Anastasia Toynbee from the Royal Society of Chemistry gave a great presentation a few weeks ago on how RSC are using machine translation to help authors who have English as a second language.
There is a huge language burden for ESL authors, and she covered some of the stark data in her talk. Remember, talent is evenly distributed in the world, but opportunity is not, and if we want to solve the problems facing us, we need to bring to bear a plurality of voices, views, and insights to work on these problems. Removing these barriers to ESL authors is key.
RSC are using machine translation to help with this problem. ML has advanced significantly, and previous problems with machine translation are
They have their own translation solution with parallel data, which is working better than Google Translate, due to the chemistry specific training that they have provided. Anastasia does not mention it in her talk, but I think they might be using AWS's service - https://docs.aws.amazon.com/translate/latest/dg/customizing-translations-parallel-data.html#.
What they are working on is very much at POC stage.
They are currently rolling this out with a small cohort of users.
This is a short talk, and worth looking at.
Machine translation has been around for many years, but has not seen widespread adoption in scholarly publishing due accuracy concerns. This talk is a good signal that the ground is moving here.
You can see her talk here:
https://www.youtube.com/watch?v=5wtgWahel8M&list=PL0SVAjXYdL0nb3wWAKmTR2L_C6ggE_x8z
There is a huge language burden for ESL authors, and she covered some of the stark data in her talk. Remember, talent is evenly distributed in the world, but opportunity is not, and if we want to solve the problems facing us, we need to bring to bear a plurality of voices, views, and insights to work on these problems. Removing these barriers to ESL authors is key.
RSC are using machine translation to help with this problem. ML has advanced significantly, and previous problems with machine translation are
They have their own translation solution with parallel data, which is working better than Google Translate, due to the chemistry specific training that they have provided. Anastasia does not mention it in her talk, but I think they might be using AWS's service - https://docs.aws.amazon.com/translate/latest/dg/customizing-translations-parallel-data.html#.
What they are working on is very much at POC stage.
They are currently rolling this out with a small cohort of users.
This is a short talk, and worth looking at.
Machine translation has been around for many years, but has not seen widespread adoption in scholarly publishing due accuracy concerns. This talk is a good signal that the ground is moving here.
You can see her talk here:
https://www.youtube.com/watch?v=5wtgWahel8M&list=PL0SVAjXYdL0nb3wWAKmTR2L_C6ggE_x8z