OWhat comes to the mind of the average person upon the mention of machine translation (MT) is probably Google translate, but translation enthusiasts know that MT covers way more.
Everything from the different types of MT, its end-use applications, and the role of Artificial Intelligence, makes it apparent just how extensive Machine Translation is.
The emergence of the pandemic brought along interruptions in every sphere of life, with businesses requiring innovative ways of conducting their operations.
This blog post is an overview of the present state of MT and how it is of relevance to translation companies and clients alike.
Overview
Although the pandemic may have caused disruptions in the translation industry, one segment thriving is the machine translation category.
The sector, which had a market size of $622.5 million in 2020, has projections to grow to $1.5 billion by 2027 with a CAGR of 13.9 percent during this period, with the U.S market acting as a principal center on this front, accounting for a whopping 30% ($183.4 million) of the entire market share.
Principal drivers for the growth amidst the pandemic are; the need to translate bulk health-related materials into more languages, the need for faster translations coupled with the necessity for saving costs at this critical moment.
If you want to know more about machine translation, check out one of our previous posts.
Trends in Machine Translation
- NMT going mainstream
As the demand for machine translation continues to grow, so does the technology. Neural Machine Technology (NMT), which made its debut just a few years ago, is already causing a paradigm shift in the industry.
Many industry players are turning to NMT due to its superiority in the accuracy of most translations.
Unlike statistical translation that adopts a rule-based (phrase, word, syntax) approach to predict and translate text, which has a fair share of errors, NMT sequentially utilizes deep learning to create translations that are much closer to human levels of accuracy.
It is worth pointing out that neural machine translation still struggles with proper names, rare terms, and contextualized translation formats.
- Machine Translation in Video
Video content consumption is on the rise. Besides the wildfire called social media, the increasing adoption of virtual events and e-learning due to the pandemic plays a pivotal role in the surge of video content consumption being witnessed.
These effects are also having a snowball impact on machine translation. Many video content providers are now combining automatic speech recognition with machine translation in order to translate the transcribed source text to the desired target language, thereby providing captions and subtitles to deliver a better viewing experience to international audiences.
- Combination of NMT with MTPE in healthcare on the rise
With the need to complete clinical trials at the fastest possible duration the primary priority of pharma companies during the pandemic, translation companies are exploring the feasibility of combining neural machine translation alongside post-editing when translating clinical trial documents to keep up with time requirements.
It would still involve the extensive training of machine models so that errors during translation remain minimal.
- Newer MT models bursting forth
As neural machine translation continues to take center stage, researchers are coming up with innovative models in an attempt to disrupt the space. A model of worthy mention is work done by a group of researchers to create a multilingual NMT model to translate biomedical data from five languages to English. This model, which was intended to help with large-scale multilingual analysis as it concerns the Covid-19 pandemic, was trained on over 350 million sentences to produce state-of-the-art results.
Another notable model used an attention mechanism to improve the performance of the NMT model.
Importance of MT in the prevailing translation industry
There might have never been a better time for both translation companies and clients to look for creative ways to cut down operating costs, and the use of MT provides a viable solution. Besides cost savings, MT grants translation companies performance and efficiency upgrades by delivering closer to human-levels of accuracy when compared to the previous (statistical machine translation).
Conclusion
Machine translation adoption in the translation sector is clearly on the incline, and the data is there to back it up. Correspondingly, the segment is also witnessing a tilt from rule-based machine translation to neural machine translation, which has proved over time to be more accurate in its end result.
Although much work still needs to be done to improve its performance on context-related translation.
Translation companies and customers alike, to save cost, must see the option of machine translation as a good thing and as an opportunity to adapt their workflow to a more efficient way of carrying out their operations.
Translationsinlondon is your one-stop-shop for all your translation and localization projects. Our process leverages cutting-edge technologies and tools to create accurate, quick, and efficient translations for your business.