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. 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. By clustering pidgin english as one language, the language processing tool would have more data to assess during attention mechanism matching. 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. However, pidgin english exists across a variety of cultural groups which have their own syntax. Google Translate obviously has a robust database of the english language and larger dialects like American English or British English. One example of this may be when translation applications are introduced to different dialects of an already existing language like pidgin english.

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. Ultimately, end to end systems like NLPs have high performance requirements which subsequently require a-lot of processing power for operation. Training broadens the database of semantic, syntactical and contextual information available for the designers of translation tools.

Translation of complex sentences are heavily dependent on machine learning and the training of attention mechanisms.

By complex sentences, I do not mean longer sentences with sophisticated vocabulary, I mean satire, comedy or even sarcasm. This process seems efficient in translating simple sentences of a certain length, but brings into question the effectiveness of translation programs on complex sentences. 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.

English to pidgin google translate