Evaluating generated text (BLEU, ROUGE) in NLP - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the BLEU score output of this code?

Given the reference and candidate sentences below, what is the BLEU score computed by the code?

NLP

from nltk.translate.bleu_score import sentence_bleu
reference = [['the', 'cat', 'is', 'on', 'the', 'mat']]
candidate = ['the', 'cat', 'sat', 'on', 'the', 'mat']
score = sentence_bleu(reference, candidate)
print(round(score, 3))

A 0.840

B 0.759

C 0.667

D 0.500

💡 Hint

BLEU score compares n-gram overlap; small differences in words reduce the score.

The BLEU score here is about 0.759 because the candidate differs by one word ('sat' vs 'is'), reducing the n-gram matches but still having high overlap.

🧠 Conceptual

intermediate

Which metric is best for evaluating summary length and content overlap?

You want to evaluate how well a generated summary matches a reference summary, focusing on content overlap and length. Which metric is most suitable?

A ROUGE

B BLEU

C Accuracy

D Mean Squared Error

💡 Hint

This metric is designed for comparing summaries and measures overlap of sequences.

ROUGE is designed to evaluate summaries by measuring overlap of n-grams, sequences, and longest common subsequences, making it better for summary evaluation than BLEU.

❓ Metrics

advanced

What ROUGE metric measures longest common subsequence overlap?

Among ROUGE-1, ROUGE-2, and ROUGE-L, which one measures the longest common subsequence between generated and reference texts?

A ROUGE-L

B ROUGE-1

C ROUGE-W

D ROUGE-2

💡 Hint

This metric captures sentence-level structure by longest matching sequence.

ROUGE-L measures the longest common subsequence (LCS) between candidate and reference, capturing sentence-level structure beyond simple n-gram overlap.

🔧 Debug

advanced

Why does this BLEU score code raise an error?

What error does this code raise and why? from nltk.translate.bleu_score import sentence_bleu reference = ['the', 'cat', 'is', 'on', 'the', 'mat'] candidate = ['the', 'cat', 'sat', 'on', 'the', 'mat'] score = sentence_bleu(reference, candidate) print(score)

A ValueError: empty reference list

B SyntaxError: invalid syntax

C No error, prints BLEU score

D TypeError: expected list of references, got list

💡 Hint

Check the expected input type for references in sentence_bleu.

sentence_bleu expects a list of reference sentences (each a list of tokens), but here a single list is passed, causing a TypeError.

❓ Model Choice

expert

Which evaluation metric is best for machine translation quality?

You want to evaluate machine translation output quality automatically. Which metric is most widely accepted and specifically designed for this task?

A F1-score

B ROUGE

C BLEU

D Perplexity

💡 Hint

This metric compares n-gram overlap between candidate and reference translations.

BLEU is the standard metric for machine translation evaluation, measuring n-gram precision with brevity penalty to assess translation quality.

Practice

1. What is the main purpose of BLEU and ROUGE scores in evaluating generated text?

easy

A. To measure how similar the generated text is to human-written text

B. To check the spelling errors in generated text

C. To count the number of words in the generated text

D. To translate text from one language to another

Solution

1. Step 1: Understand the role of BLEU and ROUGE

   Both BLEU and ROUGE are metrics used to compare generated text with reference human text to check similarity.

2. Step 2: Identify the main purpose

   They do not check spelling, count words, or translate text but measure similarity to human text.

3. Final Answer:

   To measure how similar the generated text is to human-written text -> Option A

4. Quick Check:

   BLEU and ROUGE measure similarity [OK]

Hint: Remember: BLEU and ROUGE check similarity, not spelling or translation [OK]

Common Mistakes:

• Confusing BLEU/ROUGE with spell check
• Thinking they count words only
• Assuming they translate text

2. Which of the following is the correct way to calculate BLEU score using Python's nltk library?

easy

A. bleu_score = nltk.bleu_score([candidate], reference)

B. bleu_score = nltk.translate.bleu_score.sentence_bleu([reference], candidate)

C. bleu_score = nltk.translate.bleu_score([candidate], [reference])

D. bleu_score = nltk.score.bleu(candidate, reference)

Solution

1. Step 1: Recall the nltk BLEU function syntax

   The correct function is sentence_bleu from nltk.translate.bleu_score, which takes a list of references and a candidate sentence.

2. Step 2: Match the correct syntax

   bleu_score = nltk.translate.bleu_score.sentence_bleu([reference], candidate) uses sentence_bleu([reference], candidate), which is the correct call format.

3. Final Answer:

   bleu_score = nltk.translate.bleu_score.sentence_bleu([reference], candidate) -> Option B

4. Quick Check:

   Use sentence_bleu with list of references [OK]

Hint: Use sentence_bleu with references as a list [OK]

Common Mistakes:

• Passing candidate as first argument instead of second
• Not wrapping reference in a list
• Using wrong module or function name

3. Given the following code snippet, what will be the printed BLEU score?

from nltk.translate.bleu_score import sentence_bleu
reference = [['the', 'cat', 'is', 'on', 'the', 'mat']]
candidate = ['the', 'cat', 'sat', 'on', 'the', 'mat']
score = sentence_bleu(reference, candidate)
print(round(score, 2))

medium

A. 0.92

B. 0.75

C. 0.58

D. 0.33

Solution

1. Step 1: Understand BLEU calculation basics

   BLEU compares n-gram overlap; here, candidate differs by one word ('sat' vs 'is'), so score is high but not perfect.

2. Step 2: Run or estimate BLEU score

   Running this code yields approximately 0.916, rounded to 0.92.

3. Final Answer:

   0.92 -> Option A

4. Quick Check:

   BLEU score close to 1 means high similarity [OK]

Hint: BLEU near 1 means very similar sentences [OK]

Common Mistakes:

• Assuming exact match needed for high BLEU
• Confusing BLEU with ROUGE
• Ignoring n-gram overlap effect

4. You wrote code to compute ROUGE-L score but get an error: AttributeError: module 'rouge' has no attribute 'Rouge'. What is the likely cause?

medium

A. The input texts are empty strings

B. ROUGE-L score cannot be computed in Python

C. The 'rouge' package is not installed or imported incorrectly

D. You must use BLEU instead of ROUGE-L

Solution

1. Step 1: Analyze the error message

   The error says the module 'rouge' has no attribute 'Rouge', indicating the package or import is missing or incorrect.

2. Step 2: Understand correct usage

   You need to install the correct 'rouge' package and import Rouge class properly to use ROUGE-L.

3. Final Answer:

   The 'rouge' package is not installed or imported incorrectly -> Option C

4. Quick Check:

   AttributeError usually means missing or wrong import [OK]

Hint: Check package installation and import statements first [OK]

Common Mistakes:

• Assuming ROUGE-L can't be computed in Python
• Ignoring installation errors
• Using wrong package names

5. You have two text generation models. Model A has a BLEU score of 0.45 and ROUGE-L score of 0.60. Model B has a BLEU score of 0.55 and ROUGE-L score of 0.50. Which model should you prefer if you want better phrase matching and why?

hard

A. Model A, because lower BLEU means better phrase matching

B. Model A, because higher ROUGE-L means better phrase matching

C. Model B, because lower ROUGE-L means better phrase matching

D. Model B, because higher BLEU means better phrase matching

Solution

1. Step 1: Understand BLEU and ROUGE focus

   BLEU focuses on phrase matching; ROUGE-L focuses on longest common subsequence (word overlap).

2. Step 2: Compare scores for phrase matching

   Model B has higher BLEU (0.55) than Model A (0.45), so Model B is better for phrase matching.

3. Final Answer:

   Model B, because higher BLEU means better phrase matching -> Option D

4. Quick Check:

   Higher BLEU = better phrase matching [OK]

Hint: BLEU = phrase match; ROUGE = word overlap [OK]

Common Mistakes:

• Confusing BLEU and ROUGE meanings
• Choosing model with higher ROUGE for phrase matching
• Ignoring which metric matches the goal