Assignment 9

This week's reading draws an interesting parallel between machine translation and natural language. At one point, the author argues that there is an important difference between programming languages and natural language, arguing that we can never truly have perfect machine translation. I thought that this was an interesting parallel because language is grounded in rules and programming languages consist of a series of rules. Thus, we ought to be able to reconstruct something that makes up a series of rules using other rules. The author treats machine translation as a black-box, whereas we know exactly how these models are being generated. The program is being fed a corpus that contains a body of text and the "right" translation. Hence, we have complete control over how texts are being translated; the only necessary component to change is the corpus to obtain a better translation. Knowing this, we are able to produce very literal translations. The author uses the concept of a black-box machine translation model to further his argument. However, machine translation is not a hat - one size does not fit all. To create better, more accurate translations, the author ought to explore different machine translation models or train existing models differently to observe the difference.