What will be the BLEU score output by this code snippet?

medium📝 Predict Output Q4 of 15

NLP - Text Generation

from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction
reference = [['a', 'quick', 'brown', 'fox']]
candidate = ['a', 'fast', 'brown', 'fox']
score = sentence_bleu(reference, candidate, smoothing_function=SmoothingFunction().method1)
print(round(score, 2))

A0.75

B0.92

C0.50

D0.33

Step-by-Step Solution

Solution:

Step 1: Analyze n-gram matches
The candidate differs from the reference only in 'quick' vs 'fast'. The rest of the words match exactly.
Step 2: Consider smoothing
Smoothing method1 reduces zero counts impact, increasing the score despite the mismatch.
Step 3: Calculate approximate BLEU
High overlap with smoothing leads to a score close to 0.92.
Final Answer:
0.92 -> Option B
Quick Check:
One word difference with smoothing yields high BLEU [OK]

Quick Trick: Smoothing boosts BLEU when few n-grams mismatch [OK]

Common Mistakes:

MISTAKES

Ignoring smoothing effects on BLEU score
Assuming exact word matches only
Miscounting n-gram overlaps

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What will be the BLEU score output by this code snippet?

Step 1: Analyze n-gram matches

Step 2: Consider smoothing

Step 3: Calculate approximate BLEU

Final Answer:

Quick Check:

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