I'm a little more pessimistic about machine translation (MT) replacing the need for human translators anytime soon. This is a good article about some of the technical barriers limiting machine translation improvement in some significant ways, such as lack of sufficient training data for the automated translation: https://academic.oup.com/bioscience/article/72/10/988/6653151 . There are even bigger problems in using ML in certain areas like communications between diplomats where the exact choice of word and the meanings it has in its culture would need to be considered (which knowledge is not going to be in the MT system).
Yes, I agree there will continue to be a role for human translation in specific situations, as I mentioned in my article.
In terms of the availability of data for training the models, rhank you for the reference to that article, but it pertains to the previous generation of translation technology. The latest LLM-based generation has entirely different requirements for training data that relies much less heavily on bilingual data sets.
It would be interesting to see any academic research articles that actually did a quantitative analysis on how the availability of machine learning tools actually affected the the percent of people willing to spend the time to learn a foreign language (outside of some mandatory eduational or vocational requirement). I did a cursory literature search and was unable to find any such articles. It is hard to search for such articles even using AI-enhanced search tools with neural search and embeddings because the search tool returns irrelevant articles such as ones on how machine translation helps students trying to learn a foreign language.
While your predictions may indeed come true, it will always be intellectually stimulating to become as proficient as possible in a foreign language. There must also be many cognitive benefits.
I'm a little more pessimistic about machine translation (MT) replacing the need for human translators anytime soon. This is a good article about some of the technical barriers limiting machine translation improvement in some significant ways, such as lack of sufficient training data for the automated translation: https://academic.oup.com/bioscience/article/72/10/988/6653151 . There are even bigger problems in using ML in certain areas like communications between diplomats where the exact choice of word and the meanings it has in its culture would need to be considered (which knowledge is not going to be in the MT system).
Yes, I agree there will continue to be a role for human translation in specific situations, as I mentioned in my article.
In terms of the availability of data for training the models, rhank you for the reference to that article, but it pertains to the previous generation of translation technology. The latest LLM-based generation has entirely different requirements for training data that relies much less heavily on bilingual data sets.
Thanks. That is interesting that the latest generation of LLMs depend less on bilingual data sets.
It would be interesting to see any academic research articles that actually did a quantitative analysis on how the availability of machine learning tools actually affected the the percent of people willing to spend the time to learn a foreign language (outside of some mandatory eduational or vocational requirement). I did a cursory literature search and was unable to find any such articles. It is hard to search for such articles even using AI-enhanced search tools with neural search and embeddings because the search tool returns irrelevant articles such as ones on how machine translation helps students trying to learn a foreign language.
While your predictions may indeed come true, it will always be intellectually stimulating to become as proficient as possible in a foreign language. There must also be many cognitive benefits.