There is a popular old example that makes the first point well. Consider the sentence:
The police refused the students permission because they feared violence.
Assume that it is to be translated into a language like French in which the word for ‘police’ is feminine. Presumably the pronoun that translates ‘they’ will also have to be feminine. Now replace the word ‘feared’ with ‘advocated’. Now, suddenly, it seems that ‘they’ refers to the students and not to the police and, if the word for students is masculine, it will therefore require a different translation. The knowledge needed to reach these conclusions has nothing linguistic about it. It has to do with everyday facts related students, police, violence, and the kinds of relationships we have seen these things enter into. There are many factors that are involved in the hardness of machine translation, including words with multiple meanings, sentences with multiple grammatical structures, uncertainty about what a pronoun refers to, and other problems of grammar. Still two common misunderstandings make translation seem altogether simpler than it is. First, translation is not basically a linguistic operation, and second, translation is not an operation that preserves meaning
The Structure of Machine Translation Systems
While there have been many variants, most MT systems, and certainly those that have found practical application, have parts that can be named for the chapters in a linguistic textbook. The obtained sentence is produced from this essentially by reversing the analysis process. Some systems that are involved in making use of a so-called ‘interlingua’ or intermediate language, in which case the transfer stage is divided into two steps, one translating a source sentence with the interlingua and the much other translating that results into an abstract representation in the target language.
What Does this Mean for the Translator Community?
Nowadays machine translation usually involves post-editing work done by humans. This post-editing procedure makes sure the content meets the highest quality level desired by the client. For translators, this refers to mastering a new skill that requires a new type of training — post-editing work. This can be much more challenging for translators to adapt to this new job skill, but fortunately, if they do, they have many benefits.
Machine translation may help them deliver work much faster, and therefore, takes on more work. It can also make their jobs much easier. The technology provides translators a starting point, an opportunity to improve the quality, and the skill to churn out more content quickly.
Prices Go Down, but Job Opportunities Go up
When machines will do most of the legwork, automatically the prices of translated content go down. This makes many translators across many such industries nervous. As the prices go down job opportunities will lead their way.
Fortunately, with the requirement of post-editing, the job opportunities are still there. But, there needs to be an efficient and effective way to measure post-editing efforts and then pay translators accordingly. In this way the machine translations will help the content to grow more and more.