BLEU score evaluation in NLP

Introduction

BLEU score helps us check how good a computer's translation is by comparing it to human translations. It tells us if the computer is doing a good job.

When you want to see how well a machine translated a sentence compared to a human translation.

When testing different translation models to pick the best one.

When improving a chatbot's language by checking its responses against correct answers.

When comparing summaries or paraphrases generated by a computer to original texts.

Syntax

NLP

from nltk.translate.bleu_score import sentence_bleu

reference = [['this', 'is', 'a', 'test']]
candidate = ['this', 'is', 'a', 'test']
score = sentence_bleu(reference, candidate)
print(score)

The reference is a list of correct translations (each is a list of words).

The candidate is the machine's translation (a list of words).

Examples

Compare a candidate sentence with one reference sentence.

NLP

reference = [['the', 'cat', 'is', 'on', 'the', 'mat']]
candidate = ['the', 'cat', 'sat', 'on', 'the', 'mat']
score = sentence_bleu(reference, candidate)
print(score)

Compare candidate with multiple reference sentences.

NLP

references = [['this', 'is', 'a', 'test'], ['this', 'is', 'test']]
candidate = ['this', 'is', 'a', 'test']
score = sentence_bleu(references, candidate)
print(score)

Sample Model

This program calculates the BLEU score between one human reference and one machine candidate sentence. It shows how close the machine's sentence is to the human's.

NLP

from nltk.translate.bleu_score import sentence_bleu

# One reference translation
reference = [['the', 'quick', 'brown', 'fox', 'jumps', 'over', 'the', 'lazy', 'dog']]
# Candidate translation from machine
candidate = ['the', 'quick', 'brown', 'fox', 'jumped', 'over', 'the', 'lazy', 'dog']

# Calculate BLEU score
score = sentence_bleu(reference, candidate)

print(f"BLEU score: {score:.4f}")

Important Notes

BLEU score ranges from 0 to 1, where 1 means perfect match.

BLEU uses matching of small word groups (called n-grams) to compare sentences.

Shorter candidate sentences may get lower scores even if correct.

Summary

BLEU score measures how close a machine translation is to human translations.

It compares words and word groups between candidate and reference sentences.

Higher BLEU means better translation quality.

Practice

1. What does the BLEU score primarily measure in machine translation?

easy

A. How close the machine translation is to human translations

B. The speed of the translation process

C. The number of words in the translated sentence

D. The grammar correctness of the translation

Solution

1. Step 1: Understand BLEU score purpose

   BLEU score is designed to compare machine translations to human reference translations.

2. Step 2: Identify what BLEU measures

   It measures similarity in words and phrases, not speed or grammar correctness.

3. Final Answer:

   How close the machine translation is to human translations -> Option A

4. Quick Check:

   BLEU = similarity to human translations [OK]

Hint: BLEU = closeness to human translation quality [OK]

Common Mistakes:

• Confusing BLEU with translation speed
• Thinking BLEU measures grammar correctness
• Assuming BLEU counts total words only

2. Which of the following is the correct way to calculate the BLEU score using NLTK in Python?

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.translate.bleu_score.score(candidate, reference)

Solution

1. Step 1: Recall NLTK BLEU function syntax

   The correct function is sentence_bleu and it takes a list of references and a candidate sentence.

2. Step 2: Match correct argument order

   References must be a list of lists, candidate is a list of tokens.

3. Final Answer:

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

4. Quick Check:

   Use sentence_bleu([ref], cand) syntax [OK]

Hint: Use sentence_bleu with references as list of lists [OK]

Common Mistakes:

• Passing candidate before reference
• Not wrapping reference in a list
• Using incorrect function names

3. Given the candidate sentence ["the", "cat", "is", "on", "the", "mat"] and reference sentence ["there", "is", "a", "cat", "on", "the", "mat"], what is the approximate BLEU score (unigram precision only)?

medium

A. 0.83

B. 0.50

C. 0.67

D. 0.33

Solution

1. Step 1: Calculate unigram matches

   Candidate words: the, cat, is, on, the, mat
   Reference words: there, is, a, cat, on, the, mat
   Matching unigrams: the, cat, is, on, mat (count matches carefully)

2. Step 2: Compute unigram precision

   Matches = 5 (the counted once), Candidate length = 6
   Precision = 5/6 ≈ 0.83 but 'the' appears twice in candidate but once in reference, so max count for 'the' is 1.
   Counting max matches: 'the' once, 'cat' once, 'is' once, 'on' once, 'mat' once = 5 matches
   Precision = 5/6 ≈ 0.83

3. Step 3: Adjust for max counts

   Since 'the' appears twice in candidate but only once in reference, only one 'the' counts.
   So total matches = 5, candidate length = 6, precision = 5/6 ≈ 0.83

4. Final Answer:

   0.83 -> Option A

5. Quick Check:

   Unigram precision = 5/6 = 0.83 [OK]

Hint: Count max reference word matches for unigram precision [OK]

Common Mistakes:

• Counting repeated words more than reference max
• Confusing unigram with bigram precision
• Ignoring max count clipping

4. Identify the error in this BLEU score calculation code snippet:

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)

medium

A. Candidate should be a string, not a list

B. Missing import for nltk

C. Reference should be a list of lists, not a single list

D. sentence_bleu requires lowercase strings only

Solution

1. Step 1: Check sentence_bleu input format

   sentence_bleu expects references as a list of reference sentences (each a list of tokens), so reference must be wrapped in another list.

2. Step 2: Identify the error in code

   Reference is given as a single list, not a list of lists, causing a type error or wrong calculation.

3. Final Answer:

   Reference should be a list of lists, not a single list -> Option C

4. Quick Check:

   References = list of lists [OK]

Hint: Wrap reference in a list for sentence_bleu [OK]

Common Mistakes:

• Passing reference as a flat list
• Passing candidate as string instead of list
• Ignoring input format requirements

5. You have two reference translations:
ref1 = ['the', 'cat', 'is', 'on', 'the', 'mat']
ref2 = ['there', 'is', 'a', 'cat', 'on', 'the', 'mat']
And a candidate translation:
candidate = ['the', 'cat', 'sat', 'on', 'the', 'mat']
How should you prepare the references to correctly compute the BLEU score considering multiple references?

hard

A. Pass references as separate calls to sentence_bleu

B. Concatenate ref1 and ref2 into a single list and pass as one reference

C. Pass only the reference closest in length to candidate

D. Pass references as a list containing both ref1 and ref2 lists

Solution

1. Step 1: Understand multiple references in BLEU

   BLEU supports multiple references by passing a list of reference sentences (each a list of tokens).

2. Step 2: Prepare references correctly

   References should be passed as [ref1, ref2], a list containing both reference lists.

3. Step 3: Avoid incorrect methods

   Concatenating references or passing separately will give wrong results.

4. Final Answer:

   Pass references as a list containing both ref1 and ref2 lists -> Option D

5. Quick Check:

   Multiple references = list of reference lists [OK]

Hint: Use list of reference lists for multiple references [OK]

Common Mistakes:

• Concatenating references into one list
• Passing references separately in multiple calls
• Using only one reference when multiple exist