┌───────────────────────────────┐ │ Candidate vs Reference Text │ └───────────────┬───────────────┘ │ ┌────────────────┴────────────────┐ ▼ ▼ ┌─────────────────────────────┐ ┌─────────────────────────────┐ │ Modified N-gram Precision │ │ Brevity Penalty │ │ (1-gram to 4-gram) │ │ (Penalizes short outputs) │ └──────────────┬──────────────┘ └──────────────┬──────────────┘ │ │ └────────────────┬────────────────┘ ▼ ┌─────────────────────────────┐ │ Final BLEU Score (0-1) │ └─────────────────────────────┘ Pillar A: Modified N-gram Precision An "n-gram" is simply a continuous sequence of n words. : Individual words ("the", "cat"). 2-gram (Bigram) : Pairs of words ("the cat", "cat sat"). 4-gram (4-gram) : Word groups of four ("the cat sat down").
If your query refers to the software (often phonetically associated with "bleu") for professional PDF review workflows: bleu+pdf+work
Poor translation, usually indicates the model failed to capture the context. 4. Limitations of BLEU in PDF Work 4-gram (4-gram) : Word groups of four ("the cat sat down")
import pdfplumber from nltk.translate.bleu_score import sentence_bleu # 1. Extract text from your target PDF report with pdfplumber.open("machine_translation_output.pdf") as pdf: first_page = pdf.pages[0] candidate_text = first_page.extract_text().split() # 2. Define your human-validated baseline references reference_text = [ "The engineering team must update the architectural blueprint by Friday.".split(), "The technical drawings need to be revised by the engineers before the weekend.".split() ] # 3. Calculate how well the PDF text matches human quality bleu_score = sentence_bleu(reference_text, candidate_text) print(f"Document BLEU Score: bleu_score:.4f") Use code with caution. Limitations of BLEU in PDF Work import pdfplumber