A Newbie translating

  Where can a translator improve his/her "Knowledge of Privacy and Copyright Regulations"? Translators usually build this competence through a mix of formal CPD, professional guidelines, and authoritative legal resources. 1. Professional associations and CPD Look for webinars and courses on GDPR, confidentiality, and copyright from associations such as ATA, ITI, CIOL, and local translator associations; many offer on-demand CPD specifically for translators handling personal data and client content. Ethics and confidentiality guidelines from these bodies (codes of conduct, best-practice documents) explain what is expected of translators in terms of privacy, NDAs, and handling sensitive texts. 2. GDPR and privacy-focused resources Read targeted guides on  GDPR and translation , which explain roles (controller/processor), lawful bases, retention, NDAs, and secure tools, tailored to freelance translators and LSPs. ...

  What are the "BLEU/COMET scores" in translation? BLEU and COMET are automatic metrics used to score the quality of machine-translated text by comparing it to human reference translations. BLEU (Bilingual Evaluation Understudy) BLEU measures how many words and short phrases (n‑grams) in the machine translation also appear in the reference translation, giving higher weight to longer matching sequences. It outputs a score between 0 and 1 (often shown as 0–100); higher scores mean the MT output is more like the reference. BLEU is fast and widely used, but it captures surface overlap and is less sensitive to meaning when the wording differs but is still correct. COMET COMET is a newer, neural-network–based metric that uses pretrained language models to judge similarity in  meaning  between MT output and reference (and often also considers the source sentence). Instead of just counting overlapping words, it em...

  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 f...

  AI Post-Editing (MTPE) and Quality Control “AI post-editing (MTPE) and quality control” refers to the whole workflow where machine‑translated text is first produced by an MT/LLM engine and then reviewed, corrected, and checked by humans (often with additional automated QA) to reach a defined quality standard. AI post-editing (MTPE) In this context,  machine translation post-editing  means: An MT or LLM engine produces a draft translation instead of a human translating from scratch. A human linguist then revises that draft to fix meaning errors, mistranslations, omissions, additions, grammar, punctuation, style, and terminology. Depending on the brief, this can be: Light post‑editing : just enough corrections for basic comprehensibility and accuracy, less focus on style. Full post‑editing : bringing the text up to the level of a good human translation (natural, consistent, publication‑ready). When people say  AI ...

  How does MTPE compare to traditional human translation? Machine Translation Post-Editing (MTPE) sits between raw MT and full human translation: a machine produces a draft, and a human linguist edits it instead of translating from scratch. Main differences Aspect MTPE Traditional human translation Starting point Machine‑generated draft, then edited by a human. Human translator writes the translation from scratch, optionally with CAT/TM support. Speed/productivity Often faster, some reports claim up to ~2–3× output (e.g., 2,000 → 7,000 words/day), though gains vary widely in practice. Slower per word, since the human makes all decisions; typical figures around 2,000 words/day are often cited. Cost Usually, it is cheaper per word than full human translation, especially on large volumes. Highest cost, reflecting full human effort and expe...

  How do LLMs (Large Language Models - artificial intellingence)   differ from traditional NMT (Neural Machine Translation) in translation LLM-based translation and traditional NMT differ mainly in purpose, training, context handling, and behavior, even though both often use Transformer architectures. Core purpose and training Traditional NMT systems (e.g., Google Translate, DeepL) are  specialized  models trained specifically to map source sentences to target sentences using large parallel corpora. ​ LLMs are  general-purpose  models trained to predict the next token on massive multilingual and monolingual text, with translation being just one emergent capability among many. What they focus on NMT is optimized for segment-level translation accuracy, terminology consistency, and predictable behavior on sentence-by-sentence input. LLMs emphasize broader context, discourse coherence, and natural, human-like wording, often across whole paragraphs or documents....