Machine Translation
Machine Translation (MT) refers to the automated process of translating text from one language into another without human involvement. MT systems vary in complexity and approach, ranging from rule-based systems that rely on linguistic rules and dictionaries to statistical models that analyze large amounts of bilingual text data, and more recently, neural MT systems that utilize deep learning algorithms.
Unlike public-facing MT tools, PGLS has the capability to train our MT engines with industry- and client-specific glossaries to help them better understand and utilize specific words or phrases unique to that field. We can also incorporate content from the client’s translation memory (TM) before initiating machine translation. When the content you need translated is not mission-critical and/or specialized in nature, MT can be a viable alternative to human translation.
While MT can save you time and resources, it can still struggle with the finer cultural and linguistic nuances. This is where Machine Translation Post Editing (MTPE) comes in. MTPE combines the speed of machine translation with the oversight of a professional human translator. Schedule a consultation with one of our translation experts to learn more about our MT capabilities, and to discover which approach is best for you.
Our Machine Translation Capabilities
Public-Facing Machine Translation
Open-source, public-facing Machine Translation engines cannot be trained with custom glossaries that are specific to a client, field, or industry.
Public-facing MT systems do not have the capability to incorporate content from the client’s translation memory (TM).
Public-facing MT systems do not have the ability to specify language register (e.g. formal vs. informal).
When using public-facing MT engines, you cannot use more than one MT engine at a time.
Public-facing MT engines use sensitive data that you upload to further refine and train their MT capabilities.
PGLS Machine Translation
Our Machine Translation engines can be trained with industry- and client-specific glossaries to help them better understand and utilize specific words or phrases unique to that field.
PGLS can also incorporate content from the client’s translation memory (TM) before initiating machine translation.
Our Machine Translation engines have the ability to specify language register (e.g. formal vs. informal).
PGLS can use more than one MT Engine at a time and customize which engines are used for the language pair and vertical. We have access to 12 different MT engines and 4 different OenAI GPT-based engines.
PGLS protects your sensitive data by restricting open-source MT engines from harvesting sensitive information.
Machine Translation Post Editing (MTPE)
Machine Translation Post-Editing involves the careful review and refinement of machine-translated text to improve its accuracy and coherence. By combining the rapid processing capabilities of machine translation with the linguistic proficiency and domain knowledge of human translators, MTPE ensures higher quality translations.
Compared to human translation, MTPE can significantly reduce translation time. Machine translation generates a draft translation quickly, which can then be edited by a human post-editor. This streamlined process can be more time-efficient, especially for large volumes.
MTPE is more cost-effective than human translation. Since the initial translation is generated by a machine, the overall cost per word is lower. Human post-editors can focus their efforts on refining the machine-generated translation, rather than translating from scratch.
MTPE is highly scalable and can handle large volumes of translation work efficiently. It allows organizations to quickly translate a large amount of content to meet tight deadlines or address sudden surges in translation demand.
While machine translation may not always produce perfect translations, post-editing allows for quality control and refinement by human linguists. Post-editors can correct errors, improve clarity, and ensure that the final translation meets the desired quality standards.