models find the exact answer inside the given text. It is simpler and more accurate than generating new text, especially when the answer is a direct excerpt.

intermediate

What kind of model output is used for answer span extraction?

Models output two probability distributions: one for the start position and one for the end position of the answer span. The highest probabilities indicate the predicted answer boundaries.

intermediate

How is the training loss calculated for answer span extraction models?

The loss is usually the sum of two cross-entropy losses: one comparing predicted start positions to true start, and one comparing predicted end positions to true end. This guides the model to predict correct spans.

advanced

What is a common challenge when extracting answer spans from long passages?

Long passages can have multiple similar phrases, making it hard to pick the correct span. Also, the answer might be spread out or require understanding context beyond simple matching.

In answer span extraction, what do models predict?

A Start and end positions of the answer in the text

B The full generated answer text

C Only the start position of the answer

D The question category

Models predict the start and end positions to locate the exact answer span within the passage.

Which loss function is commonly used to train answer span extraction models?

A Mean squared error

B Cross-entropy loss

C Hinge loss

D Cosine similarity

Cross-entropy loss is used to compare predicted start/end positions with true positions.

Why is answer span extraction preferred over answer generation in some QA tasks?

A It requires less computation

B It always produces longer answers

C It finds exact text spans, improving accuracy

D It does not need training data

Extracting exact spans from text often leads to more precise answers than generating new text.

What is a typical output format of an answer span extraction model?

A Two probability distributions over tokens for start and end

B A single probability for the whole answer

C A list of possible answers

D A confidence score only

The model outputs two distributions indicating likely start and end tokens of the answer.

What makes answer span extraction challenging in long texts?

A Answers are always at the start

B Models cannot handle long texts

C Answers are never in the text

D Multiple similar phrases can confuse the model

Similar phrases in long texts can make it hard to identify the correct answer span.

Explain how answer span extraction models find answers in a passage.

Think about how the model points to parts of the text instead of creating new words.

Describe challenges faced when extracting answer spans from long or complex passages.

Consider why picking the right part of a long text can be tricky.