Preparing data (clean parallel texts) for companies that want to create customized internal translation systems.

 

Preparing data (clean parallel texts) for companies that want to create customized internal translation systems.

Preparing clean parallel texts means collecting, aligning, and cleaning bilingual sentence pairs so a company can safely use them to train or fine‑tune its own MT/LLM (Machine Translation/Large Language Model (artificial intelligence) translation engine.

What “clean parallel texts” involves

  • Parallel alignment: Each source sentence must be correctly paired with its exact translation (no misaligned or shifted segments).
  • Noise removal: Delete non‑linguistic junk (HTML, boilerplate, navigation text, cookie banners, duplicated segments, empty or near‑empty lines, wrong‑language lines).
  • Length and structure filters: Remove segments that are too long/too short, badly segmented, or contain extreme length mismatches between source and target.
  • Consistency and domain control: Enforce consistent terminology, punctuation, and formatting, and keep only content that reflects the domain/style the client wants the system to learn.

Typical workflow for companies

  • Extract bilingual content from TMs, CMSs, previous projects, and documents; align them into sentence pairs if not already aligned.
  • Run automated cleaning (e.g., tools like Bifixer/Bicleaner, MTCleanse, inhouse scripts) to filter duplicates, detect non‑parallel lines, and remove noise.
  • Optionally, have human linguists review samples or high‑value subsets to correct alignment and terminology and to exclude sensitive or unsuitable texts.

Why do companies pay for this?

  • Model quality: MT quality is strongly correlated with training data quality; “garbage in, garbage out” is particularly true in MT.
  • Efficiency: Clean corpora reduce training time and computational cost while maintaining or improving BLEU/COMET scores.
  • Control and privacy: Using curated internal data lets companies build domain‑specific systems without exposing confidential texts to public engines.

 

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