The Evolving Machine Translation and Translation Memory Landscape for Life Sciences
Over the past 70+ years and to this day, translation technology has changed drastically as more companies are focusing on ways to improve their tech performance with data that is higher quality and customizable. Consequently, life sciences organizations have been investing more in AI language automation technologies to drive down costs, reduce time to market, improve translation consistency and quality, and streamline workflows across global operations.
Machine translation (MT) and translation memory (TM) are two computer-assisted technology (CAT) tools that have helped drive innovation in the life sciences sector. To better understand these language technology tools and their benefits for life sciences organizations, it’s important to understand how the landscape has evolved since they were first introduced, as well as considerations to take into account when deciding on your translation technology partner.
Life Sciences and Machine Translation
We’ve come a long way since the first time machine translation was presented in 1947 by Warren Weave, a researcher from Rockefeller Foundation. Since its inception, machine translation and language technology, in general, have taken on new legs in their capabilities. In fact, according to Global Market Insights, the machine translation market is expected to grow from $650 million in 2020 to approximately $3 billion by 2027. This growth is attributed to several factors, including investment in AI globally, demand for localized content among businesses, focus on improving customer service and experience, and the increasing need for cost-efficient and timely translations. This is particularly true for life sciences and healthcare organizations.
So, how has machine translation evolved? To answer this, it’s best to first identify the main types of machine translation:
- Rule-based machine translation (RBMT): RBMT was the first type of MT to enter the language technology scene in the 1950s. It translates content based on grammatical rules of the source language and target language. This type of MT typically requires a lot of editing, particularly for regulated industries such as life sciences.
- Example-based machine translation (EBMT): In the 1980s, a new CAT tool emerged known as EBMT. This model uses phrases or analogies in the source language before matching them to corresponding translations in the target language.
- Statistical machine translation (SMT): Statistical machine translation is arguably the most well-known type of machine translation, making its first appearance in the language tech scene in the 1990s. It matches words from the source language to the target language. An example of SMT is Google Translate.
- Hybrid machine translation: The hybrid model for MT is exactly as it sounds—a cross between two different kinds of machine translation. Typically, when people refer to hybrid machine translation, it’s a blend of RBMT and SMT. The biggest difference in this model is that it leverages translation memory to ensure higher quality translations; however, it typically still requires linguists for a post-editing process.
- Neural machine translation (NMT): The newest player to enter the field of language technology is NMT, which heavily relies on artificial intelligence. The theory of NMT was first proposed in 2013 and operates on a neural network model, similar to the human brain, which learns statistical models for machine translation. This means that NMT learns directly from mapping source language text to the associated target text, rather than relying on rules.
Machine learning, specifically in NMT, has taken center stage for its benefits in training translation engines to be more intuitive and accurate. While we’ve made monumental progress since machine translation was first introduced, as it is, regulated industries such as life sciences typically require human post-editing (also known as machine translation post-editing) in order to achieve the highest quality of results.
The Importance of Translation Memory in Life Sciences
While machine translation has done wonders for life sciences organizations operating at a global scale, it’s not the only progress in translation automation technology for the industry. In fact, one of the reasons machine translation has become so successful and more widely used is the increasing use of translation memory. When translation memory was first introduced in the late 1970s and early 1980s, it was cited as a way to lighten the translation burden on linguists, improving turnaround time as well as machine translation quality. Fast forward to today, and translation memory is one of the most widely used CAT tools for helping companies build data repositories to enable faster and cheaper translations.
For life sciences organizations, leveraging translation memories or data banks of words related to the industry, as well as company-specific jargon, is imperative for ensuring consistent, higher-quality translations. As an industry that relies heavily on acronyms and technical terminology, translation memory is hugely beneficial for life sciences organizations, particularly when paired with machine translation. With higher quality data being collected, and with a growing focus on developing better, automated translation technology, the role of translation memories will continue to change from being primarily a company translation database to a training tool for machine translation.
Considerations for Choosing a Translation Technology Partner
As the life sciences industry continues to evolve and innovate, translation technology will also continue to improve and adapt to meet companies’ expectations, as well as keep up with the plasticity of languages. More and more, global life sciences companies are looking for technologies that are intuitive, secure, and easily accessible. When deciding on the best fit for your translation technology partner, take these considerations into account:
- Integration capabilities: Check that the translation management platform has integration capabilities to leading content repositories to streamline the transfer of content and avoid disruptions in workflows.
- Cloud interface: Being able to access translation projects at any time, anywhere in the world is critical for global life sciences companies conducting operations across multiple time zones and regions. Double-check that your technology partner’s platform is easily accessible whenever, wherever.
- Validated ecosystem: Arguably the most important feature for any machine translation portal is ensuring it is compliant with regulations. Confirm that your translation partner has a secure and validated system that ideally should be CFR 21 Part 11 compliant.
- Reporting and KPI metrics: Your translation technology partner’s platform should have reporting capabilities to enable greater visibility and transparency on quality, costs, and timelines.
- Vendor agnostic: To better leverage linguistic assets, including TM and MT, consider partnering with an agnostic vendor. This also helps drive consistency between multiple suppliers.
- Collaborative online review: Ensure that your partner’s platform allows for collaborative online review and editing capabilities to eliminate manual processes and reduce project management time.
- Innovation and continuous improvement: Last, but certainly not least, make sure your translation technology partner is consistently looking for ways to improve their products, as well as your processes. As mentioned throughout this article, the translation technology landscape is an ever-evolving topic, which is why you need a partner that invests in innovation and drives technology improvements.
Drop us a line if you’re interested in learning more about machine translation, translation memory, and their benefits for life sciences organizations. If you’re interested in seeing a demo of our GlobalLink AI portal, you may do so here.