At a most basic level, language translation tools like Google Translate are designed to encode content from one language into vectors, decipher the related word in another language through an attention mechanism then decode the vector into the desired language. This process seems efficient in translating simple sentences of a certain length, but brings into question the effectiveness of translation programs on complex sentences. By complex sentences, I do not mean longer sentences with sophisticated vocabulary, I mean satire, comedy or even sarcasm. Translation of complex sentences are heavily dependent on machine learning and the training of attention mechanisms. Training broadens the database of semantic, syntactical and contextual information available for the designers of translation tools. Ultimately, end to end systems like NLPs have high performance requirements which subsequently require a-lot of processing power for operation. It can also be assumed that the high level of power needed for operating these systems impede the flexibility of these systems to process new information and languages.
One example of this may be when translation applications are introduced to different dialects of an already existing language like pidgin english. Google Translate obviously has a robust database of the english language and larger dialects like American English or British English. However, pidgin english exists across a variety of cultural groups which have their own syntax. Since volume is likely a driver for the depth of database knowledge used for translation, does all pidgin get categorized as one “language” and is translated accordingly? In this instance, clustering which is a common machine learning technique would likely be used for efficiency of operating the language processing tool but hinder its accuracy in translation. By clustering pidgin english as one language, the language processing tool would have more data to assess during attention mechanism matching. It is unclear what other machine learning techniques could be used to manage a small languages like dialects of pidgin english and increase the accuracy of pidgin english translation. I also wonder how translation apps would manage languages like Esperanto which has semantic roots from romance languages, but is an artificial language which does not belong to any linguistic family. The existing semantic, syntactical and contextual information exists for the different languages which were used to develop Esperanto but how can the attention mechanism analyze the rules from different languages simultaneously.
Daniel Jurafsky and James H. Martin, Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, 2nd ed. (Upper Saddle River, N.J: Prentice Hall, 2008). Selections.
Thierry Poibeau, Machine Translation (Cambridge, MA: MIT Press, 2017). Selections.
How Google Translate Works: The Machine Learning Algorithm Explained (Code Emporium). Video.