Bleu+pdf+work ~upd~

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).

| 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 | bleu+pdf+work

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