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Introduction In the world of Natural Language Processing (NLP) and machine translation (MT), the BLEU score (Bilingual Evaluation Understudy) remains the most widely cited metric for evaluating translation quality. However, a recurring challenge for researchers, localization managers, and developers is getting the BLEU score to work correctly with PDF files . PDFs introduce layers of complexity—embedded fonts, multi-column layouts, headers, footers, and non-text elements—that can severely distort BLEU calculations.
pdftotext -layout reference.pdf ref_raw.txt pdftotext -layout candidate.pdf cand_raw.txt ./clean_pdf.sh ref_raw.txt > ref_clean.txt ./clean_pdf.sh cand_raw.txt > cand_clean.txt cat cand_clean.txt | sacrebleu ref_clean.txt --tokenize zh Pitfall 1: Different Tokenization BLEU requires identical tokenization for candidate and reference. PDFs often introduce non-standard spaces. Fix: Apply the same tokenizer (e.g., sacrebleu ’s built-in tokenizers) to both after extraction. Pitfall 2: Scanned PDFs (No Text Layer) If your PDF is image-based, you must run OCR. Use pytesseract . However, OCR errors (e.g., "r n" becoming "m" ) will degrade BLEU. Fix: Post-process with a spellchecker or use a high-quality OCR model (e.g., EasyOCR). Pitfall 3: Multi-Column Layouts BLEU assumes linear text. In two-column scientific papers, the reading order is often left column top-to-bottom, then right column. PDF extractors might read across columns. Fix: Use pdfplumber with coordinates to crop columns or use grobid for structured extraction. Part 5: Advanced Techniques – Improving BLEU Reliability for PDF Workflows 1. Character-Level BLEU as a Fallback If your PDF extraction is extremely noisy (e.g., OCR errors), character n-gram BLEU can be more robust. Use sacrebleu --char-level . 2. Use Smoothing Functions PDF noise often results in zero n-gram matches for higher n-grams. Apply smoothing (e.g., method 2 or 3 in nltk.BLEU ) to mitigate. 3. Segment by Paragraph, Not Page Page boundaries are arbitrary for BLEU. Concatenate all extracted text from the PDF into a single string, then segment by punctuation. This avoids penalizing valid line breaks. 4. Validate with a Sanity Check Run BLEU on a small, manually cleaned portion of two PDFs. If the score changes dramatically after you clean automatically, your cleaning pipeline needs tuning. Part 6: Real-World Case Study – Evaluating MT on Legal PDFs Scenario: A language service provider needs to BLEU-evaluate an MT engine on a 200-page legal contract (English to German). bleu+pdf+work
| Tool | Best for | Handling of BLEU-sensitive elements | |------|----------|--------------------------------------| | (Export to Word) | Small documents with complex layouts | Good for columns, poor for hyphenation | | pdfplumber (Python) | Programmatic, multilingual text | Excellent; can detect line breaks and table structures | | Tesseract + OCR (for scanned PDFs) | Image-based PDFs | Required but introduces OCR errors | | Grobid | Scientific papers (double columns) | Superior for multi-column text ordering | Introduction In the world of Natural Language Processing