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.

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