Synergizing Human Expertise with Machine Translation
In today’s AI landscape, Machine Translation (MT) stands out as a tool for rapid translations in numerous language pairs, thanks to sophisticated algorithms and linguistic databases.
While it improves speed and cost-effectiveness, raw machine translation still struggles to grasp the finer cultural nuances of human translations. There is still progress to be made before it reaches parity with human translators.
This is where Machine Translation Post-Editing (MTPE) comes in. MTPE combines the speed of machine translation with human expertise to refine translations. Human editors add cultural insights, adjust for context, and ensure quality, making the translations more accurate and effective.
Before delving deeper into Machine Translation Post- Editing, it’s essential to clarify some key terms.
What is Machine Translation?
According to AWS, machine translation is the automated process of translating text from one language to another using artificial intelligence, thus eliminating the need for human involvement. It leverages natural language processing and deep learning to comprehend the meaning of a given text and translate it into different languages.
There are three main types of machine translation:
Rules-Based Machine Translation: Utilizes built-in linguistic rules and bilingual dictionaries for specific industries or topics to translate content. It is the oldest form of machine translation and also the least accurate.
Statistical Machine Translation: Employs machine learning to analyze large collections of pre-existing human translations to identify statistical patterns. Subsequently, it makes informed predictions regarding the statistical likelihood of certain words or phrases being associated with others in the target language.
Neural Net Machine Translation (NMT): Leverages artificial intelligence to learn languages and continually enhance its understanding. This type of machine translation excels when provided with context, often beyond the confines of a single sentence. The output quality significantly improves because the translation is performed on entire paragraphs rather than isolated sentences. This approach enables NMT systems to address many limitations of other methods and produce higher-quality translations.
At PGLS, we have the capability to train our machine translation (MT) engines with industry and client-specific glossaries to help it better understand and utilize specific words or phrases unique to that field. Furthermore, we incorporate content from the client’s translation memory (TM) before initiating machine translation for low-fuzzy matches or new translation units. A translation memory is a database that stores previously translated phrases and sentences.
When a new document is ready for translation, our Computer Assisted Translation (CAT) tool refers to the TM to find and utilize translations that bear similarity to those in the new document. This unique feature sets our service apart from publicly-available machine translation engines, thereby ensuring faster, consistent, and more accurate translations.
Limitations of Raw Machine Translation
Despite technological advancements, raw machine translation is limited in its ability to accurately grasp context, idiomatic expressions, and cultural nuances. Moreover, reliance on third-party servers for processing often raises privacy and security concerns, especially when sensitive information is involved. Machine translation performance also deteriorates for low-resource languages, where less training data is available to ensure accurate translations.
The US Department of Health and Human Services (HHS) has provided its perspective on machine translation in Section 1557 of the Affordable Care Act. It outlines when and how machine translation may be used in healthcare communications.
While the technology behind machine translation has improved in accuracy, the possibilities of significant consequences from inaccurate translation continue to exist. All studies indicated error rates so high as to be ‘unacceptable for actual deployment in health settings.
Healthcare providers must adhere to stringent HIPAA requirements to ensure patient privacy and regulatory compliance. These regulations include strict guidelines for the handling, disclosure, and transmission of Protected Health Information (PHI). PHI includes details about an individual’s health, healthcare provisions, and payments, all of which can be traced back to them.
Not only do third-party machine translation systems lack robust security measures to prevent data breaches and unauthorized access, but the lack of human translation also compromises the accuracy and reliability of translated content.
What is Machine Translation Post-Editing?
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.
How Does Machine Translation Post-Editing Work?
Once the machine translation engine generates the translated text, a human translator, also known as a post-editor, reviews and refines the output. The aim is to correct any errors, improve coherence, and elevate the overall quality of the translation.
The post-editor carefully compares the machine-generated translation with the original source text, making necessary adjustments to ensure accuracy, fluency, and adherence to specific style or terminology requirements.
Benefits of Machine Translation Post-Editing
Efficiency: MTPE combines the speed and scalability of machine translation with the accuracy and fluency provided by human post-editors. This allows for the quick processing of large volumes of content while ensuring high-quality translations.
Cost-efficiency: By leveraging machine translation for the initial translation process and human post-editing for refinement, MTPE can be more cost-effective compared to traditional human-only translation services, especially for large projects.
Quality assurance: Human post-editors review and improve machine-translated content, ensuring that translations are accurate, coherent, and culturally appropriate for the target audience.
Faster turnaround times: With MTPE, translations can be delivered more quickly compared to human-only translation services, making it ideal for projects with tight deadlines.
Get Started with PGLS
Piedmont Global Language Solutions (PGLS) provides high-quality and idiomatic translations in nearly any document format, and in more than 200 languages and regional varieties. All of our document translations are completed by professional, qualified translators and editors, all of whom have gone through our rigorous recruitment and vetting process.
All translation projects are managed by our team of expert, multilingual Project Managers and go through our complete, multi-step Quality Control Process to ensure that you receive an accurate and idiomatically appropriate translation. Get in touch with our team to learn more.