Transforming Translation Technology in Life Sciences: The Future of Machine Translation and Translation Memory

Translation technology has undergone tremendous changes over the past 70 years. As more companies focus on improving technology performance, the life sciences industry is also increasing its investment in AI language automation technologies. These technologies not only help reduce costs, but also shorten time to market, improve translation consistency and quality, and optimize global operations processes.

Machine translation (MT) and translation memory (TM) are two key computer-assisted translation (CAT) tools that are driving innovation in the life sciences sector. To better understand these technology tools and their benefits to life sciences organizations, we need to review their evolution and consider the key factors when selecting a translation technology partner.

Life Sciences and Machine Translation

Machine translation and language technology have made significant progress since Warren Weave first proposed machine translation in 1947. According to a report by Global Market Insights, the machine translation market is expected to grow from US$650 million in 2020 to US$3 billion in 2027. This growth is driven by global investment in artificial intelligence, enterprise demand for localized content, improved customer service and experience, and the need for efficient and cost-effective translation, especially in the life sciences and medical fields.

Machine translation has gone through several stages of development. The following are the main types:

• Rule-Based Machine Translation (RBMT) : RBMT is the oldest type of machine translation, translating based on the grammatical rules of the source and target languages, and often requires extensive editing, especially in regulated industries such as life sciences.
• Example-based machine translation (EBMT) : EBMT was developed in the 1980s. It uses phrases or analogies in the source language and matches them with the corresponding translations in the target language.
• Statistical Machine Translation (SMT) : In the 90s, statistical machine translation became mainstream, with Google Translate being an example of this type.
• Hybrid machine translation : This model combines the advantages of RBMT and SMT, using translation memory to improve translation quality, but still requires linguists for post-editing.
• Neural Machine Translation (NMT) : NMT relies on artificial intelligence, simulates the human brain through neural networks, and learns directly from the source language to the target language, avoiding reliance on traditional rules.

The application of NMT, especially in machine learning, improves the intuitiveness and accuracy of translation engines. Despite significant advances in machine translation technology, regulated industries such as life sciences often still require human post-editing to ensure the highest quality translations.

The importance of translation memory in life sciences

Machine translation has brought great convenience to life science organizations operating globally, but the introduction of translation memory has also promoted the advancement of translation automation technology. Since translation memory was first introduced in the late 1970s and early 1980s, it has become an important tool for improving translation efficiency and quality.

For life sciences organizations, the use of translation memories, especially databases containing industry-related terminology and company-specific terminology, is critical to ensuring consistent and high-quality translations. The life sciences industry relies heavily on abbreviations and jargon, so translation memory can significantly improve translation quality when used in conjunction with machine translation. With the improvement of data quality and the continuous improvement of translation technology, the role of translation memory will change from a simple translation database to a training tool for machine translation.

Key considerations when choosing a translation technology partner

As the life sciences industry continues to innovate, translation technology will continue to improve to meet company needs and adapt to language changes. Life sciences companies are increasingly focusing on technologies that are intuitive, secure and easily accessible. When choosing a translation technology partner, here are a few key factors to consider:

• Integration capabilities : Ensure that the translation management platform can integrate with major content repositories to streamline content transfer and avoid workflow disruptions.
• Cloud access : Companies need to access translation projects anytime, anywhere, ensuring that the technology platform is easily accessible from anywhere.
• Validate the ecosystem : Ensure that the platform complies with relevant regulatory requirements, especially compliance standards such as CFR 21 Part 11.
• Reporting and KPI indicators : The platform should have reporting capabilities to provide transparency on quality, cost and timeline.
• Vendor neutrality : Choosing a neutral vendor helps better leverage language assets and ensures consistency across different vendors.
• Collaborative online review : The platform should support online collaborative review and editing to reduce manual operations and project management time.
• Innovation and Continuous Improvement : Ensure that partners continuously pursue technological innovation and improvement to keep pace with the continuous development of translation technology.

◀️：Five key considerations for implementing digital technologies to support patients in China

▶️：Key elements for creating global training content