Do you ever feel lost in a sea of machine translation (MT) terms? Fear not! This article will help you navigate the world of MT and post-editing. From the futuristic Neural Machine Translation (NMT) to the trusty Computer-Aided Translation (CAT), we've got you covered. Let's get started!
Have you met the MT robots? They're here to help!
Machine Translation (MT) is a technology that automatically translates text from one language to another. The goal of MT is to produce a translated text that accurately conveys the meaning of the source text while preserving its grammatical structure and syntax. Read our article “What is Machine Translation” to learn more.
Automated Translation is the way of streamlining the translation process by collaborating with CAT or MT tools to produce high-quality translations. Making the job of the translator easier by proofreading/editing the raw automated texts. But this will depend on the context as some instances see you needing to complete the translation entirely. Automated translation workflows offer a more efficient process for translating content, as compared to automatic translation which simply translates text without any consideration for context or meaning. To learn further about automated translation, you can access our article on the subject here.
This assists human translators by suggesting translations. Essentially, CAT is a sidekick to the human translator. These tools work by storing previously translated texts in a database, which can then be used to suggest translations for similar texts in the future. CAT tools can also analyze the structure of the source text and suggest potential translations based on previous translations and language rules. SDL Trados and MemoQ are examples of this technology. If you would like to delve deeper into computer-aided translation, you can access our article on the topic here.
Neural Machine Translation (NMT) is a type of MT that uses deep learning algorithms to generate translations. NMT systems are based on artificial neural networks that are trained on large datasets of parallel texts. The neural networks can then analyze the source text and generate a translated text that accurately captures its meaning and context. Examples of this technology are Google Neural Machine Translation, DeepL, and more. If you want to gain more knowledge about Neural Machine Translation, you can access our article on this subject here.
This is a type of MT that uses statistical models to generate translations. It is kind of like the data analyst of the MT world. SMT systems are based on the analysis of large datasets of parallel texts to learn the probabilities of word and phrase combinations in the source and target languages. The system then uses these probabilities to generate translations. Moses and Apertium are some examples that still use SMT. If you would like to deepen your understanding of statistical machine translation, you can access our article on this topic here.
This is a type of MT that uses rule-based models to generate translations. It can be considered the traditionalist of the MT world. RBMT systems rely on a set of predefined rules that are created by human linguists or experts to translate texts. These rules are based on language rules and grammar, and they allow the system to analyze the structure of the source text and generate a grammatically correct translation. Examples of companies that used this in the past are Lucy LT and Systran. To know more about the history of MT, consider reading our article “What is Machine Translation?”.
This is a combination of two or more MT technologies. It is the chameleon of the MT world, adapting to any situation. HMT systems can be customized to fit specific translation needs by combining the strengths of different MT technologies. Companies that utilize this are SDL Language Cloud, KantanMT, and more. If you want to gain more knowledge about hybrid machine translation, you can access our article on this subject by clicking here.
This is a type of MT that uses AI and machine learning technologies to generate translations. Because it is still learning, it is really the newbie of the MT world. AI translation systems use large amounts of data to learn how to translate and improve over time. These systems use neural networks and deep learning algorithms to analyze and understand the structure and meaning of sentences.
This depends on the use of MT and other language technologies to automate language workflows. It is like the magician of the MT world because it makes language workflows disappear. Language automation can help organizations reduce the time and costs associated with language-related tasks. For example, it can automatically translate large volumes of content into multiple languages, eliminating the need for manual translation.
This is a process where human translators use MT as an aid in translation. The team player of the MT world, working alongside humans. It involves using MT to suggest translations and provide context to the translator, who then reviews and edits the suggested translations to ensure accuracy and fluency. This can help human translators increase their productivity and reduce the time and effort required to translate large volumes of content.
But wait, there's more! Meet the post-editing experts who take MT to the next level.
This is the process of editing machine-translated text by a human translator. The translator is the MT whisperer, taming the wild MT. The process involves reviewing the MT output, identifying errors, and making necessary corrections to ensure the text is fluent and accurate. MTPE can be considered the "MT whisperer" of the MT world, as it involves taming the wild MT output and refining it to a high-quality translation. To know more about this, read our blog about it here!
Post-Editing Machine Translation and Machine Translation Post-Editing are essentially the same thing. They both refer to the process of a human translator reviewing and editing machine-translated content to ensure its accuracy, fluency, and cultural appropriateness.
This is where human translators assist in the MT process. The cheerleader, cheering on the MT team. HAMT involves using machine translation as a starting point for translation, and then having human translators review and refine the output to ensure accuracy, fluency, and cultural relevance. Human translators can help to fine-tune the MT system by providing feedback on its performance and suggesting improvements. They can also correct errors and improve the fluency of the translation, ensuring that it meets the needs of the target audience.
Want to know more about post-editing machine-translated outputs? Feel free to read our previous article called “Quality of Machine Translation: How Good It Needs to Be.”