Mirela
It’s estimated that the majority of businesses offering global services are using AI translation tools. Translation, once a slow, manual process is now supported by advanced neural networks, automation pipelines, and real-time language technologies. Alongside this shift has come a wave of new acronyms, the acronyms used in AI translation. Here are the main ones you need to know about.
MT (Machine Translation)
At its core, machine translation is the use of software to automatically translate text or speech from one language to another. It’s the foundation on which all the other acronyms rest. In AI-driven localization, MT serves as the foundation for automating multilingual content production, speeding things up while reducing costs.
TMS (Translation Management System)
A platform (like POEditor) that integrates MT, translation memory, terminology, and workflow automation for translation/localization teams. A TMS improves efficiency, ensures consistency across multilingual content, and reduces the time and cost associated with large-scale localization projects.
ML (Machine Learning)
A core branch of AI where systems improve their performance by learning from data rather than following fixed rules. In translation, ML underpins the ability of models to adapt, recognize patterns, and improve accuracy over time.
MTPE (Machine Translation Post-Editing)
Human editing of MT output to correct errors, improve style, and ensure accuracy. Can be light (minor tweaks) or full (high-quality, publishable text). MTPE combines the speed of AI translation with human linguistic expertise. MTPE is often integrated with APE (Automatic Post-Editing) and translation memory tools.
APE (Automatic Post-Editing)
An AI system that automatically corrects errors in machine translation output without human intervention. APE models are trained on pairs of raw MT output and human post-edited translations so they can learn common error patterns and fix them.
TER (Translation Edit Rate)
A metric showing how many edits are needed to turn an MT output into a perfect translation, useful for measuring post-editing. Edits can include insertions, deletions, substitutions, and shifts of word sequences. The resulting score is expressed as a percentage, with lower percentages indicating that fewer edits are needed and therefore higher translation quality.
NMT (Neural Machine Translation)
This is the modern standard for translation technology. NMT uses deep learning models to produce more fluent and natural translations, far surpassing the stilted output of earlier methods.
NLP (Natural Language Processing)
The broader field of AI that enables machines to understand, interpret, and generate human language. Machine translation is just one branch of this fast-growing area.
SMT (Statistical Machine Translation)
Before neural networks took over, SMT was the dominant method. It relied on finding statistical patterns in large bilingual text datasets. While largely replaced by NMT, SMT remains an important milestone in translation history.
TAAF (Translation as a Feature)
An approach where translation capabilities are embedded directly into a product or service as a built-in function, rather than being offered as a separate tool or workflow. In AI contexts, TaaF often uses integrated MT and NLP models to allow users to interact with multilingual content seamlessly.
BLEU (Bilingual Evaluation Understudy)
How do you know if an AI translation is good? BLEU is a metric that automatically compares machine translations to human reference translations, giving a quick, quantitative score for quality. It works by measuring the overlap of n-grams (contiguous sequences of words) between the machine output and the reference, with higher overlap indicating better translation quality.
RBMT (Rule-Based Machine Translation)
One of the earliest approaches to translation, RBMT uses linguistic rules and dictionaries. Though it’s less common today, it still finds use in specialized scenarios where precision is more important than flexibility. RBMT is highly interpretable, but it often struggles with fluency and scalability.
BERT (Bidirectional Encoder Representations from Transformers)
A star in the NLP world, BERT is a powerful AI model developed by Google for natural language understanding. It processes text bidirectionally, meaning it considers the context of each word from both directions simultaneously.
COMET (Crosslingual Optimized Metric for Evaluation of Translation)
A neural-based evaluation metric for MT that predicts translation quality more accurately than older methods like BLEU. COMET uses neural network models to assess translations based on semantic meaning and context. It is trained on human judgment scores.
ASR (Automatic Speech Recognition)
When translating speech, you first need to turn it into text. ASR technology does exactly that, transcribing spoken words so they can be fed into a translation system.
TTS (Text-to-Speech)
The other end of the speech translation pipeline: once the text is translated, TTS technology turns it into spoken audio in the target language.
Whether you want to keep up with jargon or understand the tools and trends shaping how we communicate across languages, the more acronyms used in AI translation you know, the better prepared you are for what’s next.