Bleu+pdf+work Direct

def calculate_bleu_for_pdf(reference_pdf, candidate_text): ref_clean = clean_pdf_text(reference_pdf) ref_sents = chunk_sentences(ref_clean) cand_sents = chunk_sentences(candidate_text)

Introduction In the rapidly evolving world of machine translation (MT) and localization, three terms increasingly intersect in the daily workflow of linguists, developers, and project managers: BLEU , PDF , and Work . bleu+pdf+work

smoothing = SmoothingFunction().method1 scores = [] for ref, cand in zip(ref_sents, cand_sents): score = sentence_bleu([ref.split()], cand.split(), smoothing_function=smoothing) scores.append(score) What is BLEU Score

By following the pipeline described—high-fidelity extraction, sentence alignment, automated BLEU computation, and workflow integration—you can turn BLEU from an academic curiosity into a practical driver of translation quality. and project managers: BLEU

import pdfplumber from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction import re def clean_pdf_text(pdf_path): with pdfplumber.open(pdf_path) as pdf: full_text = "" for page in pdf.pages: text = page.extract_text() # Fix line-break hyphens text = re.sub(r'(\w+)-\n(\w+)', r'\1\2', text) # Replace newlines with spaces text = re.sub(r'\n+', ' ', text) full_text += text + " " return full_text.strip()

def chunk_sentences(text): # Simple sentence splitter (improve with spaCy for production) return re.split(r'(?<=[.!?])\s+', text)

This article explores why this combination matters, how to implement it, and best practices for making BLEU scores meaningful when working with PDF documents. What is BLEU Score? Developed by IBM in 2002, BLEU is an algorithm for evaluating the quality of machine-translated text against one or more human reference translations. It works by analyzing n-gram overlap (sequences of n words) between the candidate translation (machine output) and the reference (human gold standard).