At first glance, these concepts seem unrelated. BLEU (Bilingual Evaluation Understudy) is a mathematical metric for translation quality. PDF (Portable Document Format) is a ubiquitous file format for document exchange. And "Work" encompasses the operational pipelines of translation. However, when you combine them—searching for how to make efficiently—you uncover a critical need: extracting translatable content from locked PDFs, running automated quality metrics like BLEU on the output, and integrating that process into a professional translation workflow.
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
def chunk_sentences(text): # Simple sentence splitter (improve with spaCy for production) return re.split(r'(?<=[.!?])\s+', text) At first glance, these concepts seem unrelated
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() automated BLEU computation
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.
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)